Viral Post: Reddit user claims he was stopped by the TC from entering the 1AC coach despite ticket, internet reacts.

'He Called Security': Reddit User Claims TC Didn't Let Him in 1AC Despite Ticket, Internet Reacts - News18

Binghamton volunteers are coming together to clean up city streets after seeing a post on online forum Reddit.

A Reddit post sparked a community clean-up effort in Binghamton. Here's how you can help

Wall Street ratings continue to roll in for Reddit (NYSE: RDDT), the social media stock that went public just last month · Reddit has been volatile in its first few weeks of trading as the stock searches for equilibrium, but one analyst thinks it has more upside from here.

Reddit Stock Has 22% Upside, According to 1 Wall Street Analyst

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Reddit Stock Has 22% Upside, According to 1 Wall Street Analyst @themotleyfool #stocks $RDDT

It&#x27;s a big year for Reddit. After its IPO, the platform is planning a slew of product features for the year ahead, and -- spoiler alert -- most of them

Reddit CPO talks new features — better translations, moderation and dev tools | TechCrunch

Reddit and Quora results are ranking higher than ever in Google search. Google says it&#x27;s simply giving users what they want.

It's not just you: Reddit is taking over Google

Which QB would Ernest Jones want to fight in the ring? Which player talks the most trash? Who’s the toughest player to tackle? Jones answered all of those questions on Reddit.

Highlights from Ernest Jones' AMA on Reddit, including which QB he'd want to fight in a boxing match

834K Followers, 177 Following, 2,669 Posts - See Instagram photos and videos from Reddit (@reddit)

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Reddit stock has come down quite a ways from its initial pop on its IPO, but the question is whether a rally is in store moving forward.

Now That the IPO Dust Has Settled, Is It the Right Time to Buy Reddit Stock? | InvestorPlace

A full quarter of 2024 is behind us, and one thing is certain: change is on the way. The introduction of generative AI into the tech world late in 2022

Reddit or Astera Labs: J.P. Morgan Selects the Superior AI Stock to Buy - TipRanks.com

Redu

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Expertise Delivered Frontend, Backend & QA Industry Insurance Client Brief Our client, HealthPro Insurance, is a premier Medicare insurance broker, ranked among the top 5 in the United States by Healthcare.gov. Their comprehensive offerings include specialized support for health insurance, Dental, Vision, and long-term life insurance, ensuring their clients receive the best possible care and coverage. HealthPro provides valuable subsidies to employers, individual consumers, and nonprofits. They aim to enhance the accessibility and provision of insurance for all US citizens. Project Scope Our client managed their website and peripheral services on legacy processes and infrastructure. Their unoptimized processes and abrupt resource investments made them pay through their nose. They wanted to introduce automation with DevOps practices to optimize their system’s performance, flexibility, efficiency, and data security. HealthPro’s project scope revolves around the following attributes. Distribute server load and processes across multiple domains.
Switch to private cloud from public to prevent cyber attacks like DDoS, resulting in data breaches.
Creating two different environments for production and staging can mitigate the risks of compromised user experiences when experimenting with or upgrading systems. The Challenge Due to hosting all services in a single public cloud and relying solely on credential protection, our client faced server overloading and security vulnerabilities. This also increased the platform’s instability, posing limitations with hosting the production and staging environments. The above issues compromised the performance of their services, increased costs, and created data risk, hindering their accessibility and scalability. Another prime challenge was switching all their domains and sub-domains to AWS and implementing autoscaling to optimize costs. Our client’s services used MariaDP as a database, and a significant challenge was to plan a systematic switch from MariaDP to MySQL while keeping their data intact. Why Maruti Techlabs? One of our consultants, who was acquainted with our client's challenges and with our expertise, advocated for our services. Subsequently, the consultant and the client's Chief of Sales, who served as the primary point of contact, designed a questionnaire to evaluate our approach. After thoroughly understanding the client's expectations through the questionnaire, our team of experts brainstormed various solutions. We developed a detailed workflow outlining the implementation of these solutions, focusing on enhancing security, improving stability, and optimizing costs. Our proven DevOps expertise in server administration and maintenance, implementing and maintaining CI/CD pipelines, cloud migration, integrating DevSecOps, and more, supported by the consultant's confidence in our capabilities, secured the project for us. The proposed workflow and solutions demonstrated our ability to meet the client's needs effectively, ultimately earning their trust and business. Solution A phase-wise approach was chosen to implement the required solutions to ensure uninterrupted services for the client's customers. Here's how we implemented the perfect tech infrastructure and optimized costs for our client: 1. Isolating Staging and Production Environment Firstly, we isolated the whole(one) unit into two different environments for ‘Production’ and ‘Staging.’ This helped us offer a seamless customer experience in production, allowing our client to experiment with new features, load and regression testing, and introducing new upgrades with security in staging. 2. Transitioning from Public to Private Cloud Our next step was building the staging environment by creating a new Virtual Private Cloud (VPC) with private and public subnets. 3. Creating Data Back-Up We then transferred the database from the old VPC’s public subnet to the new VPC’s private subnet. We did this by taking a snapshot or backing up the data, creating a copy in the new VPC, and restoring it to the private subnet. Additionally, to allow the server to access the database seamlessly, we assigned roles to the EC2 server and upgraded its database security rules. 4. Cost Optimization As a final step, we optimized costs for our client by implementing automatic scaling with AWS’s Aurora Serverless. Aurora Serverless offers the convenience of auto-scaling CPU and memory usage based on traffic. For instance, if our clients don’t have weekly traffic, they do not need to pay for that period. We achieved this by migrating from MariaDB to MySQL, as Aurora supports both database engines. We then transferred the migrated data to Aurora Serverless. Communication and Collaboration We conducted a one-week discovery phase during which our team members communicated extensively to understand their current solution's limitations and expectations concerning their desired solution. We allocated two team members, one DevOps manager and one project manager, to execute this project’s specifics end-to-end. For the entirety of the project, our primary point of contact was our client’s ‘Chief of Sales.’ Here are the platforms we majorly use for communication: Slack - Quick communication,
Google Meet - Daily updates and queries, roll-outs
Email - Official channel to share contracts, weekly updates, and more. Technology Stack Result Implementing our DevOps solutions significantly improved the client's infrastructure and operational efficiency. Reduced on-platform search time from 8 minutes to under 1 minute.
We achieved a 300% boost in application performance over previous versions.
Our solution facilitated a 50% decrease in database and server management costs.
Optimized costs by implementing auto-scaling with AWS Aurora Serverless.
The device system performs automated backups and recovery in case of a crash or security threat.
In case of a sudden server load, the system sends alert signals through email and Slack.
Migration to a private cloud offers end-to-end data encryption. Our Development Process We follow Agile, Lean, & DevOps best practices to create a superior prototype that brings your users’ ideas to fruition through collaboration & rapid execution. Our top priority is quick reaction time & accessibility. We really want to be your extended team, so apart from the regular meetings, you can be sure that each of our team members is one phone call, email, or message away. Expertise Delivered Frontend, Backend & QA Industry Insurance Client Brief Our client, HealthPro Insurance, is a premier Medicare insurance broker, ranked among the top 5 in the United States by Healthcare.gov. Their comprehensive offerings include specialized support for health insurance, Dental, Vision, and long-term life insurance, ensuring their clients receive the best possible care and coverage. HealthPro provides valuable subsidies to employers, individual consumers, and nonprofits. They aim to enhance the accessibility and provision of insurance for all US citizens. Project Scope Our client managed their website and peripheral services on legacy processes and infrastructure. Their unoptimized processes and abrupt resource investments made them pay through their nose. They wanted to introduce automation with DevOps practices to optimize their system’s performance, flexibility, efficiency, and data security. HealthPro’s project scope revolves around the following attributes. Distribute server load and processes across multiple domains. Switch to private cloud from public to prevent cyber attacks like DDoS, resulting in data breaches. Creating two different environments for production and staging can mitigate the risks of compromised user experiences when experimenting with or upgrading systems. Distribute server load and processes across multiple domains. Switch to private cloud from public to prevent cyber attacks like DDoS, resulting in data breaches. Creating two different environments for production and staging can mitigate the risks of compromised user experiences when experimenting with or upgrading systems. The Challenge Due to hosting all services in a single public cloud and relying solely on credential protection, our client faced server overloading and security vulnerabilities. This also increased the platform’s instability, posing limitations with hosting the production and staging environments. The above issues compromised the performance of their services, increased costs, and created data risk, hindering their accessibility and scalability. Another prime challenge was switching all their domains and sub-domains to AWS and implementing autoscaling to optimize costs. Our client’s services used MariaDP as a database, and a significant challenge was to plan a systematic switch from MariaDP to MySQL while keeping their data intact. Why Maruti Techlabs? One of our consultants, who was acquainted with our client's challenges and with our expertise, advocated for our services. Subsequently, the consultant and the client's Chief of Sales, who served as the primary point of contact, designed a questionnaire to evaluate our approach. After thoroughly understanding the client's expectations through the questionnaire, our team of experts brainstormed various solutions. We developed a detailed workflow outlining the implementation of these solutions, focusing on enhancing security, improving stability, and optimizing costs. Our proven DevOps expertise in server administration and maintenance, implementing and maintaining CI/CD pipelines, cloud migration, integrating DevSecOps, and more, supported by the consultant's confidence in our capabilities, secured the project for us. The proposed workflow and solutions demonstrated our ability to meet the client's needs effectively, ultimately earning their trust and business. Solution A phase-wise approach was chosen to implement the required solutions to ensure uninterrupted services for the client's customers. Here's how we implemented the perfect tech infrastructure and optimized costs for our client: 1. Isolating Staging and Production Environment 1. Isolating Staging and Production Environment Firstly, we isolated the whole(one) unit into two different environments for ‘Production’ and ‘Staging.’ This helped us offer a seamless customer experience in production, allowing our client to experiment with new features, load and regression testing, and introducing new upgrades with security in staging. 2. Transitioning from Public to Private Cloud 2. Transitioning from Public to Private Cloud Our next step was building the staging environment by creating a new Virtual Private Cloud (VPC) with private and public subnets. 3. Creating Data Back-Up 3. Creating Data Back-Up We then transferred the database from the old VPC’s public subnet to the new VPC’s private subnet. We did this by taking a snapshot or backing up the data, creating a copy in the new VPC, and restoring it to the private subnet. Additionally, to allow the server to access the database seamlessly, we assigned roles to the EC2 server and upgraded its database security rules. 4. Cost Optimization 4. Cost Optimization As a final step, we optimized costs for our client by implementing automatic scaling with AWS’s Aurora Serverless. Aurora Serverless offers the convenience of auto-scaling CPU and memory usage based on traffic. For instance, if our clients don’t have weekly traffic, they do not need to pay for that period. We achieved this by migrating from MariaDB to MySQL, as Aurora supports both database engines. We then transferred the migrated data to Aurora Serverless. Communication and Collaboration We conducted a one-week discovery phase during which our team members communicated extensively to understand their current solution's limitations and expectations concerning their desired solution. We allocated two team members, one DevOps manager and one project manager, to execute this project’s specifics end-to-end. For the entirety of the project, our primary point of contact was our client’s ‘Chief of Sales.’ Here are the platforms we majorly use for communication: Slack - Quick communication, Google Meet - Daily updates and queries, roll-outs Email - Official channel to share contracts, weekly updates, and more. Slack - Quick communication, Google Meet - Daily updates and queries, roll-outs Email - Official channel to share contracts, weekly updates, and more. Technology Stack Result Implementing our DevOps solutions significantly improved the client's infrastructure and operational efficiency. Reduced on-platform search time from 8 minutes to under 1 minute. We achieved a 300% boost in application performance over previous versions. Our solution facilitated a 50% decrease in database and server management costs. Optimized costs by implementing auto-scaling with AWS Aurora Serverless. The device system performs automated backups and recovery in case of a crash or security threat. In case of a sudden server load, the system sends alert signals through email and Slack. Migration to a private cloud offers end-to-end data encryption. Reduced on-platform search time from 8 minutes to under 1 minute. We achieved a 300% boost in application performance over previous versions. Our solution facilitated a 50% decrease in database and server management costs. Optimized costs by implementing auto-scaling with AWS Aurora Serverless. The device system performs automated backups and recovery in case of a crash or security threat. In case of a sudden server load, the system sends alert signals through email and Slack. Migration to a private cloud offers end-to-end data encryption. Our Development Process We follow Agile, Lean, & DevOps best practices to create a superior prototype that brings your users’ ideas to fruition through collaboration & rapid execution. Our top priority is quick reaction time & accessibility. We really want to be your extended team, so apart from the regular meetings, you can be sure that each of our team members is one phone call, email, or message away.

Reducing Server and Database Costs by 50% for an Insurance Broker using AWS

تخفيض تكاليف الخادم وقاعدة البيانات بنسبة 50% لشركة وساطة تأمين تستخدم AWS

Servidor ukat Base de Datos ukan qullqip 50% ukj jisk’achaña mä Corredor de Seguros ukan AWS apnaqasa

AWS-dən istifadə edən Sığorta Brokeri üçün Server və Verilənlər Bazası Xərclərinin 50% azaldılması

AWS ব্যবহারকারী বীমা ব্রোকারের জন্য সার্ভার এবং ডাটাবেস খরচ ৫০% কমানো

Reducción de costos de servidor y base de datos en un 50% para un corredor de seguros que utiliza AWS

Palvelin- ja tietokantakustannusten alentaminen 50 % AWS:ää käyttävän vakuutusmeklarin osalta

AWS を利用して保険ブローカーのサーバーとデータベースのコストを 50% 削減

Kokitisa ba Coûts ya Serveur na Base de Données na 50% pona Correcteur d'Assurance oyo azali kosalela AWS

Obniżenie kosztów serwera i bazy danych o 50% dla brokera ubezpieczeniowego korzystającego z AWS

Kugabanya Seriveri na Ububikoshingiro Ibiciro 50% kuri Broker wubwishingizi ukoresheje AWS

Minska server- och databaskostnader med 50 % för en försäkringsmäklare som använder AWS

ንሓደ AWS ዝጥቀም ናይ መድሕን ደላላይ ዋጋ ሰርቨርን ዳታቤዝን ብ50% ምንካይ

Authors:
(1) Andrew Draganov, Aarhus University and All authors contributed equally to this research;
(2) David Saulpic, Université Paris Cité & CNRS;
(3) Chris Schwiegelshohn, Aarhus University. Table of Links Abstract and 1 Introduction 2 Preliminaries and Related Work 2.1 On Sampling Strategies 2.2 Other Coreset Strategies 2.3 Coresets for Database Applications 2.4 Quadtree Embeddings 3 Fast-Coresets 4 Reducing the Impact of the Spread 4.1 Computing a crude upper-bound 4.2 From Approximate Solution to Reduced Spread 5 Fast Compression in Practice 5.1 Goal and Scope of the Empirical Analysis 5.2 Experimental Setup 5.3 Evaluating Sampling Strategies 5.4 Streaming Setting and 5.5 Takeaways 6 Conclusion 7 Acknowledgements 8 Proofs, Pseudo-Code, and Extensions and 8.1 Proof of Corollary 3.2 8.2 Reduction of k-means to k-median 8.3 Estimation of the Optimal Cost in a Tree 8.4 Extensions to Algorithm 1 References 4 Reducing the Impact of the Spread We now show how one can replace the linear time-dependency on log ∆ with a logarithmic one (i.e., log ∆ → log(log ∆)). Overview of the Approach Without loss of generality, we assume that the smallest pairwise distance is at least 1, and ∆ is a (known) upper-bound on the diameter of the input. To remove the log ∆ dependency, we proceed by producing a substitute dataset P ′ that has a lower-bound on its minimum distance and an upper-bound on its maximum distance. We then show that, with overwhelming likelihood, reasonable solutions on P ′ have the same cost as solutions on P up to an additive error. In order to produce the substitute dataset P ′ , we first find a crude upper-bound on the cost of the optimal solution to the clustering task on P. We then create a grid such that, for every cluster in the optimal solution, the cluster is entirely contained in a grid cell with overwhelming likelihood – in some sense, points in different grid cell do not interact. We can then freely move these grid cells around without affecting the cost of the solution, as long as they stay far enough away so that points in different cells still do not interact. Thus, we can constrain the diameter of P ′ to be small with respect to the quality of the approximate solution. We show later how to constrain the minimum distance to be comparable to the quality of this approximate solution as well. We will focus this section on the simpler k-median problem but show how to reduce k-means to this case in Section 8.2. 4.1 Computing a crude upper-bound As described, we start by computing an approximate solution U such that OPT ≤ U ≤ poly(n)· OPT. For this, the first step is to embed the input into a quadtree. This embedding has two key properties. First, distances are preserved up to a multiplicative factor O(d log ∆), and therefore the k-median cost is preserved up to this factor as well. Second, the metric is a hierarchically separated tree: it can be represented with a tree, where points of P are the leafs. The distance between two points is then given by the depth of their lowest common ancestor – if it is at depth ℓ, their distance is √ d∆2−ℓ . Our first lemma shows that finding the first level of the tree for which the input lies in k + 1 disjoint subtrees provides us with the desired approximation. We prove this in Section 8.3. A direct consequence is that the first level of the tree for which at least k + 1 cells are non empty allows us to compute an O(n)-approximation for k-median on the tree metric. Since the tree metric approximates the original Euclidean metric up to O(d log ∆), this is therefore an O(nd log ∆)-approximation to k-median in the Euclidean space. To turn this observation into an algorithm, one needs to count the number of non-empty cells at a given level ℓ: for each point, we identify the cell that contains it using modulo operations. Furthermore, we count the number of distinct non-empty cells using a standard dictionary data structure. This is done in time O˜(nd), and pseudo-code is given Algorithm 2. Using a binary search on the O(log ∆) many levels then gives the following result: Given this crude approximate solution, it remains to create a substitute dataset P ′ that satisfies two requirements: 1 First, P ′ must have spread linear in the quality of the approximate solution. If this holds, Algorithm 1 on P ′ will take O˜(nd log log ∆) time. 2 Second, any reasonable solution on P ′ should be roughly equivalent to a corresponding solution on P. This would mean that running Algorithm 1 on P ′ gives us a valid coreset for P. 4.2 From Approximate Solution to Reduced Spread Proposition 4.4. Let P ′ be the dataset produced by Algorithm 3. It holds that: Combining the algorithm of Lemma 4.2, which gives a bound on U, with Lemma 4.5 brings us to the following theorem: To summarize, we have shown that one can, in O˜(nd) time, find a modified dataset P ′ that preserves the cost of corresponding k-means and k-median solutions from P. Importantly, this P ′ has spread that is logarithmic in the spread of P. As a result, one can apply Algorithm 1 onto P ′ in O˜(nd) time without compromising the compression quality with respect to P. Lastly, this compression on P ′ can be re-formatted onto P in O˜(nd) time. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Andrew Draganov, Aarhus University and All authors contributed equally to this research; (2) David Saulpic, Université Paris Cité & CNRS; (3) Chris Schwiegelshohn, Aarhus University. Authors: Authors: (1) Andrew Draganov, Aarhus University and All authors contributed equally to this research; (2) David Saulpic, Université Paris Cité & CNRS; (3) Chris Schwiegelshohn, Aarhus University. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Preliminaries and Related Work 2.1 On Sampling Strategies 2.1 On Sampling Strategies 2.2 Other Coreset Strategies 2.2 Other Coreset Strategies 2.3 Coresets for Database Applications 2.3 Coresets for Database Applications 2.4 Quadtree Embeddings 2.4 Quadtree Embeddings 3 Fast-Coresets 3 Fast-Coresets 4 Reducing the Impact of the Spread 4 Reducing the Impact of the Spread 4.1 Computing a crude upper-bound 4.1 Computing a crude upper-bound 4.2 From Approximate Solution to Reduced Spread 4.2 From Approximate Solution to Reduced Spread 5 Fast Compression in Practice 5 Fast Compression in Practice 5.1 Goal and Scope of the Empirical Analysis 5.1 Goal and Scope of the Empirical Analysis 5.2 Experimental Setup 5.2 Experimental Setup 5.3 Evaluating Sampling Strategies 5.3 Evaluating Sampling Strategies 5.4 Streaming Setting and 5.5 Takeaways 5.4 Streaming Setting and 5.5 Takeaways 6 Conclusion 6 Conclusion 7 Acknowledgements 7 Acknowledgements 8 Proofs, Pseudo-Code, and Extensions and 8.1 Proof of Corollary 3.2 8 Proofs, Pseudo-Code, and Extensions and 8.1 Proof of Corollary 3.2 8.2 Reduction of k-means to k-median 8.2 Reduction of k-means to k-median 8.3 Estimation of the Optimal Cost in a Tree 8.3 Estimation of the Optimal Cost in a Tree 8.4 Extensions to Algorithm 1 8.4 Extensions to Algorithm 1 References References 4 Reducing the Impact of the Spread We now show how one can replace the linear time-dependency on log ∆ with a logarithmic one (i.e., log ∆ → log(log ∆)). Overview of the Approach Without loss of generality, we assume that the smallest pairwise distance is at least 1, and ∆ is a (known) upper-bound on the diameter of the input. To remove the log ∆ dependency, we proceed by producing a substitute dataset P ′ that has a lower-bound on its minimum distance and an upper-bound on its maximum distance. We then show that, with overwhelming likelihood, reasonable solutions on P ′ have the same cost as solutions on P up to an additive error. Overview of the Approach In order to produce the substitute dataset P ′ , we first find a crude upper-bound on the cost of the optimal solution to the clustering task on P. We then create a grid such that, for every cluster in the optimal solution, the cluster is entirely contained in a grid cell with overwhelming likelihood – in some sense, points in different grid cell do not interact. We can then freely move these grid cells around without affecting the cost of the solution, as long as they stay far enough away so that points in different cells still do not interact. Thus, we can constrain the diameter of P ′ to be small with respect to the quality of the approximate solution. We show later how to constrain the minimum distance to be comparable to the quality of this approximate solution as well. We will focus this section on the simpler k-median problem but show how to reduce k-means to this case in Section 8.2. 4.1 Computing a crude upper-bound As described, we start by computing an approximate solution U such that OPT ≤ U ≤ poly(n)· OPT. For this, the first step is to embed the input into a quadtree. This embedding has two key properties. First, distances are preserved up to a multiplicative factor O(d log ∆), and therefore the k-median cost is preserved up to this factor as well. Second, the metric is a hierarchically separated tree: it can be represented with a tree, where points of P are the leafs. The distance between two points is then given by the depth of their lowest common ancestor – if it is at depth ℓ, their distance is √ d∆2−ℓ . Our first lemma shows that finding the first level of the tree for which the input lies in k + 1 disjoint subtrees provides us with the desired approximation. We prove this in Section 8.3. A direct consequence is that the first level of the tree for which at least k + 1 cells are non empty allows us to compute an O(n)-approximation for k-median on the tree metric. Since the tree metric approximates the original Euclidean metric up to O(d log ∆), this is therefore an O(nd log ∆)-approximation to k-median in the Euclidean space. To turn this observation into an algorithm, one needs to count the number of non-empty cells at a given level ℓ: for each point, we identify the cell that contains it using modulo operations. Furthermore, we count the number of distinct non-empty cells using a standard dictionary data structure. This is done in time O˜(nd), and pseudo-code is given Algorithm 2. Using a binary search on the O(log ∆) many levels then gives the following result: Given this crude approximate solution, it remains to create a substitute dataset P ′ that satisfies two requirements: 1 First, P ′ must have spread linear in the quality of the approximate solution. If this holds, Algorithm 1 on P ′ will take O˜(nd log log ∆) time. 2 Second, any reasonable solution on P ′ should be roughly equivalent to a corresponding solution on P. This would mean that running Algorithm 1 on P ′ gives us a valid coreset for P. 4.2 From Approximate Solution to Reduced Spread Proposition 4.4. Let P ′ be the dataset produced by Algorithm 3. It holds that: Proposition 4.4. Let P ′ be the dataset produced by Algorithm 3. It holds that: Combining the algorithm of Lemma 4.2, which gives a bound on U, with Lemma 4.5 brings us to the following theorem: To summarize, we have shown that one can, in O˜(nd) time, find a modified dataset P ′ that preserves the cost of corresponding k-means and k-median solutions from P. Importantly, this P ′ has spread that is logarithmic in the spread of P. As a result, one can apply Algorithm 1 onto P ′ in O˜(nd) time without compromising the compression quality with respect to P. Lastly, this compression on P ′ can be re-formatted onto P in O˜(nd) time. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

Reducing Spread Impact in Clustering Algorithms

How 3D-Printed Metamaterials Could Revolutionize Noise Reduction

Author:
(1) A.J. Santos, B.A. (UTSA), J.D. (STCL), Department of Private International Law, Ankara Yıldırım Beyazıt University, Faculty of Law (ajsantos@protonmail.com). Table of Links Abstract and I. Introduction II. Award Form and Content III. Electronic Signatures, Writing Requirement, and Authentication A. Is it possible for an Arbitrator o Digitally Sign an Arbitral Award
B. How could an Electronic Award be Considered to be "In Writing"
C. Could a Multisig E-Award be Duly Authenticated IV. Can Multisig Arbitration be Seen as an Autonomous Legal Order? V. Conclusion Appendix A: Multisig Transaction on Bitcoin Testnet Example Appendix B: Breakdown of BTC Stored in 2/3 Multisig Accounts Abstract: With blockchain technology, information is recorded in a permanent distributed ledger that is maintained by multiple computers in a peer-to-peer network. There is no central authority that can alter records or change network consensus rules. Such technology could be utilized for voting, title transfers, issuance of company shares, document notarization, but currently, the most popular use-case are virtual currencies. An interesting feature that some virtual currencies have is a multisignature (“multisig”) protocol that requires the electronic signatures from more than one private key to initiate a transfer of funds. Raw data of a multisig transaction may be recognized as an arbitral award under the New York Convention, where the law of England is the lex arbitri and parties have opted-out of a reasoned award. I. INTRODUCTION Multisig is a trust-less method for conducting electronic transactions that could possibly be used for international trade. For instance, a seller and buyer can set-up a multisig account and nominate a third party. If goods are delivered, funds are released from the multisig account with the signatures of the buyer and seller. In case of a dispute, the third party could adjudicate the dispute and sign the release of funds in favor of the prevailing party. However, the legal effect of the act of releasing funds from a multisig scheme has not been fully explored. It is of vital importance for resolved disputes to be final and binding. Businesses operate across many different jurisdictions and having an innovative method to finally resolve disputes would help decrease transactional costs and bolster certainty in an electronic trading environment. For this to occur, multisig needs to be re-imagined not as a simple transaction, but an arbitration process. The resulting multisig electronic award would have res judicata effect, where parties can rely on multisig to further the development of a case or to stop the reopening of a matter which has already been decided in a previous proceeding[2]. The res judicata effect of awards is an integral part of international commercial arbitration, which is evident in Article III of the New York Convention that requires courts in contracting states to recognize awards as binding[3]. This paper will determine whether a multisig transaction, arising out of an arbitration agreement, can be viewed as an electronic arbitral award that is capable of recognition under international arbitration law. In Part I, the legal framework for the form and content requirements of an arbitral award will be discussed. In Part II, we will touch on the law of electronic signatures and consider the authentication requirement of the New York Convention; and finally, in Part III, we will look at the possibility of characterizing multisig arbitration as an autonomous legal order. It is assumed the reader has a basic understanding of blockchain technology and asymmetric cryptography. This paper is available on arxiv under ATTRIBUTION-NONCOMMERCIAL-SHAREALIKE 4.0 INTERNATIONAL license. [2] Schaffstein, Silja (2016) The Doctrine of Res Judicata Before International Commercial Arbitral Tribunals, Oxford, Oxford University Press, pg. 210; Trans-Lex.org ‘No. XIII.4.5 - Conclusive and preclusive effect of awards; res judicata’ https://www.trans-lex.org/970070 l.a.d. 04/18/2019. [3] Id. Author: (1) A.J. Santos, B.A. (UTSA), J.D. (STCL), Department of Private International Law, Ankara Yıldırım Beyazıt University, Faculty of Law (ajsantos@protonmail.com). Author: Author: (1) A.J. Santos, B.A. (UTSA), J.D. (STCL), Department of Private International Law, Ankara Yıldırım Beyazıt University, Faculty of Law (ajsantos@protonmail.com). Table of Links Abstract and I. Introduction Abstract and I. Introduction II. Award Form and Content II. Award Form and Content III. Electronic Signatures, Writing Requirement, and Authentication A. Is it possible for an Arbitrator o Digitally Sign an Arbitral Award B. How could an Electronic Award be Considered to be "In Writing" C. Could a Multisig E-Award be Duly Authenticated A. Is it possible for an Arbitrator o Digitally Sign an Arbitral Award A. Is it possible for an Arbitrator o Digitally Sign an Arbitral Award B. How could an Electronic Award be Considered to be "In Writing" B. How could an Electronic Award be Considered to be "In Writing" C. Could a Multisig E-Award be Duly Authenticated C. Could a Multisig E-Award be Duly Authenticated IV. Can Multisig Arbitration be Seen as an Autonomous Legal Order? IV. Can Multisig Arbitration be Seen as an Autonomous Legal Order? V. Conclusion V. Conclusion Appendix A : Multisig Transaction on Bitcoin Testnet Example Appendix A Appendix A : Multisig Transaction on Bitcoin Testnet Example Appendix B : Breakdown of BTC Stored in 2/3 Multisig Accounts Appendix B Appendix B : Breakdown of BTC Stored in 2/3 Multisig Accounts Abstract : With blockchain technology, information is recorded in a permanent distributed ledger that is maintained by multiple computers in a peer-to-peer network. There is no central authority that can alter records or change network consensus rules. Such technology could be utilized for voting, title transfers, issuance of company shares, document notarization, but currently, the most popular use-case are virtual currencies. An interesting feature that some virtual currencies have is a multisignature (“multisig”) protocol that requires the electronic signatures from more than one private key to initiate a transfer of funds. Raw data of a multisig transaction may be recognized as an arbitral award under the New York Convention, where the law of England is the lex arbitri and parties have opted-out of a reasoned award. Abstract I. INTRODUCTION Multisig is a trust-less method for conducting electronic transactions that could possibly be used for international trade. For instance, a seller and buyer can set-up a multisig account and nominate a third party. If goods are delivered, funds are released from the multisig account with the signatures of the buyer and seller. In case of a dispute, the third party could adjudicate the dispute and sign the release of funds in favor of the prevailing party. However, the legal effect of the act of releasing funds from a multisig scheme has not been fully explored. It is of vital importance for resolved disputes to be final and binding. Businesses operate across many different jurisdictions and having an innovative method to finally resolve disputes would help decrease transactional costs and bolster certainty in an electronic trading environment. For this to occur, multisig needs to be re-imagined not as a simple transaction, but an arbitration process. The resulting multisig electronic award would have res judicata effect, where parties can rely on multisig to further the development of a case or to stop the reopening of a matter which has already been decided in a previous proceeding[2]. The res judicata effect of awards is an integral part of international commercial arbitration, which is evident in Article III of the New York Convention that requires courts in contracting states to recognize awards as binding[3]. This paper will determine whether a multisig transaction, arising out of an arbitration agreement, can be viewed as an electronic arbitral award that is capable of recognition under international arbitration law. In Part I, the legal framework for the form and content requirements of an arbitral award will be discussed. In Part II, we will touch on the law of electronic signatures and consider the authentication requirement of the New York Convention; and finally, in Part III, we will look at the possibility of characterizing multisig arbitration as an autonomous legal order. It is assumed the reader has a basic understanding of blockchain technology and asymmetric cryptography. This paper is available on arxiv under ATTRIBUTION-NONCOMMERCIAL-SHAREALIKE 4.0 INTERNATIONAL license. This paper is available on arxiv under ATTRIBUTION-NONCOMMERCIAL-SHAREALIKE 4.0 INTERNATIONAL license. available on arxiv available on arxiv [2] Schaffstein, Silja (2016) The Doctrine of Res Judicata Before International Commercial Arbitral Tribunals, Oxford, Oxford University Press, pg. 210; Trans-Lex.org ‘No. XIII.4.5 - Conclusive and preclusive effect of awards; res judicata’ https://www.trans-lex.org/970070 l.a.d. 04/18/2019. Trans-Lex.org https://www.trans-lex.org/970070 [3] Id.

Can the Blockchain Make Traditional Courts Redundant?

هل يمكن لتقنية البلوكشين أن تجعل المحاكم التقليدية غير ضرورية؟

Ці можа блокчейн зрабіць традыцыйныя суды лішнімі?

¿Puede la cadena de bloques hacer que los tribunales tradicionales sean redundantes?

آیا بلاک چین می تواند دادگاه های سنتی را بیهوده کند؟

Pode a cadea de bloques facer que os tribunais tradicionais sexan redundantes?

Արդյո՞ք բլոկչեյնը կարող է ավելորդ դարձնել ավանդական դատարանները:

ブロックチェーンは従来の裁判所を不要にできるか？

შეუძლია თუ არა ბლოკჩეინს ტრადიციული სასამართლოები ზედმეტი გახადოს?

Blockchain ekoki kosala ete ba tribunaux traditionnels ezala redundant?

Дали блокчејнот може да ги направи традиционалните судови непотребни?

Может ли блокчейн сделать традиционные суды ненужными?

Xana Blockchain Yi Nga Endla Leswaku Tikhoto Ta Ndhavuko Ti Nga Laveki?

How To Build a SaaS Customer Success Strategy that Reduces Churn and Drives Retention in 2025

Workflow Optimization in Healthcare Insurance: How We Achieved 88% Reduction in Execution Time

በጤና እንክብካቤ ኢንሹራንስ ውስጥ የስራ ፍሰት ማመቻቸት፡ በአፈፃፀም ጊዜ 88% ቅነሳን እንዴት እንዳሳካን።

Оптимизиране на работния процес в здравното осигуряване: Как постигнахме 88% намаление на времето за изпълнение

Optimizacija toka rada u zdravstvenom osiguranju: kako smo postigli 88% smanjenje vremena izvršenja

Optimering af arbejdsgange i sundhedsforsikring: Sådan opnåede vi 88 % reduktion i eksekveringstid

Optimización del flujo de trabajo en seguros médicos: cómo logramos una reducción del 88 % en el tiempo de ejecución

医療保険におけるワークフローの最適化: 実行時間を 88% 削減した方法

Otimização do fluxo de trabalho em seguros de saúde: como alcançamos uma redução de 88% no tempo de execução

Оптимизация рабочего процесса в медицинском страховании: как нам удалось сократить время выполнения на 88%

Optimalizácia pracovného toku v zdravotnom poistení: Ako sme dosiahli 88 % skrátenie času realizácie

Arbetsflödesoptimering i sjukvårdsförsäkringar: Hur vi uppnådde 88 % minskning av exekveringstid

Saglygy goraýyş ätiýaçlandyryşynda iş akymynyň optimizasiýasy: ecutionerine ýetiriş wagtynda 88% azalmagy nädip gazandyk

医疗保险中的工作流程优化：我们如何将执行时间缩短 88%

Table of Links Abstract and Introduction


Preliminaries


Overview


Protocol
4.1 Efficient Option Transfer Protocol
4.2 Holder Collateral-Free Cross-Chain Options


Security Analysis
5.1 Option Transfer Properties
5.2 Option Properties


Implementation


Related Work


Conclusion and Discussion, and References A. Codes A.1 Robust and Efficient Transfer Protocol
A.2 Holder Collateral-Free Cross-Chain Options B. Proofs B.1 Transfer Protocol Proofs
B.2 Option 3 OVERVIEW Consider the scenario described in Section 1: Alice and Bob strike a deal: Bob agrees to sell his 100 guilder coins to Alice in exchange for 100 florin coins within one week. To secure this arrangement, Alice pays a 2-florin premium for the option to complete the transaction. The 100 florin and 100 guilder coins are referred to as 𝐴𝑠𝑠𝑒𝑡𝐴 on 𝐶ℎ𝑎𝑖𝑛𝐴 and 𝐴𝑠𝑠𝑒𝑡𝐵 on 𝐶ℎ𝑎𝑖𝑛𝐵, respectively, with one week as the expiration time 𝑇𝐸. Suppose there are 𝐿 holder position bidders, 𝐶𝑎𝑟𝑜𝑙𝑖 , and 𝑀 writer position bidders,𝐷𝑎𝑣𝑒𝑗 , where 𝑖 ∈ {1, 2, . . . , 𝐿} and 𝑗 ∈ {1, 2, . . . , 𝑀}. They are willing to pay a holder transfer fee, 𝐻𝑜𝑙𝑑𝑒𝑟𝐹𝑒𝑒𝑖 , and a writer transfer fee, 𝑊 𝑟𝑖𝑡𝑒𝑟𝐹𝑒𝑒 𝑗 , to obtain the option position from Alice and Bob, respectively. The holder transfer fee represents the price for the option and Alice’s asset locked in the contract, while the writer transfer fee is the fee to acquire Bob’s risky asset locked in a contract with obligations tied to that asset. An HTLC-based option requires Alice to escrow 𝐴𝑠𝑠𝑒𝑡𝐴 in advance, followed by Bob escrowing 𝐴𝑠𝑠𝑒𝑡𝐵. Making transfer more robust and efficient. In an HTLC-based option, Alice and Bob lock their assets with the hashlock 𝐻(𝐴), where 𝐴 is the exercise secret generated by Alice. Consider the case where Carol purchases Alice’s position. In the previous work [12], the protocol first locks the contracts by tentatively assigning a new hashlock to replace the old one. Considering that Alice can place different hashlocks on two chains and Carol may replace Alice on one chain but not the other, Bob is given a time-consuming consistency phase to ensure the new hashlocks are consistent. We would like to reduce the time needed for this transfer. We manage to reduce the transfer time by a key observation. Since Alice cannot transfer the option to multiple bidders simultaneously, it logically prompts the use of Double-AuthenticationPreventing Signatures (DAPS) to prevent a seller from selling signatures to multiple bidders, thus remove the requirement of guaranteeing consistency by Bob’s efforts. If multiple signatures are revealed, a secret key can be extracted by DAPS, then punishment is enforced automatically to ensure fair payoff. By adopting DAPS, the transaction completion in our protocol is less than half the time of the previous method [12]. Holder Collateral-Free Cross-Chain Options. Our objective is to allow Alice to pay a premium to secure this right, without the need to escrow the assets in advance. We need a mechanism to enable the correct exercise of this right—Alice pays her florin coins to get Bob’s guilder coins. Alice cannot get Bob’s coins without paying florin coins to Bob. A naive approach would be requiring cross-chain transaction confirmation of Alice’s escrow when Alice decides to exercise. However, cross-chain bridges, which are used for cross-chain transaction confirmation, suffer from various security issues [19, 40], such as key leakage [24], smart contract vulnerabilities [28, 37], and rug pulls [18, 38]. We want to design a holder collateral-free option without a trusted cross-chain bridge. If we grant Alice direct access to the exercise right (or the preimage of hash, exercise secret in HTLC), then Bob’s interests is not protected, as Alice could withdraw Bob’s coins directly. To resolve this problem, we resort to economic incentives that commonly present in the DeFi markets, which are also drive forces for options. We let Bob control the exercise secret while Alice retains the right to penalize Bob. In addition to a collateral (100 guilder coins in our example) required by normal option contracts, we ask Bob to lock another valuable asset on 𝐶ℎ𝑎𝑖𝑛𝐴 as a guarantee for Alice when she wants to exercise the right. If Alice later sends her coins on 𝐶ℎ𝑎𝑖𝑛𝐴 in order to exercise her right but Bob does not release the exercise secret, Alice will get Bob’s guarantee as compensation. Suppose this guarantee is sufficient (even more than sufficient) to compensate the expected profit of this option, this method gives Bob incentives to cooperate when Alice wants to exercise her right. Integration. We then integrate the efficient option transfer protocol into the holder collateral-free cross-chain options. Bob controls the exercise secret and the transfer process instead of Alice, then the processes for transferring the holder and writer are reversed. The hash of exercise secret on both chains must remain identical after Bob transfers his position to Dave. A key challenge is ensuring that honest parties incur no losses. This involves addressing potential misbehavior by any party and collusion between any two parties. To counter these, we introduce a withdrawal delay to allow Dave to retrieve assets in case Bob acts maliciously and a transfer confirmation delay to allow Alice to contest any inconsistent replacement of the hashlock. Authors:
(1) Zifan Peng, The Hong Kong University of Science and Technology (Guangzhou) Guangzhou, Guangdong, China (zpengao@connect.hkust-gz.edu.cn);
(2) Yingjie Xue, The Hong Kong University of Science and Technology (Guangzhou) Guangzhou, Guangdong, China (yingjiexue@hkust-gz.edu.cn);
(3) Jingyu Liu, The Hong Kong University of Science and Technology (Guangzhou) Guangzhou, Guangdong, China (jliu514@connect.hkust-gz.edu.cn). This paper is available on arxiv under CC BY 4.0 license. Table of Links Abstract and Introduction Preliminaries Overview Protocol
4.1 Efficient Option Transfer Protocol
4.2 Holder Collateral-Free Cross-Chain Options Security Analysis
5.1 Option Transfer Properties
5.2 Option Properties Implementation Related Work Conclusion and Discussion, and References Abstract and Introduction Abstract and Introduction Abstract and Introduction Preliminaries Preliminaries Preliminaries Overview Overview Overview Protocol 4.1 Efficient Option Transfer Protocol 4.2 Holder Collateral-Free Cross-Chain Options Protocol 4.1 Efficient Option Transfer Protocol 4.1 Efficient Option Transfer Protocol 4.2 Holder Collateral-Free Cross-Chain Options 4.2 Holder Collateral-Free Cross-Chain Options Security Analysis 5.1 Option Transfer Properties 5.2 Option Properties Security Analysis 5.1 Option Transfer Properties 5.1 Option Transfer Properties 5.2 Option Properties 5.2 Option Properties Implementation Implementation Implementation Related Work Related Work Related Work Conclusion and Discussion, and References Conclusion and Discussion, and References Conclusion and Discussion, and References A. Codes A. Codes A.1 Robust and Efficient Transfer Protocol A.2 Holder Collateral-Free Cross-Chain Options A.1 Robust and Efficient Transfer Protocol A.1 Robust and Efficient Transfer Protocol A.2 Holder Collateral-Free Cross-Chain Options A.2 Holder Collateral-Free Cross-Chain Options B. Proofs B. Proofs B.1 Transfer Protocol Proofs B.2 Option B.1 Transfer Protocol Proofs B.1 Transfer Protocol Proofs B.2 Option B.2 Option 3 OVERVIEW Consider the scenario described in Section 1: Alice and Bob strike a deal: Bob agrees to sell his 100 guilder coins to Alice in exchange for 100 florin coins within one week. To secure this arrangement, Alice pays a 2-florin premium for the option to complete the transaction. The 100 florin and 100 guilder coins are referred to as 𝐴𝑠𝑠𝑒𝑡𝐴 on 𝐶ℎ𝑎𝑖𝑛𝐴 and 𝐴𝑠𝑠𝑒𝑡𝐵 on 𝐶ℎ𝑎𝑖𝑛𝐵, respectively, with one week as the expiration time 𝑇𝐸. Suppose there are 𝐿 holder position bidders, 𝐶𝑎𝑟𝑜𝑙𝑖 , and 𝑀 writer position bidders,𝐷𝑎𝑣𝑒𝑗 , where 𝑖 ∈ {1, 2, . . . , 𝐿} and 𝑗 ∈ {1, 2, . . . , 𝑀}. They are willing to pay a holder transfer fee, 𝐻𝑜𝑙𝑑𝑒𝑟𝐹𝑒𝑒𝑖 , and a writer transfer fee, 𝑊 𝑟𝑖𝑡𝑒𝑟𝐹𝑒𝑒 𝑗 , to obtain the option position from Alice and Bob, respectively. The holder transfer fee represents the price for the option and Alice’s asset locked in the contract, while the writer transfer fee is the fee to acquire Bob’s risky asset locked in a contract with obligations tied to that asset. An HTLC-based option requires Alice to escrow 𝐴𝑠𝑠𝑒𝑡𝐴 in advance, followed by Bob escrowing 𝐴𝑠𝑠𝑒𝑡𝐵. Making transfer more robust and efficient. In an HTLC-based option, Alice and Bob lock their assets with the hashlock 𝐻(𝐴), where 𝐴 is the exercise secret generated by Alice. Consider the case where Carol purchases Alice’s position. In the previous work [12], the protocol first locks the contracts by tentatively assigning a new hashlock to replace the old one. Considering that Alice can place different hashlocks on two chains and Carol may replace Alice on one chain but not the other, Bob is given a time-consuming consistency phase to ensure the new hashlocks are consistent. We would like to reduce the time needed for this transfer. Making transfer more robust and efficient. We manage to reduce the transfer time by a key observation. Since Alice cannot transfer the option to multiple bidders simultaneously, it logically prompts the use of Double-AuthenticationPreventing Signatures (DAPS) to prevent a seller from selling signatures to multiple bidders, thus remove the requirement of guaranteeing consistency by Bob’s efforts. If multiple signatures are revealed, a secret key can be extracted by DAPS, then punishment is enforced automatically to ensure fair payoff. By adopting DAPS, the transaction completion in our protocol is less than half the time of the previous method [12]. Holder Collateral-Free Cross-Chain Options . Our objective is to allow Alice to pay a premium to secure this right, without the need to escrow the assets in advance. We need a mechanism to enable the correct exercise of this right—Alice pays her florin coins to get Bob’s guilder coins. Alice cannot get Bob’s coins without paying florin coins to Bob. A naive approach would be requiring cross-chain transaction confirmation of Alice’s escrow when Alice decides to exercise. However, cross-chain bridges, which are used for cross-chain transaction confirmation, suffer from various security issues [19, 40], such as key leakage [24], smart contract vulnerabilities [28, 37], and rug pulls [18, 38]. We want to design a holder collateral-free option without a trusted cross-chain bridge. Holder Collateral-Free Cross-Chain Options If we grant Alice direct access to the exercise right (or the preimage of hash, exercise secret in HTLC), then Bob’s interests is not protected, as Alice could withdraw Bob’s coins directly. To resolve this problem, we resort to economic incentives that commonly present in the DeFi markets, which are also drive forces for options. We let Bob control the exercise secret while Alice retains the right to penalize Bob. In addition to a collateral (100 guilder coins in our example) required by normal option contracts, we ask Bob to lock another valuable asset on 𝐶ℎ𝑎𝑖𝑛𝐴 as a guarantee for Alice when she wants to exercise the right. If Alice later sends her coins on 𝐶ℎ𝑎𝑖𝑛𝐴 in order to exercise her right but Bob does not release the exercise secret, Alice will get Bob’s guarantee as compensation. Suppose this guarantee is sufficient (even more than sufficient) to compensate the expected profit of this option, this method gives Bob incentives to cooperate when Alice wants to exercise her right. Integration. We then integrate the efficient option transfer protocol into the holder collateral-free cross-chain options. Bob controls the exercise secret and the transfer process instead of Alice, then the processes for transferring the holder and writer are reversed. The hash of exercise secret on both chains must remain identical after Bob transfers his position to Dave. A key challenge is ensuring that honest parties incur no losses. This involves addressing potential misbehavior by any party and collusion between any two parties. To counter these, we introduce a withdrawal delay to allow Dave to retrieve assets in case Bob acts maliciously and a transfer confirmation delay to allow Alice to contest any inconsistent replacement of the hashlock. Authors: (1) Zifan Peng, The Hong Kong University of Science and Technology (Guangzhou) Guangzhou, Guangdong, China (zpengao@connect.hkust-gz.edu.cn); (2) Yingjie Xue, The Hong Kong University of Science and Technology (Guangzhou) Guangzhou, Guangdong, China (yingjiexue@hkust-gz.edu.cn); (3) Jingyu Liu, The Hong Kong University of Science and Technology (Guangzhou) Guangzhou, Guangdong, China (jliu514@connect.hkust-gz.edu.cn). Authors: Authors: (1) Zifan Peng, The Hong Kong University of Science and Technology (Guangzhou) Guangzhou, Guangdong, China (zpengao@connect.hkust-gz.edu.cn); (2) Yingjie Xue, The Hong Kong University of Science and Technology (Guangzhou) Guangzhou, Guangdong, China (yingjiexue@hkust-gz.edu.cn); (3) Jingyu Liu, The Hong Kong University of Science and Technology (Guangzhou) Guangzhou, Guangdong, China (jliu514@connect.hkust-gz.edu.cn). This paper is available on arxiv under CC BY 4.0 license. This paper is available on arxiv under CC BY 4.0 license. available on arxiv

Study Uncovers Efficient Cross-Chain Option Protocol With Reduced Latency

Speculative decoding is an advanced AI inference technique that is gaining traction in natural language processing (NLP) and other sequence generation tasks. It addresses one of the most significant challenges in deploying large-scale models: balancing computational efficiency with the quality of generated outputs. Models like GPT-3 and GPT-4 have set new benchmarks in AI capability but are computationally expensive, especially for real-time applications such as chatbots, translation systems, and interactive assistants. Speculative decoding offers a clever solution by introducing a two-model system: a smaller, faster draft model and a larger, more accurate target model. This approach reduces latency without compromising the quality of the generated sequences. At its core, speculative decoding is a two-step process. First, a lightweight draft model generates a sequence of tokens quickly. This model is typically a distilled version of the target model or another smaller model trained for speed. The draft model's output is not final but serves as a proposal. The second step involves the larger, more accurate target model, which evaluates the draft model’s tokens. The target model either accepts the proposed tokens if they meet a confidence threshold or refines them if they do not. This process allows the system to leverage the efficiency of the draft model while maintaining the precision of the target model. The reason speculative decoding is particularly appealing lies in its ability to reduce the computational burden of the larger model. By delegating the initial token generation to the draft model, the target model operates selectively, focusing only on validation or correction rather than generating every token from scratch. This division of labor significantly reduces inference latency and resource consumption, making it a preferred technique for real-time applications. The technique works well in scenarios where speed is critical, and the quality of generated outputs cannot be compromised. For instance, in chat-based applications, users expect near-instantaneous responses. Speculative decoding ensures that the system generates text quickly while retaining the nuanced understanding of the larger model. Similarly, in real-time translation systems, where delays can disrupt communication, speculative decoding provides a practical way to meet latency constraints. To understand speculative decoding more concretely, let us consider a code implementation. Below is an example that demonstrates how speculative decoding could be implemented using PyTorch and Hugging Face's Transformers library. This implementation involves a draft model (a smaller, faster version like DistilGPT-2) and a target model (a larger, more accurate model like GPT-2). from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the draft (smaller) and target (larger) models

draft_model_name = "distilgpt2"  # Smaller, faster model 

target_model_name = "gpt2"      # Larger, more accurate model

draft_tokenizer = AutoTokenizer.from_pretrained(draft_model_name) draft_model = AutoModelForCausalLM.from_pretrained(draft_model_name)

target_tokenizer = AutoTokenizer.from_pretrained(target_model_name) target_model = AutoModelForCausalLM.from_pretrained(target_model_name)

def speculative_decode(prompt, max_length=50, threshold=0.9): """ Performs speculative decoding using a draft model and a target model.

Args:
    prompt (str): The input text to the model.
    max_length (int): The maximum length of the generated sequence.
    threshold (float): The confidence threshold for the target model to accept tokens.

Returns:
    str: The generated text sequence.
"""
# Step 1: Generate tokens using the draft model
draft_inputs = draft_tokenizer(prompt, return_tensors="pt")  # Tokenize the prompt
draft_outputs = draft_model.generate(draft_inputs.input_ids, max_length=max_length)  # Generate draft tokens
draft_tokens = draft_tokenizer.decode(draft_outputs[0], skip_special_tokens=True)  # Decode draft tokens to text

# Step 2: Verify tokens using the target model
target_inputs = target_tokenizer(draft_tokens, return_tensors="pt")  # Tokenize the draft output for the target model
target_logits = target_model(**target_inputs).logits  # Get logits (un-normalized probabilities) from the target model

# Step 3: Calculate confidence scores for each token
probs = target_logits.softmax(dim=-1)  # Convert logits to probabilities
confidence = probs.max(dim=-1).values.mean().item()  # Calculate the average confidence score across all tokens

# Step 4: Decide whether to accept or refine tokens
if confidence >= threshold:  # If confidence exceeds the threshold
    return draft_tokens  # Accept the draft tokens
else:  # Otherwise, refine the sequence using the target model
    refined_outputs = target_model.generate(target_inputs.input_ids, max_length=max_length)
    return target_tokenizer.decode(refined_outputs[0], skip_special_tokens=True)  # Return the refined sequence


prompt = "The future of AI is" output = speculative_decode(prompt, max_length=20) print(output) In this implementation, the speculative decoding process begins with the draft model generating a sequence of tokens. These tokens are then passed to the target model, which calculates the confidence for each token. If the average confidence exceeds a pre-defined threshold, the tokens are accepted as is. Otherwise, the target model refines the sequence to ensure output quality. This code includes detailed comments explaining each step, from tokenizing the input for the draft model to generating and validating tokens with the target model. The threshold parameter can be adjusted based on the application’s tolerance for speed versus accuracy trade-offs. One of the primary challenges in implementing speculative decoding is training the draft model. The draft model must align closely with the target model in terms of predictions, as significant discrepancies can reduce the efficiency of the process. For example, if the draft model frequently proposes incorrect tokens, the target model spends more time correcting them, negating the benefits of speculative decoding. Ensuring this alignment typically involves fine-tuning or distillation techniques that transfer knowledge from the target model to the draft model. Another critical aspect of speculative decoding is the confidence threshold used by the target model to accept or reject the draft model's tokens. If the threshold is too strict, the target model rejects too many tokens, increasing its workload and reducing efficiency. Conversely, if the threshold is too lenient, the system may accept suboptimal tokens, degrading output quality. Fine-tuning this parameter requires careful experimentation and may vary depending on the application. Speculative decoding can also integrate with parallel computation to further enhance efficiency. For example, while the target model processes one batch of tokens, the draft model can begin generating the next batch. This overlap reduces idle time for both models and maximizes throughput, making speculative decoding scalable for high-volume systems. While challenges like draft-target alignment and confidence threshold tuning exist, ongoing advancements in model training and inference techniques promise to make speculative decoding even more robust and accessible. This technique is poised to play a pivotal role in the future of AI deployment, particularly as demand for high-speed, high-quality systems continues to grow. The code above is publicly available in my github https://github.com/philhopkinsML Speculative decoding is an advanced AI inference technique that is gaining traction in natural language processing (NLP) and other sequence generation tasks. It addresses one of the most significant challenges in deploying large-scale models: balancing computational efficiency with the quality of generated outputs. Models like GPT-3 and GPT-4 have set new benchmarks in AI capability but are computationally expensive, especially for real-time applications such as chatbots, translation systems, and interactive assistants. Speculative decoding offers a clever solution by introducing a two-model system: a smaller, faster draft model and a larger, more accurate target model. This approach reduces latency without compromising the quality of the generated sequences. At its core, speculative decoding is a two-step process. First, a lightweight draft model generates a sequence of tokens quickly. This model is typically a distilled version of the target model or another smaller model trained for speed. The draft model's output is not final but serves as a proposal. The second step involves the larger, more accurate target model, which evaluates the draft model’s tokens. The target model either accepts the proposed tokens if they meet a confidence threshold or refines them if they do not. This process allows the system to leverage the efficiency of the draft model while maintaining the precision of the target model. The reason speculative decoding is particularly appealing lies in its ability to reduce the computational burden of the larger model. By delegating the initial token generation to the draft model, the target model operates selectively, focusing only on validation or correction rather than generating every token from scratch. This division of labor significantly reduces inference latency and resource consumption, making it a preferred technique for real-time applications. The technique works well in scenarios where speed is critical, and the quality of generated outputs cannot be compromised. For instance, in chat-based applications, users expect near-instantaneous responses. Speculative decoding ensures that the system generates text quickly while retaining the nuanced understanding of the larger model. Similarly, in real-time translation systems, where delays can disrupt communication, speculative decoding provides a practical way to meet latency constraints. To understand speculative decoding more concretely, let us consider a code implementation. Below is an example that demonstrates how speculative decoding could be implemented using PyTorch and Hugging Face's Transformers library. This implementation involves a draft model (a smaller, faster version like DistilGPT-2) and a target model (a larger, more accurate model like GPT-2). from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the draft (smaller) and target (larger) models

draft_model_name = "distilgpt2"  # Smaller, faster model 

target_model_name = "gpt2"      # Larger, more accurate model

draft_tokenizer = AutoTokenizer.from_pretrained(draft_model_name) draft_model = AutoModelForCausalLM.from_pretrained(draft_model_name)

target_tokenizer = AutoTokenizer.from_pretrained(target_model_name) target_model = AutoModelForCausalLM.from_pretrained(target_model_name)

def speculative_decode(prompt, max_length=50, threshold=0.9): """ Performs speculative decoding using a draft model and a target model.

Args:
    prompt (str): The input text to the model.
    max_length (int): The maximum length of the generated sequence.
    threshold (float): The confidence threshold for the target model to accept tokens.

Returns:
    str: The generated text sequence.
"""
# Step 1: Generate tokens using the draft model
draft_inputs = draft_tokenizer(prompt, return_tensors="pt")  # Tokenize the prompt
draft_outputs = draft_model.generate(draft_inputs.input_ids, max_length=max_length)  # Generate draft tokens
draft_tokens = draft_tokenizer.decode(draft_outputs[0], skip_special_tokens=True)  # Decode draft tokens to text

# Step 2: Verify tokens using the target model
target_inputs = target_tokenizer(draft_tokens, return_tensors="pt")  # Tokenize the draft output for the target model
target_logits = target_model(**target_inputs).logits  # Get logits (un-normalized probabilities) from the target model

# Step 3: Calculate confidence scores for each token
probs = target_logits.softmax(dim=-1)  # Convert logits to probabilities
confidence = probs.max(dim=-1).values.mean().item()  # Calculate the average confidence score across all tokens

# Step 4: Decide whether to accept or refine tokens
if confidence >= threshold:  # If confidence exceeds the threshold
    return draft_tokens  # Accept the draft tokens
else:  # Otherwise, refine the sequence using the target model
    refined_outputs = target_model.generate(target_inputs.input_ids, max_length=max_length)
    return target_tokenizer.decode(refined_outputs[0], skip_special_tokens=True)  # Return the refined sequence


prompt = "The future of AI is" output = speculative_decode(prompt, max_length=20) print(output) from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the draft (smaller) and target (larger) models

draft_model_name = "distilgpt2"  # Smaller, faster model 

target_model_name = "gpt2"      # Larger, more accurate model

draft_tokenizer = AutoTokenizer.from_pretrained(draft_model_name) draft_model = AutoModelForCausalLM.from_pretrained(draft_model_name)

target_tokenizer = AutoTokenizer.from_pretrained(target_model_name) target_model = AutoModelForCausalLM.from_pretrained(target_model_name)

def speculative_decode(prompt, max_length=50, threshold=0.9): """ Performs speculative decoding using a draft model and a target model.

Args:
    prompt (str): The input text to the model.
    max_length (int): The maximum length of the generated sequence.
    threshold (float): The confidence threshold for the target model to accept tokens.

Returns:
    str: The generated text sequence.
"""
# Step 1: Generate tokens using the draft model
draft_inputs = draft_tokenizer(prompt, return_tensors="pt")  # Tokenize the prompt
draft_outputs = draft_model.generate(draft_inputs.input_ids, max_length=max_length)  # Generate draft tokens
draft_tokens = draft_tokenizer.decode(draft_outputs[0], skip_special_tokens=True)  # Decode draft tokens to text

# Step 2: Verify tokens using the target model
target_inputs = target_tokenizer(draft_tokens, return_tensors="pt")  # Tokenize the draft output for the target model
target_logits = target_model(**target_inputs).logits  # Get logits (un-normalized probabilities) from the target model

# Step 3: Calculate confidence scores for each token
probs = target_logits.softmax(dim=-1)  # Convert logits to probabilities
confidence = probs.max(dim=-1).values.mean().item()  # Calculate the average confidence score across all tokens

# Step 4: Decide whether to accept or refine tokens
if confidence >= threshold:  # If confidence exceeds the threshold
    return draft_tokens  # Accept the draft tokens
else:  # Otherwise, refine the sequence using the target model
    refined_outputs = target_model.generate(target_inputs.input_ids, max_length=max_length)
    return target_tokenizer.decode(refined_outputs[0], skip_special_tokens=True)  # Return the refined sequence


prompt = "The future of AI is" output = speculative_decode(prompt, max_length=20) print(output) In this implementation, the speculative decoding process begins with the draft model generating a sequence of tokens. These tokens are then passed to the target model, which calculates the confidence for each token. If the average confidence exceeds a pre-defined threshold, the tokens are accepted as is. Otherwise, the target model refines the sequence to ensure output quality. This code includes detailed comments explaining each step, from tokenizing the input for the draft model to generating and validating tokens with the target model. The threshold parameter can be adjusted based on the application’s tolerance for speed versus accuracy trade-offs. One of the primary challenges in implementing speculative decoding is training the draft model. The draft model must align closely with the target model in terms of predictions, as significant discrepancies can reduce the efficiency of the process. For example, if the draft model frequently proposes incorrect tokens, the target model spends more time correcting them, negating the benefits of speculative decoding. Ensuring this alignment typically involves fine-tuning or distillation techniques that transfer knowledge from the target model to the draft model. Another critical aspect of speculative decoding is the confidence threshold used by the target model to accept or reject the draft model's tokens. If the threshold is too strict, the target model rejects too many tokens, increasing its workload and reducing efficiency. Conversely, if the threshold is too lenient, the system may accept suboptimal tokens, degrading output quality. Fine-tuning this parameter requires careful experimentation and may vary depending on the application. Speculative decoding can also integrate with parallel computation to further enhance efficiency. For example, while the target model processes one batch of tokens, the draft model can begin generating the next batch. This overlap reduces idle time for both models and maximizes throughput, making speculative decoding scalable for high-volume systems. While challenges like draft-target alignment and confidence threshold tuning exist, ongoing advancements in model training and inference techniques promise to make speculative decoding even more robust and accessible. This technique is poised to play a pivotal role in the future of AI deployment, particularly as demand for high-speed, high-quality systems continues to grow. The code above is publicly available in my github https://github.com/philhopkinsML https://github.com/philhopkinsML

Meet The AI Tag-Team Method That Reduces Latency in Your Model's Response

Authors:
(1) Limeng Zhang, Centre for Research on Engineering Software Technologies (CREST), The University of Adelaide, Australia;
(2) M. Ali Babar, Centre for Research on Engineering Software Technologies (CREST), The University of Adelaide, Australia. Table of Links Abstract and 1 Introduction 1.1 Configuration Parameter Tuning Challenges and 1.2 Contributions 2 Tuning Objectives 3 Overview of Tuning Framework 4 Workload Characterization and 4.1 Query-level Characterization 4.2 Runtime-based Characterization 5 Feature Pruning and 5.1 Workload-level Pruning 5.2 Configuration-level Pruning 5.3 Summary 6 Knowledge from Experience 7 Configuration Recommendation and 7.1 Bayesian Optimization 7.2 Neural Network 7.3 Reinforcement Learning 7.4 Search-based Solutions 8 Experimental Setting 9 Related Work 10 Discussion and Conclusion, and References 5.3 Summary Given the complex configuration space and the diversity of workloads, employing pruning techniques to reduce workload running time and configuration search space emerges as a natural approach in addressing these complexities. In this section, we aim to provide direction for future practitioners and researchers on improving data collection efficiency and training efficiency through various pruning strategies. Specifically, we classify the pruning techniques into two levels: workload-level and configuration space. Regarding the workload level, we categorize it into two directions: eliminating redundant queries and workload feature reduction techniques. For the configuration level, we present the existing feature reduction methods applied in the state-of-the-art tuning methods, mainly focusing on feature projection, importance ranking, or feature clustering. Moreover, future researchers and practitioners can also try to explore different dimensionality reduction techniques tailored to specific data characteristics, as outlined in the survey by Hou et al. [31]. Furthermore, advancements in high-dimensional data technology offer opportunities to enhance the performance of feature pruning methods. For instance, Yang et al. [32] proposed an innovative variant of LASSO, named Efficient Tuning of Lasso (ET-Lasso), which focuses on ensuring feature selection consistency. This method has demonstrated effectiveness in efficiently selecting active features contributing to the response, achieved by integrating permuted features as pseudo-features within linear models. This paper is available on arxiv under CC BY 4.0 DEED. Authors: (1) Limeng Zhang, Centre for Research on Engineering Software Technologies (CREST), The University of Adelaide, Australia; (2) M. Ali Babar, Centre for Research on Engineering Software Technologies (CREST), The University of Adelaide, Australia. Authors: Authors: (1) Limeng Zhang, Centre for Research on Engineering Software Technologies (CREST), The University of Adelaide, Australia; (2) M. Ali Babar, Centre for Research on Engineering Software Technologies (CREST), The University of Adelaide, Australia. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 1.1 Configuration Parameter Tuning Challenges and 1.2 Contributions 1.1 Configuration Parameter Tuning Challenges and 1.2 Contributions 2 Tuning Objectives 2 Tuning Objectives 3 Overview of Tuning Framework 3 Overview of Tuning Framework 4 Workload Characterization and 4.1 Query-level Characterization 4 Workload Characterization and 4.1 Query-level Characterization 4.2 Runtime-based Characterization 4.2 Runtime-based Characterization 5 Feature Pruning and 5.1 Workload-level Pruning 5 Feature Pruning and 5.1 Workload-level Pruning 5.2 Configuration-level Pruning 5.2 Configuration-level Pruning 5.3 Summary 5.3 Summary 6 Knowledge from Experience 6 Knowledge from Experience 7 Configuration Recommendation and 7.1 Bayesian Optimization 7 Configuration Recommendation and 7.1 Bayesian Optimization 7.2 Neural Network 7.2 Neural Network 7.3 Reinforcement Learning 7.3 Reinforcement Learning 7.4 Search-based Solutions 7.4 Search-based Solutions 8 Experimental Setting 8 Experimental Setting 9 Related Work 9 Related Work 10 Discussion and Conclusion, and References 10 Discussion and Conclusion, and References 5.3 Summary Given the complex configuration space and the diversity of workloads, employing pruning techniques to reduce workload running time and configuration search space emerges as a natural approach in addressing these complexities. In this section, we aim to provide direction for future practitioners and researchers on improving data collection efficiency and training efficiency through various pruning strategies. Specifically, we classify the pruning techniques into two levels: workload-level and configuration space. Regarding the workload level, we categorize it into two directions: eliminating redundant queries and workload feature reduction techniques. For the configuration level, we present the existing feature reduction methods applied in the state-of-the-art tuning methods, mainly focusing on feature projection, importance ranking, or feature clustering. Moreover, future researchers and practitioners can also try to explore different dimensionality reduction techniques tailored to specific data characteristics, as outlined in the survey by Hou et al. [31]. Furthermore, advancements in high-dimensional data technology offer opportunities to enhance the performance of feature pruning methods. For instance, Yang et al. [32] proposed an innovative variant of LASSO, named Efficient Tuning of Lasso (ET-Lasso), which focuses on ensuring feature selection consistency. This method has demonstrated effectiveness in efficiently selecting active features contributing to the response, achieved by integrating permuted features as pseudo-features within linear models. This paper is available on arxiv under CC BY 4.0 DEED. This paper is available on arxiv under CC BY 4.0 DEED. available on arxiv

Pruning Techniques for Reducing Workload and Configuration Space Complexity

Authors:
(1) Limeng Zhang, Centre for Research on Engineering Software Technologies (CREST), The University of Adelaide, Australia;
(2) M. Ali Babar, Centre for Research on Engineering Software Technologies (CREST), The University of Adelaide, Australia. Table of Links Abstract and 1 Introduction 1.1 Configuration Parameter Tuning Challenges and 1.2 Contributions 2 Tuning Objectives 3 Overview of Tuning Framework 4 Workload Characterization and 4.1 Query-level Characterization 4.2 Runtime-based Characterization 5 Feature Pruning and 5.1 Workload-level Pruning 5.2 Configuration-level Pruning 5.3 Summary 6 Knowledge from Experience 7 Configuration Recommendation and 7.1 Bayesian Optimization 7.2 Neural Network 7.3 Reinforcement Learning 7.4 Search-based Solutions 8 Experimental Setting 9 Related Work 10 Discussion and Conclusion, and References 5.2 Configuration-level Pruning Another approach to mitigating the optimization time required by machine learning-based methodologies is to reduce configuration space necessitating tuning. Configuration-level pruning endeavors to discern and prioritize the tuning of parameters that exert substantial influence on the DBMSs based on historical observations. • Spearman Rank Correlation Coefficient (SRCC) [26]: is a nonparametric measure of rank correlation on the strength of a monotonic relationship between paired data. It is defined as the Pearson correlation coefficient between the rank variables. By leveraging its capacity to gauge monotonic relationships, SRCC facilitates the identification and removal of redundant parameters, contributing to a more efficient and simplified configuration. LOCAT [7] calculates SRCC between each configuration parameter and the workload execution time to filter out some redundant configuration parameters and narrow down the parameter search space.” • Principal Component Analysis (PCA): is a dimensionality reduction method that transforms correlated variables into uncorrelated ones, known as principal components. It does so by calculating the covariance matrix, performing eigendecomposition, and selecting a subset of principal components based on their variance. After SRCC, LOCAT [7] applies Gaussian kernel Principal Component Analysis (KPCA) to further extract and form new lower-dimensional parameters derived after removal of redundant configuration parameters for subsequent optimal parameter searching. • Least Absolute Shrinkage and Selection Operator (LASSO) and Lasso Path Algorithm. In the context of linear regression, LASSO modifies this objective function by adding a L1 penalty term equals to λ times the sum of absolute values of the regression coefficients, where λ controls the strength of the penalty, and it helps to reduce the effect of irrelevant variables by penalizing models with large weights. A higher λ value results in more coefficients being pushed toward zero, thereby achieving interpretable, stable, and computationally efficient feature selection. OtterTune [8] uses the LASSO path algorithm to assess the importance of configuration parameters by gradually reducing the penalty term. The order in 6 which parameters are included during this process is then considered as the importance order, and OtterTune uses this information to prune less important parameters. • Random Forest (RF): is a supervised machine learning method to deal with classification and regression problems. It can be deemed a voting algorithm consisting of multiple decision trees, used to calculate the importance of features. In the study [27], the authors utilize Latin Hypercube Sampling (LHS) to select a subset of configurations. Subsequently, they employ Random Forests with 300 Classification and Regression Trees (CART) to ascertain the importance of each parameter. Their findings reveal that when employing the YCSB workload-A on Cassandra, optimizing just five parameters can yield performance levels comparable to 99% of the best configurations attained through extensive parameter tuning. HUNTER [13] adopt the RF classifier with CARTs and adopts the majority voting principle to determine the importance of the knobs. It then selects the top-20 knobs to reduce the configuration search space in its tuning framework. • Hashing-enhanced Subspace BO (HeSBO) [28]: is a randomized low-dimensional projection approach that operates by deducing the search space for Bayesian Optimization-based solutions (discussed in Section 7.1). By utilizing two uniform hash functions to construct the rows and signs of the random projection matrix, it performs a count-sketch projection to transform the original high-dimensional configuration space into lower-dimensional subspace. This process is intuitively designed to effectively preserve the characteristics, such as pairwise point distances, of the original high-dimensional space for the reduced (important) dimensions. LlamaTune [16] adopts it to perform a projection of the configuration space from all documentations to a lowerdimensional subspace and then use the Bayesian Optimization-based optimizer to tune this smaller subspace. • Alternative approaches: Due to the challenges of tuning in high-dimensional configuration spaces, the identification of parameters that significantly affect DBMS performance has gained considerable attention. Among them, Researchers in [29] propose Caver to select a subset of storage parameters. Given a set of initial parameters and configurations, Carver first employs LHS to select a small number of configurations for evaluation. Subsequently, it greedily selects the most important parameters based on their parameter importance values. The first parameter is determined by the highest importance value, after which Carver fixes its value and computes the parameter importance values for the remaining parameters. During this process, importance is calculated as the difference between the variance of the original set of configurations and the sum of variances within the subset when parameters take different values. The algorithm selects parameters with the maximum difference. Recently, OpAdviser [30] proposed constructing an effective configuration region that contains promising configurations. It leverages performance similarity among historical workloads to guide the construction process. Specifically, it extracts a relatively compact region based on similar workloads and a larger region based on less similar ones. Furthermore, OpAdviser constructs the target search space by employing a majority weighted voting strategy to aggregate suggestions from candidate tasks. This paper is available on arxiv under CC BY 4.0 DEED. Authors: (1) Limeng Zhang, Centre for Research on Engineering Software Technologies (CREST), The University of Adelaide, Australia; (2) M. Ali Babar, Centre for Research on Engineering Software Technologies (CREST), The University of Adelaide, Australia. Authors: Authors: (1) Limeng Zhang, Centre for Research on Engineering Software Technologies (CREST), The University of Adelaide, Australia; (2) M. Ali Babar, Centre for Research on Engineering Software Technologies (CREST), The University of Adelaide, Australia. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 1.1 Configuration Parameter Tuning Challenges and 1.2 Contributions 1.1 Configuration Parameter Tuning Challenges and 1.2 Contributions 2 Tuning Objectives 2 Tuning Objectives 3 Overview of Tuning Framework 3 Overview of Tuning Framework 4 Workload Characterization and 4.1 Query-level Characterization 4 Workload Characterization and 4.1 Query-level Characterization 4.2 Runtime-based Characterization 4.2 Runtime-based Characterization 5 Feature Pruning and 5.1 Workload-level Pruning 5 Feature Pruning and 5.1 Workload-level Pruning 5.2 Configuration-level Pruning 5.2 Configuration-level Pruning 5.3 Summary 5.3 Summary 6 Knowledge from Experience 6 Knowledge from Experience 7 Configuration Recommendation and 7.1 Bayesian Optimization 7 Configuration Recommendation and 7.1 Bayesian Optimization 7.2 Neural Network 7.2 Neural Network 7.3 Reinforcement Learning 7.3 Reinforcement Learning 7.4 Search-based Solutions 7.4 Search-based Solutions 8 Experimental Setting 8 Experimental Setting 9 Related Work 9 Related Work 10 Discussion and Conclusion, and References 10 Discussion and Conclusion, and References 5.2 Configuration-level Pruning Another approach to mitigating the optimization time required by machine learning-based methodologies is to reduce configuration space necessitating tuning. Configuration-level pruning endeavors to discern and prioritize the tuning of parameters that exert substantial influence on the DBMSs based on historical observations. • Spearman Rank Correlation Coefficient (SRCC) [26]: is a nonparametric measure of rank correlation on the strength of a monotonic relationship between paired data. It is defined as the Pearson correlation coefficient between the rank variables. By leveraging its capacity to gauge monotonic relationships, SRCC facilitates the identification and removal of redundant parameters, contributing to a more efficient and simplified configuration. LOCAT [7] calculates SRCC between each configuration parameter and the workload execution time to filter out some redundant configuration parameters and narrow down the parameter search space.” • Principal Component Analysis (PCA): is a dimensionality reduction method that transforms correlated variables into uncorrelated ones, known as principal components. It does so by calculating the covariance matrix, performing eigendecomposition, and selecting a subset of principal components based on their variance. After SRCC, LOCAT [7] applies Gaussian kernel Principal Component Analysis (KPCA) to further extract and form new lower-dimensional parameters derived after removal of redundant configuration parameters for subsequent optimal parameter searching. • Least Absolute Shrinkage and Selection Operator (LASSO) and Lasso Path Algorithm. In the context of linear regression, LASSO modifies this objective function by adding a L1 penalty term equals to λ times the sum of absolute values of the regression coefficients, where λ controls the strength of the penalty, and it helps to reduce the effect of irrelevant variables by penalizing models with large weights. A higher λ value results in more coefficients being pushed toward zero, thereby achieving interpretable, stable, and computationally efficient feature selection. OtterTune [8] uses the LASSO path algorithm to assess the importance of configuration parameters by gradually reducing the penalty term. The order in 6 which parameters are included during this process is then considered as the importance order, and OtterTune uses this information to prune less important parameters. • Random Forest (RF): is a supervised machine learning method to deal with classification and regression problems. It can be deemed a voting algorithm consisting of multiple decision trees, used to calculate the importance of features. In the study [27], the authors utilize Latin Hypercube Sampling (LHS) to select a subset of configurations. Subsequently, they employ Random Forests with 300 Classification and Regression Trees (CART) to ascertain the importance of each parameter. Their findings reveal that when employing the YCSB workload-A on Cassandra, optimizing just five parameters can yield performance levels comparable to 99% of the best configurations attained through extensive parameter tuning. HUNTER [13] adopt the RF classifier with CARTs and adopts the majority voting principle to determine the importance of the knobs. It then selects the top-20 knobs to reduce the configuration search space in its tuning framework. • Hashing-enhanced Subspace BO (HeSBO) [28]: is a randomized low-dimensional projection approach that operates by deducing the search space for Bayesian Optimization-based solutions (discussed in Section 7.1). By utilizing two uniform hash functions to construct the rows and signs of the random projection matrix, it performs a count-sketch projection to transform the original high-dimensional configuration space into lower-dimensional subspace. This process is intuitively designed to effectively preserve the characteristics, such as pairwise point distances, of the original high-dimensional space for the reduced (important) dimensions. LlamaTune [16] adopts it to perform a projection of the configuration space from all documentations to a lowerdimensional subspace and then use the Bayesian Optimization-based optimizer to tune this smaller subspace. • Alternative approaches: Due to the challenges of tuning in high-dimensional configuration spaces, the identification of parameters that significantly affect DBMS performance has gained considerable attention. Among them, Researchers in [29] propose Caver to select a subset of storage parameters. Given a set of initial parameters and configurations, Carver first employs LHS to select a small number of configurations for evaluation. Subsequently, it greedily selects the most important parameters based on their parameter importance values. The first parameter is determined by the highest importance value, after which Carver fixes its value and computes the parameter importance values for the remaining parameters. During this process, importance is calculated as the difference between the variance of the original set of configurations and the sum of variances within the subset when parameters take different values. The algorithm selects parameters with the maximum difference. Recently, OpAdviser [30] proposed constructing an effective configuration region that contains promising configurations. It leverages performance similarity among historical workloads to guide the construction process. Specifically, it extracts a relatively compact region based on similar workloads and a larger region based on less similar ones. Furthermore, OpAdviser constructs the target search space by employing a majority weighted voting strategy to aggregate suggestions from candidate tasks. This paper is available on arxiv under CC BY 4.0 DEED. This paper is available on arxiv under CC BY 4.0 DEED. available on arxiv

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