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Even

even

Table of Links Abstract and 1 Introduction 2 Background and Motivation 2.1 Decentralized Computing Infrastructure and Cloud 2.2 Advantages of DeFaaS 2.3 System Requirements 3 Overview of DeFaaS 4 Detailed Design of DeFaaS and 4.1 Decentralized Scheduling and Load Balancing 4.2 Decentralized Event Distribution 4.3 API Registration and Access Control 4.4 OAuth2.0 Support 4.5 Logging and Billing and 4.6 Trust Management 4.7 Supporting Multi-Cloud Service Mesh 5 Implementation and Evaluation 5.1 Implementations 5.2 Experiments 5.3 Evaluation 6 Related Work 7 Conclusions and References 5.3 Evaluation Multi-cloud event dissemination. Multi-cloud event distribution performance is evaluated. We first present the latency with different applications from remote nodes in Figure 2. For small messages (Figure 2), the latency varies significantly across different remote nodes. In particular, the maximum latency can be much higher than the median value. To evaluate the performance for a given message size on each node, we run the experiment 30 times for selected applications. For Figure 1 we see that when communicating between different clouds, what affects the event latency more is the different geographical locations than different cloud providers. Since most of the cloud providers are already very optimized, inter-cloud communications have a lesser impact than actual physical servers being in different geographical regions than our testbed and also from the different servers sending the different-sized packets to IPFS. Note that the differences between applications are small and negligible. Out current work is to integrate with a new decentralized network for disseminating information on top of the IPFS, called SmartPubSub [Agostinho et al.(2022)]. SmartPubSub leverages ScoutSubs, a completely decentralized protocol for event message distribution and a fast delivery protocol centered around the publisher. Based on the experiments with a network of containerized SmartPubSub nodes (in one region), [Agostinho et al.(2022)], the average event latency under normal situation is between 200 - 250ms. Effect of IPFS read operations. We measure performance of the downloading operations across clouds (data saved to the IPFS and retrieved using read operations by another node). Latency is determined by the length of time it takes for an operation to complete, and throughput is calculated by dividing the amount of data processed by the latency of the operation. We observe that the locations of the remote nodes do not have a major impact on the read latency. This is likely because the experimented applications often perform small read requests, which can result in high variance in read latency. When a node performs remote read operations, the resolving and downloading operations required by the local node significantly affect the I/O performance. Randomized load balancing. To evaluate the performance of randomized load balancing, we have developed a simulator for experiments. The simulator models the API gateways and routing of API calls to multi-cloud data centers. In the current version, API calls take the same unit time to complete, which can be easily extended to random completion time. The simulator allows us to explore load balancing performance of different heuristics: (i) a default design based on the standard power of two choices; (ii) a simplified heuristic called choice of two; and (iii) a baseline of no randomized load balancing. In the default design, when an API gateway of a cloud data center receives a call, it will randomly pick another cloud data center using on-chain random source. If the receiving data center is busier than the randomly picked data center, then the call will be forwarded to the randomly picked data center. In the choice of two heuristic, when an API gateway of a cloud data center receives a call, it will evaluate whether the receiving cloud has been overloaded (queued calls exceeding its configured capability). If the receiving cloud is overloaded, it will forward the call to a randomly picked data center. In the baseline, there is no randomized load balancing. Due to space limit, here we report the results assuming that calls should be evenly distributed to the data centers. Further, when a call is forwarded, gateway node will be randomly selected. We have simulated three settings, i.e., 6, 8, and 10 data centers. Results are averaged based on five runs of the simulation. Each run simulates 10,000 API calls. Table 2 shows the average API call queue times for different experiment scenarios. According to the results, the default implementation of randomized load balancing achieves the best performance in terms of average queue times comparing with the other options. The second option (choice of two) only performs slightly worse. The average queue times are the worst without randomized load balancing. Although the second option has slightly longer average queue times, it may still be a good candidate in the actual deployment because of its simplicity. In the second option, a gateway node is not required to track or query any state status of other data centers. The support for a lightweight implementation (no need to keep states of other data centers) may overcome its slight disadvantage in performance. Further, the heuristic and simulator can be modified to use multi-dimension utility that combines cost, decentralization and performance. Authors:
(1) Rabimba Karanjai, Department of Computer Science, University of Houston (rkaranjai@uh.edu);
(2) Lei Xu, Department of Computer Science, Kent State University;
(3) Lin Chen, Department of Computer Science, Texas Texh University;
(4) Nour Diallo, Department of Computer Science, University Of Houston;
(5) Weidong Shi, Department of Computer Science, University Of Houston. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Background and Motivation 2.1 Decentralized Computing Infrastructure and Cloud 2.1 Decentralized Computing Infrastructure and Cloud 2.2 Advantages of DeFaaS 2.2 Advantages of DeFaaS 2.3 System Requirements 2.3 System Requirements 3 Overview of DeFaaS 3 Overview of DeFaaS 4 Detailed Design of DeFaaS and 4.1 Decentralized Scheduling and Load Balancing 4 Detailed Design of DeFaaS and 4.1 Decentralized Scheduling and Load Balancing 4.2 Decentralized Event Distribution 4.2 Decentralized Event Distribution 4.3 API Registration and Access Control 4.3 API Registration and Access Control 4.4 OAuth2.0 Support 4.4 OAuth2.0 Support 4.5 Logging and Billing and 4.6 Trust Management 4.5 Logging and Billing and 4.6 Trust Management 4.7 Supporting Multi-Cloud Service Mesh 4.7 Supporting Multi-Cloud Service Mesh 5 Implementation and Evaluation 5.1 Implementations 5.1 Implementations 5.2 Experiments 5.2 Experiments 5.3 Evaluation 5.3 Evaluation 6 Related Work 6 Related Work 7 Conclusions and References 7 Conclusions and References 5.3 Evaluation Multi-cloud event dissemination. Multi-cloud event distribution performance is evaluated. We first present the latency with different applications from remote nodes in Figure 2. For small messages (Figure 2), the latency varies significantly across different remote nodes. In particular, the maximum latency can be much higher than the median value. Multi-cloud event dissemination. To evaluate the performance for a given message size on each node, we run the experiment 30 times for selected applications. For Figure 1 we see that when communicating between different clouds, what affects the event latency more is the different geographical locations than different cloud providers. Since most of the cloud providers are already very optimized, inter-cloud communications have a lesser impact than actual physical servers being in different geographical regions than our testbed and also from the different servers sending the different-sized packets to IPFS. Note that the differences between applications are small and negligible. Out current work is to integrate with a new decentralized network for disseminating information on top of the IPFS, called SmartPubSub [Agostinho et al.(2022)]. SmartPubSub leverages ScoutSubs, a completely decentralized protocol for event message distribution and a fast delivery protocol centered around the publisher. Based on the experiments with a network of containerized SmartPubSub nodes (in one region), [Agostinho et al.(2022)], the average event latency under normal situation is between 200 - 250ms. Effect of IPFS read operations. We measure performance of the downloading operations across clouds (data saved to the IPFS and retrieved using read operations by another node). Latency is determined by the length of time it takes for an operation to complete, and throughput is calculated by dividing the amount of data processed by the latency of the operation. We observe that the locations of the remote nodes do not have a major impact on the read latency. This is likely because the experimented applications often perform small read requests, which can result in high variance in read latency. When a node performs remote read operations, the resolving and downloading operations required by the local node significantly affect the I/O performance. Effect of IPFS read operations. Randomized load balancing. To evaluate the performance of randomized load balancing, we have developed a simulator for experiments. The simulator models the API gateways and routing of API calls to multi-cloud data centers. In the current version, API calls take the same unit time to complete, which can be easily extended to random completion time. The simulator allows us to explore load balancing performance of different heuristics: (i) a default design based on the standard power of two choices; (ii) a simplified heuristic called choice of two; and (iii) a baseline of no randomized load balancing. In the default design, when an API gateway of a cloud data center receives a call, it will randomly pick another cloud data center using on-chain random source. If the receiving data center is busier than the Randomized load balancing. randomly picked data center, then the call will be forwarded to the randomly picked data center. In the choice of two heuristic, when an API gateway of a cloud data center receives a call, it will evaluate whether the receiving cloud has been overloaded (queued calls exceeding its configured capability). If the receiving cloud is overloaded, it will forward the call to a randomly picked data center. In the baseline, there is no randomized load balancing. Due to space limit, here we report the results assuming that calls should be evenly distributed to the data centers. Further, when a call is forwarded, gateway node will be randomly selected. We have simulated three settings, i.e., 6, 8, and 10 data centers. Results are averaged based on five runs of the simulation. Each run simulates 10,000 API calls. Table 2 shows the average API call queue times for different experiment scenarios. According to the results, the default implementation of randomized load balancing achieves the best performance in terms of average queue times comparing with the other options. The second option (choice of two) only performs slightly worse. The average queue times are the worst without randomized load balancing. Although the second option has slightly longer average queue times, it may still be a good candidate in the actual deployment because of its simplicity. In the second option, a gateway node is not required to track or query any state status of other data centers. The support for a lightweight implementation (no need to keep states of other data centers) may overcome its slight disadvantage in performance. Further, the heuristic and simulator can be modified to use multi-dimension utility that combines cost, decentralization and performance. Authors: (1) Rabimba Karanjai, Department of Computer Science, University of Houston (rkaranjai@uh.edu); (2) Lei Xu, Department of Computer Science, Kent State University; (3) Lin Chen, Department of Computer Science, Texas Texh University; (4) Nour Diallo, Department of Computer Science, University Of Houston; (5) Weidong Shi, Department of Computer Science, University Of Houston. Authors: Authors: (1) Rabimba Karanjai, Department of Computer Science, University of Houston (rkaranjai@uh.edu); (2) Lei Xu, Department of Computer Science, Kent State University; (3) Lin Chen, Department of Computer Science, Texas Texh University; (4) Nour Diallo, Department of Computer Science, University Of Houston; (5) Weidong Shi, Department of Computer Science, University Of Houston. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. available on arxiv available on arxiv

Evaluating Multi-Cloud Event Distribution and Load Balancing in DeFaaS

Table of Links Abstract and 1 Introduction 2 Background and Motivation 2.1 Decentralized Computing Infrastructure and Cloud 2.2 Advantages of DeFaaS 2.3 System Requirements 3 Overview of DeFaaS 4 Detailed Design of DeFaaS and 4.1 Decentralized Scheduling and Load Balancing 4.2 Decentralized Event Distribution 4.3 API Registration and Access Control 4.4 OAuth2.0 Support 4.5 Logging and Billing and 4.6 Trust Management 4.7 Supporting Multi-Cloud Service Mesh 5 Implementation and Evaluation 5.1 Implementations 5.2 Experiments 5.3 Evaluation 6 Related Work 7 Conclusions and References 4.2 Decentralized Event Distribution Event distribution is a critical element for a FaaS platform, including DeFaaS. It needs particular care in a multi-cloud setting because cloud dependent event mechanism for FaaS (e.g. [Amazon([n. d.])]) is not portable to the multi-cloud environment. Existing approaches to allow functions to consume events from different event sources on multiple clouds (e.g. [TriggerMesh([n. d.]), Knative([n. d.])]) are not designed for Web3 or dApp applications. In another word, although they can support cloud agnostic event sources/event consumers, they are not designed for decentralization. In this work, we use GossipSub [Vyzovitis et al.(2020b)] to build the event distribution system for dApps and Web3 applications. GossipSub is a robust message propagation system adopted by several important blockchain systems including Filecoin and ETH2.0 Network, and it works in the publish-subscribe mode that relies on a mesh network structure and a score function [Baldoni et al.(2007)] to disseminate messages. GossipSub builds on top of floodsub, which is a basic version of the PublishSubscribe system that is used in IPFS and Libp2p. It works by sending messages to all nodes in a network, rather than using a more efficient method called CastTree forming [libp2p(2022a)]. FloodSub is sometimes referred to as DumbSub or PubSub-Flood. The mesh network structure allows any node to participate in the message dissemination process, and the score function controls the transmission of messages based on the behavior of individual nodes. The features of GossipSub makes it a suitable tool for DeFaaS to manage events distribution in a decentralized manner across multiple cloud service providers. Support for function triggers can be implemented as a connector module in OpenFaaS. OpenFaaS is flexible enough to support multiple event trigger sources including third party event sources. In addition, after receiving a triggering event, function can be invoked. GossipSub connector is a module that maps topic based events to the registered functions in OpenFaaS. Nodes in the network can connect to each other in two ways: by sending complete messages to sparsely connected nodes called mesh members, or by sharing metadata about the availability of messages. Nodes can switch between these two types of connections, known as pruning and grafting, as needed. GossipSub [Vyzovitis et al.(2020a)] is designed to be efficient and scalable, with any peer able to participate in the message dissemination process. In addition to the mesh network and peering structure described previously, GossipSub also includes a system for scoring and limiting the transmission of messages based on the behavior of individual nodes. Nodes that do not reach a certain score threshold [libp2p(2022b)] will not receive messages. Additionally, nodes can publish messages on topics that they are not subscribed to by sending the message to a small number of randomly selected nodes, called fan-out peers, which will then redistribute the message throughout the network[libp2p(2022c)]. The combination of these features makes GossipSub highly efficient, reliable, and resistant to attacks, and allows it to scale effectively [Vyzovitis et al.(2020a)]. 4.3 API Registration and Access Control API providers need to register API end-points to the management blockchain. Registration is done through smart contract with a map that associates API end-point with the API provider’s account. API end-points can be offered to the public. Alternatively, API end-points can be private (only accessible by certain wallet accounts). Access control policies can be created for each API end-point. The policies are stored as a map from the API end-point and wallet address to a boolean value of true. If the map can be found, it means that the access is permitted. Note that for public API end-points, there is no need to perform this verification. Query of access control setting only involves view of on-chain state. Listing 1 shows an example of API registration with permission setting using Solidity. Since blockchain and smart contract based access control management has been intensively studied in the last few years (e.g., [Liu et al.(2021), Yang et al.(2020), Maesa et al.(2019), De and Ruj(2020), Almutairi et al.(2021)]), we skip details to save space and provide references for interested readers [Liu et al.(2021)]. 4.4 OAuth2.0 Support API gateways often apply common standard such as OAuth2.0 [Hardt(2012), Jones and Hardt(2012)] and OpenID [Sakimura et al.(2014)] to enable authentication to services and applications. OAuth2.0 [Hardt(2012), Jones and Hardt(2012)] is the industry-standard protocol for authorization through which an client obtains an access token from an authorization server, to access a protected resource, stored in a resource server. OAuth 2.0 has received widespread adoption by the cloud providers. It supports delegation and interoperability, and facilitates access control management. To support authorization for API calls, our platform comprises element to enable interoperability between blockchain based authorization and the API gateways using OAuth2.0 access tokens. For this objective, we take advantages of the existing research and prior work that attempt to integrate OAuth2.0 with smart contract and blockchains [Cha et al.(2021), Fotiou et al.(2020)]. Our implementation is based on [Fotiou et al.(2020)] and OpenFaaS plugin support for authentication. At high level, the workflow is the following. After verifying a client’s permission to access an API end-point, smart contract can generate an ERC token (ERC-721) and OAuth2.0 access token [Jones et al.(2015)]. The ERC token is transferred to the client’s wallet account, and the access token is provided to the client. Then, the client can request access to the API end-point providing the access token. The gateway node can retrieve the corresponding ERC-721 token which is used for verifying the validity and the ownership of the access token. If the verifications are successful, the gateway node will route the API call to the targeted function and return the response. In context of OpenFaaS, verification can be implemented as a plugin. Access tokens have expiration time. This reduces the overall performance impact of the described design. Authors:
(1) Rabimba Karanjai, Department of Computer Science, University of Houston (rkaranjai@uh.edu);
(2) Lei Xu, Department of Computer Science, Kent State University;
(3) Lin Chen, Department of Computer Science, Texas Texh University;
(4) Nour Diallo, Department of Computer Science, University Of Houston;
(5) Weidong Shi, Department of Computer Science, University Of Houston. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Background and Motivation 2.1 Decentralized Computing Infrastructure and Cloud 2.1 Decentralized Computing Infrastructure and Cloud 2.2 Advantages of DeFaaS 2.2 Advantages of DeFaaS 2.3 System Requirements 2.3 System Requirements 3 Overview of DeFaaS 3 Overview of DeFaaS 4 Detailed Design of DeFaaS and 4.1 Decentralized Scheduling and Load Balancing 4 Detailed Design of DeFaaS and 4.1 Decentralized Scheduling and Load Balancing 4.2 Decentralized Event Distribution 4.2 Decentralized Event Distribution 4.3 API Registration and Access Control 4.3 API Registration and Access Control 4.4 OAuth2.0 Support 4.4 OAuth2.0 Support 4.5 Logging and Billing and 4.6 Trust Management 4.5 Logging and Billing and 4.6 Trust Management 4.7 Supporting Multi-Cloud Service Mesh 4.7 Supporting Multi-Cloud Service Mesh 5 Implementation and Evaluation 5.1 Implementations 5.1 Implementations 5.2 Experiments 5.2 Experiments 5.3 Evaluation 5.3 Evaluation 6 Related Work 6 Related Work 7 Conclusions and References 7 Conclusions and References 4.2 Decentralized Event Distribution Event distribution is a critical element for a FaaS platform, including DeFaaS. It needs particular care in a multi-cloud setting because cloud dependent event mechanism for FaaS (e.g. [Amazon([n. d.])]) is not portable to the multi-cloud environment. Existing approaches to allow functions to consume events from different event sources on multiple clouds (e.g. [TriggerMesh([n. d.]), Knative([n. d.])]) are not designed for Web3 or dApp applications. In another word, although they can support cloud agnostic event sources/event consumers, they are not designed for decentralization. In this work, we use GossipSub [Vyzovitis et al.(2020b)] to build the event distribution system for dApps and Web3 applications. GossipSub is a robust message propagation system adopted by several important blockchain systems including Filecoin and ETH2.0 Network, and it works in the publish-subscribe mode that relies on a mesh network structure and a score function [Baldoni et al.(2007)] to disseminate messages. GossipSub builds on top of floodsub, which is a basic version of the PublishSubscribe system that is used in IPFS and Libp2p. It works by sending messages to all nodes in a network, rather than using a more efficient method called CastTree forming [libp2p(2022a)]. FloodSub is sometimes referred to as DumbSub or PubSub-Flood. The mesh network structure allows any node to participate in the message dissemination process, and the score function controls the transmission of messages based on the behavior of individual nodes. The features of GossipSub makes it a suitable tool for DeFaaS to manage events distribution in a decentralized manner across multiple cloud service providers. Support for function triggers can be implemented as a connector module in OpenFaaS. OpenFaaS is flexible enough to support multiple event trigger sources including third party event sources. In addition, after receiving a triggering event, function can be invoked. GossipSub connector is a module that maps topic based events to the registered functions in OpenFaaS. Nodes in the network can connect to each other in two ways: by sending complete messages to sparsely connected nodes called mesh members, or by sharing metadata about the availability of messages. Nodes can switch between these two types of connections, known as pruning and grafting, as needed. GossipSub [Vyzovitis et al.(2020a)] is designed to be efficient and scalable, with any peer able to participate in the message dissemination process. In addition to the mesh network and peering structure described previously, GossipSub also includes a system for scoring and limiting the transmission of messages based on the behavior of individual nodes. Nodes that do not reach a certain score threshold [libp2p(2022b)] will not receive messages. Additionally, nodes can publish messages on topics that they are not subscribed to by sending the message to a small number of randomly selected nodes, called fan-out peers, which will then redistribute the message throughout the network[libp2p(2022c)]. The combination of these features makes GossipSub highly efficient, reliable, and resistant to attacks, and allows it to scale effectively [Vyzovitis et al.(2020a)]. 4.3 API Registration and Access Control API providers need to register API end-points to the management blockchain. Registration is done through smart contract with a map that associates API end-point with the API provider’s account. API end-points can be offered to the public. Alternatively, API end-points can be private (only accessible by certain wallet accounts). Access control policies can be created for each API end-point. The policies are stored as a map from the API end-point and wallet address to a boolean value of true. If the map can be found, it means that the access is permitted. Note that for public API end-points, there is no need to perform this verification. Query of access control setting only involves view of on-chain state. Listing 1 shows an example of API registration with permission setting using Solidity. Since blockchain and smart contract based access control management has been intensively studied in the last few years (e.g., [Liu et al.(2021), Yang et al.(2020), Maesa et al.(2019), De and Ruj(2020), Almutairi et al.(2021)]), we skip details to save space and provide references for interested readers [Liu et al.(2021)]. 4.4 OAuth2.0 Support API gateways often apply common standard such as OAuth2.0 [Hardt(2012), Jones and Hardt(2012)] and OpenID [Sakimura et al.(2014)] to enable authentication to services and applications. OAuth2.0 [Hardt(2012), Jones and Hardt(2012)] is the industry-standard protocol for authorization through which an client obtains an access token from an authorization server, to access a protected resource, stored in a resource server. OAuth 2.0 has received widespread adoption by the cloud providers. It supports delegation and interoperability, and facilitates access control management. To support authorization for API calls, our platform comprises element to enable interoperability between blockchain based authorization and the API gateways using OAuth2.0 access tokens. For this objective, we take advantages of the existing research and prior work that attempt to integrate OAuth2.0 with smart contract and blockchains [Cha et al.(2021), Fotiou et al.(2020)]. Our implementation is based on [Fotiou et al.(2020)] and OpenFaaS plugin support for authentication. At high level, the workflow is the following. After verifying a client’s permission to access an API end-point, smart contract can generate an ERC token (ERC-721) and OAuth2.0 access token [Jones et al.(2015)]. The ERC token is transferred to the client’s wallet account, and the access token is provided to the client. Then, the client can request access to the API end-point providing the access token. The gateway node can retrieve the corresponding ERC-721 token which is used for verifying the validity and the ownership of the access token. If the verifications are successful, the gateway node will route the API call to the targeted function and return the response. In context of OpenFaaS, verification can be implemented as a plugin. Access tokens have expiration time. This reduces the overall performance impact of the described design. Authors: (1) Rabimba Karanjai, Department of Computer Science, University of Houston (rkaranjai@uh.edu); (2) Lei Xu, Department of Computer Science, Kent State University; (3) Lin Chen, Department of Computer Science, Texas Texh University; (4) Nour Diallo, Department of Computer Science, University Of Houston; (5) Weidong Shi, Department of Computer Science, University Of Houston. Authors: Authors: (1) Rabimba Karanjai, Department of Computer Science, University of Houston (rkaranjai@uh.edu); (2) Lei Xu, Department of Computer Science, Kent State University; (3) Lin Chen, Department of Computer Science, Texas Texh University; (4) Nour Diallo, Department of Computer Science, University Of Houston; (5) Weidong Shi, Department of Computer Science, University Of Houston. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. available on arxiv available on arxiv

Understanding Decentralized Event Distribution

Why You Should Start Onboarding New Hires Before They’re Even Hired

NICOSIA, Cyprus, January 20th, 2025/Chainwire/--Leading crypto casino BC. GAME has unveiled its latest Monkey King-themed slot game, Wukong, alongside the launch of the “Wukong Gold Legend” event. During the event, players have the potential to earn Wukong Coins by playing the Wukong slot and can explore two opportunities to win: exchanging Wukong Coins for cash rewards or holding them for a chance to share in a 1 BTC prize pool. Wukong Gold Legend The Wukong Gold Legend event runs from January 11, 2025, to February 10, 2025. During this period, players can collect exclusive Wukong Coins through the following activities: Betting Rewards: For every $1 wagered on the Wukong slot, players will receive 1 Wukong Coin.
Daily Check-ins: Logging in daily on the event page can yield up to 1,000 Wukong Coins.
Friend Invites: Both the inviter and the invited friend receive 100 Wukong Coins upon the friend's completion of KYC verification. Players unlock greater rewards and opportunities by accumulating Wukong Coins through these activities. Two Ways to Play with Wukong Coins: Cash Redemption and 1 BTC Prize Pool Wukong Coins are exclusive event tokens, offering players two distinct opportunities: Cash Redemption: Players can redeem Wukong Coins for BC.GAME’s native currency, BCD, can be used as cash to wager or exchanged for over 100 cryptocurrencies, including USDT, BTC, and ETH. Redemption Rates: 1,500 Wukong Coins = $50 BCD
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The remaining prize pool will be distributed proportionally among the other top 499 players, providing rewards to a wide range of participants Wukong Slot: A Unique Journey to the West At the heart of the event is BC.GAME’s newly launched Wukong slot. This 6-reel, 5-row slot game debuted on December 24, offering players a chance to experience the legendary Monkey King’s adventure while competing for rewards of up to 10,000x their bet. The game incorporates iconic elements inspired by Wukong’s mythology, such as the Golden Staff, magical gourds, and divine orbs, recreating the mythical atmosphere. The addition of a sticky symbol feature enhances the gaming experience by locking special symbols during free spins, significantly increasing the chances of unlocking major rewards. The event is live, offering participants the opportunity to play the Wukong slot, earn Wukong Coins, and compete for a share of 1 BTC and other rewards on BC.GAME. About BC.GAME BC.GAME is a leading global crypto gaming platform that offers a wide variety of games and a decentralized service model at its core, earning the trust and love of users worldwide. With innovative technology and a user-centric philosophy, BC.GAME has become a major player in the blockchain entertainment industry. BC.GAME will continue to deliver more innovative products and exclusive rewards to global crypto enthusiasts, injecting limitless possibilities into the gaming experience. Contact Olivia D oliviadi@bcgame.com This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here NICOSIA, Cyprus, January 20th, 2025/Chainwire/--Leading crypto casino BC. GAME has unveiled its latest Monkey King-themed slot game, Wukong , alongside the launch of the “ Wukong Gold Legend ” event. Wukong Wukong Wukong Gold Legend Wukong Gold Legend During the event, players have the potential to earn Wukong Coins by playing the Wukong slot and can explore two opportunities to win: exchanging Wukong Coins for cash rewards or holding them for a chance to share in a 1 BTC prize pool. Wukong Gold Legend The Wukong Gold Legend event runs from January 11, 2025, to February 10, 2025. During this period, players can collect exclusive Wukong Coins through the following activities: Wukong Gold Legend event Wukong Gold Legend event Betting Rewards: For every $1 wagered on the Wukong slot, players will receive 1 Wukong Coin. Daily Check-ins: Logging in daily on the event page can yield up to 1,000 Wukong Coins. Friend Invites: Both the inviter and the invited friend receive 100 Wukong Coins upon the friend's completion of KYC verification. Betting Rewards: For every $1 wagered on the Wukong slot, players will receive 1 Wukong Coin. Daily Check-ins: Logging in daily on the event page can yield up to 1,000 Wukong Coins. Friend Invites: Both the inviter and the invited friend receive 100 Wukong Coins upon the friend's completion of KYC verification. Players unlock greater rewards and opportunities by accumulating Wukong Coins through these activities. Two Ways to Play with Wukong Coins: Cash Redemption and 1 BTC Prize Pool Wukong Coins are exclusive event tokens, offering players two distinct opportunities: Cash Redemption: Players can redeem Wukong Coins for BC.GAME’s native currency, BCD, can be used as cash to wager or exchanged for over 100 cryptocurrencies, including USDT, BTC, and ETH. Redemption Rates: 1,500 Wukong Coins = $50 BCD 10,000 Wukong Coins = $350 BCD 18,000 Wukong Coins = $700 BCD 23,000 Wukong Coins = $900 BCD 1,500 Wukong Coins = $50 BCD 10,000 Wukong Coins = $350 BCD 18,000 Wukong Coins = $700 BCD 23,000 Wukong Coins = $900 BCD Redemption Time: Limits refresh every Monday at 08:00 UTC and redeemed BCD is credited instantly. Share in a 1 BTC Prize Pool: At the end of the event, the top 500 Wukong Coin holders will share a 1 BTC prize pool: The top-ranked player will receive 10% of the pool. The remaining prize pool will be distributed proportionally among the other top 499 players, providing rewards to a wide range of participants The top-ranked player will receive 10% of the pool. The remaining prize pool will be distributed proportionally among the other top 499 players, providing rewards to a wide range of participants Wukong Slot: A Unique Journey to the West At the heart of the event is BC.GAME’s newly launched Wukong slot . This 6-reel, 5-row slot game debuted on December 24, offering players a chance to experience the legendary Monkey King’s adventure while competing for rewards of up to 10,000x their bet. Wukong slot Wukong slot The game incorporates iconic elements inspired by Wukong’s mythology, such as the Golden Staff, magical gourds, and divine orbs, recreating the mythical atmosphere. The addition of a sticky symbol feature enhances the gaming experience by locking special symbols during free spins, significantly increasing the chances of unlocking major rewards. The event is live, offering participants the opportunity to play the Wukong slot, earn Wukong Coins, and compete for a share of 1 BTC and other rewards on BC.GAME . BC.GAME BC.GAME About BC.GAME BC.GAME is a leading global crypto gaming platform that offers a wide variety of games and a decentralized service model at its core, earning the trust and love of users worldwide. BC.GAME BC.GAME With innovative technology and a user-centric philosophy, BC.GAME has become a major player in the blockchain entertainment industry. BC.GAME will continue to deliver more innovative products and exclusive rewards to global crypto enthusiasts, injecting limitless possibilities into the gaming experience. Contact Olivia D oliviadi@bcgame.com This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here here here

BC.GAME Launches Wukong Slot and 'Wukong Gold Legend' Event With 1 BTC Prize Pool And Rewards

BC.GAME стартира Wukong Slot и събитието „Wukong Gold Legend“ с 1 BTC награден фонд и награди

BC.GAME lanza la tragamonedas Wukong y el evento 'Wukong Gold Legend' con premios y recompensas de 1 BTC

BC.GAME اسلات Wukong و رویداد Wukong Gold Legend را با 1 استخر جایزه BTC و جوایز راه اندازی کرد.

A BC.GAME elindítja a Wukong nyerőgépet és a „Wukong Gold Legend” eseményt 1 BTC nyereményalappal és jutalmakkal

BC.GAME が Wukong スロットと 1 BTC の賞金プールと報酬を備えた「Wukong Gold Legend」イベントを開始

BC.GAME បើកដំណើរការរន្ធដោត Wukong និងព្រឹត្តិការណ៍ 'Wukong Gold Legend' ជាមួយនឹងរង្វាន់ 1 BTC និងរង្វាន់

BC.GAME dia namoaka ny Wukong Slot sy ny hetsika 'Wukong Gold Legend' miaraka amin'ny 1 BTC loka sy valisoa

BC.GAME د 1 BTC جایزې پول او انعامونو سره د ووکونګ سلاټ او 'وکونګ گولډ لیجنډ' پیښه پیل کړه

BC.GAME Wukong Ranura y 'Wukong Quri Leyenda' Evento 1 BTC Premio Piscinawan Premioswan qallarichin

BC.GAME spúšťa automat Wukong a udalosť „Wukong Gold Legend“ s 1 balíkom cien BTC a odmenami

BC.GAME Inotangisa Wukong Slot uye 'Wukong Gold Legend' Chiitiko Ine 1 BTC Mubairo Dziva Uye Mibayiro

BC.GAME Waxay Bilawday Dhacdo Wukong Slot iyo 'Wukong Gold Legend' oo leh 1 BTC Prize Pool iyo Abaalmarino

Seychelles, January 16, 2025 – MEXC, a leading global digital asset exchange, has achieved a landmark milestone by successfully executing 2,202 airdrop events throughout 2024, generating an exceptional 65% annual percentage yield (APY) for participants. The exchange distributed a substantial $127 million in rewards to its global user base, further strengthening its position as an industry pioneer in user value creation. In 2024, MEXC's monthly reward pool averaged 10.62 million USDT, with an impressive 184 airdrops per month. These events spanned a wide range of categories, including meme coins, utility tokens, and gaming-related tokens, ensuring users had consistent opportunities to participate and earn rewards, no matter their investment preferences. MEXC's Launchpool and Kickstarter programs have redefined how users access free tokens. Unlike other platforms that typically require users to stake large amounts of tokens or complete complex tasks, MEXC implements a straightforward approach. Users can automatically qualify for airdrops by holding 1,000 MX tokens for 24 hours before an event. This simple process allows both new and experienced users to participate and earn rewards easily. \Looking toward 2025, the platform will continue to focus on providing users with generous rewards through simple participation processes, high-frequency airdrops, and a diverse selection of tokens. With its industry-leading airdrop distribution standards, these advantages have established MEXC as the best place for cryptocurrency enthusiasts worldwide to access free airdrop opportunities. About MEXC Founded in 2018, MEXC is committed to being "Your Easiest Way to Crypto". Serving over 30 million users across 170+ countries, MEXC is known for its broad selection of trending tokens, frequent airdrop opportunities, and low trading fees. Our user-friendly platform is designed to support both new traders and experienced investors, offering secure and efficient access to digital assets. MEXC prioritizes simplicity and innovation, making crypto trading more accessible and rewarding. MEXC Official Website｜ X ｜ Telegram ｜How to Sign Up on MEXC Risk Disclaimer: The information provided in this article about cryptocurrencies does not represent MEXC's official stance or investment advice. Given the highly volatile nature of the cryptocurrency market, investors are encouraged to carefully evaluate market fluctuations, project fundamentals, and potential financial risks before making any trading decisions. This story was distributed as a release by Btcwire under HackerNoon’s Business Blogging Program. Learn more about the program here Seychelles, January 16, 2025 – MEXC, a leading global digital asset exchange, has achieved a landmark milestone by successfully executing 2,202 airdrop events throughout 2024, generating an exceptional 65% annual percentage yield (APY) for participants. The exchange distributed a substantial $127 million in rewards to its global user base, further strengthening its position as an industry pioneer in user value creation. In 2024, MEXC's monthly reward pool averaged 10.62 million USDT, with an impressive 184 airdrops per month. These events spanned a wide range of categories, including meme coins, utility tokens, and gaming-related tokens, ensuring users had consistent opportunities to participate and earn rewards, no matter their investment preferences. MEXC's Launchpool and Kickstarter programs have redefined how users access free tokens. Unlike other platforms that typically require users to stake large amounts of tokens or complete complex tasks, MEXC implements a straightforward approach. Users can automatically qualify for airdrops by holding 1,000 MX tokens for 24 hours before an event. This simple process allows both new and experienced users to participate and earn rewards easily. \Looking toward 2025, the platform will continue to focus on providing users with generous rewards through simple participation processes, high-frequency airdrops, and a diverse selection of tokens. With its industry-leading airdrop distribution standards, these advantages have established MEXC as the best place for cryptocurrency enthusiasts worldwide to access free airdrop opportunities. About MEXC Founded in 2018, MEXC is committed to being "Your Easiest Way to Crypto". Serving over 30 million users across 170+ countries, MEXC is known for its broad selection of trending tokens, frequent airdrop opportunities, and low trading fees. Our user-friendly platform is designed to support both new traders and experienced investors, offering secure and efficient access to digital assets. MEXC prioritizes simplicity and innovation, making crypto trading more accessible and rewarding. MEXC Official Website｜ X ｜ Telegram ｜How to Sign Up on MEXC MEXC Official Website ｜ X ｜ Telegram ｜ How to Sign Up on MEXC MEXC Official Website ｜ X ｜ Telegram ｜ X ｜ Telegram X ｜ Telegram How to Sign Up on MEXC Risk Disclaimer: The information provided in this article about cryptocurrencies does not represent MEXC's official stance or investment advice. Given the highly volatile nature of the cryptocurrency market, investors are encouraged to carefully evaluate market fluctuations, project fundamentals, and potential financial risks before making any trading decisions. This story was distributed as a release by Btcwire under HackerNoon’s Business Blogging Program. Learn more about the program here This story was distributed as a release by Btcwire under HackerNoon’s Business Blogging Program. Learn more about the program here here here

MEXC Distributes $127M In Rewards Through 2,202 Airdrop Events In 2024, Offering Up To 65% APY

MEXC distribuye $127 millones en recompensas a través de 2202 eventos de airdrop en 2024, ofreciendo hasta un 65 % de APY

MEXC-k 127 milioi dolar banatzen ditu 2024an Airdrop-eko 2.202 ekitaldiren bidez, APY% 65era arte eskainiz

A MEXC 127 millió dollár jutalmat oszt szét 2202 Airdrop eseményen keresztül 2024-ben, akár 65%-os APY-t kínálva

MEXCは2024年に2,202回のエアドロップイベントを通じて1億2,700万ドルの報酬を配布し、年利最大65%を提供

MEXC는 2024년 2,202개의 에어드롭 이벤트를 통해 1억 2,700만 달러의 보상을 분배하고 최대 65% APY를 제공합니다.

MEXC په 2024 کې د 2,202 ایرډراپ پیښو له لارې په انعامونو کې $ 127M توزیع کوي، تر 65٪ APY وړاندیز کوي

MEXC Ikwirakwiza $ 127M Mubihembo Binyuze mu bikorwa bya Airdrop 2,202 Muri 2024, Itanga 65% APY

MEXC แจกรางวัลมูลค่า 127 ล้านดอลลาร์ผ่านกิจกรรม Airdrop 2,202 รายการในปี 2024 พร้อมเสนอ APY สูงถึง 65%

MEXC Yi hangalasa $127M Eka Hakelo Hi Ku Tirhisa 2,202 Wa Swiendlakalo Swa Airdrop Hi 2024, Yi Nyikela Ku Fika Eka 65% APY

I-MEXC isasaza i-127 yezigidi zeerandi kuMvuzo ngeMisitho ye-Airdrop eyi-2,202 ngo-2024, ibonelela ukuya kwi-65% ye-APY.

MEXC 将于 2024 年通过 2,202 次空投活动发放 1.27 亿美元奖励，年化收益率高达 65%

I-MEXC Isabalalisa U-$127M Emiklomelo Ngemicimbi Ye-Airdrop engu-2,202 Ngo-2024, Inikela Kufika Ku-65% APY

As we step into 2025, the Pacific region continues to grapple with the impacts of natural disasters, and recently, both Vanuatu and Tonga found themselves at the center of significant crises. In Vanuatu, a powerful earthquake struck the archipelago, causing widespread destruction and severing vital submarine fiber optic cable that had long been the lifeline for communication and connectivity. In Tonga, the country has experienced multiple internet outages related to its submarine cable infrastructure, with an 80% probability of such incidents recurring in the future. In these critical moments, Starlink emerged as a beacon of hope, showcasing its remarkable capabilities in restoring connectivity and ultimately saving lives. Rather than relying solely on traditional infrastructure, which had been compromised, the people of Vanuatu and Tonga turned to Starlink's innovative satellite technology. With a network of satellites orbiting the Earth, Starlink provided fast, reliable internet access that transcended the limitations of submarine cables. Imagine families separated by disaster, unable to communicate with loved ones or access essential services. With Starlink's deployment, individuals could connect to emergency services, share information, and reach out for help. In regions where every second counts, this connectivity was not just a convenience; it was a lifeline. Starlink's technology facilitated critical communication, allowing first responders to coordinate rescue efforts effectively and ensuring that medical facilities could operate without interruption. In Tonga, where repeated outages had left communities disconnected, Starlink offered a more resilient alternative, ensuring that people could stay connected even during the most challenging times. Reflecting on these recent events, my perspective on connectivity infrastructure is shifting. While Starlink has demonstrated the immediate practical benefits of satellite-based internet, emerging technology like Spacecoin is taking this concept even further by combining satellite connectivity with blockchain technology. This convergence of technologies suggests that not only do we not need traditional submarine cables, but we're on the cusp of a truly disruptive approach to internet infrastructure. The traditional internet infrastructure, dominated by submarine cables and controlled by a handful of major telecommunications companies, has created a centralized system vulnerable to both natural disasters and human intervention. While Starlink's satellite network offers one path to decentralization, Spacecoin's vision of a blockchain-powered satellite network represents the next evolution in this journey. By combining Low-Earth Orbit (LEO) satellites with blockchain technology, Spacecoin aims to create a trustless, decentralized network that could further democratize internet access and ensure complete freedom from centralized control. This shift towards decentralization through both satellite technology and blockchain could dramatically impact global accessibility and free speech, particularly in regions like the Pacific Islands. Starlink has already shown how satellite-based systems can maintain connectivity during infrastructure failures. Spacecoin's blockchain integration could take this further by ensuring that this connectivity isn't just resilient but also resistant to censorship and control through its decentralized governance structure. The crux of the connectivity challenge in the Pacific Islands lies not only in the "last mile" problem but also in the network effect that has kept communities tethered to traditional infrastructure solutions. While Starlink's Direct to Cell capability offers one solution, Spacecoin's blockchain-based approach could provide additional benefits by enabling peer-to-peer connectivity and removing the need for traditional telecommunications intermediaries entirely. In light of these challenges, Starlink's Direct to Cell capability presents an innovative solution for enhancing connectivity in the Pacific. This technology allows individuals to connect their mobile devices directly to satellites, eliminating the need for traditional ground-based infrastructure entirely. In areas where terrestrial networks are sparse or unreliable, Direct to Cell provides a lifeline, enabling seamless communication even in the most remote locations. As this capability expands, it reinforces the notion that satellite solutions can effectively meet the unique needs of small island communities, offering a reliable alternative that avoids the pitfalls of underutilized submarine systems. In the current internet landscape, algorithms controlled by major platforms significantly influence public perception and information flow. The combination of Starlink's satellite network and Spacecoin's blockchain technology could offer a powerful counter to this centralized control. While Starlink provides the physical infrastructure for decentralized access, Spacecoin's blockchain framework could ensure that the governance and operation of this infrastructure remain truly decentralized and community-driven. While fully decentralized internet through satellite technology promises greater freedom from traditional forms of censorship, new challenges may emerge. The key will be ensuring that satellite providers themselves don't become new centralized authorities. This is where Spacecoin's blockchain governance model becomes particularly relevant, offering a transparent and democratic approach to network management that complements Starlink's physical infrastructure. Starlink's satellites offer a more resilient and adaptable solution for small island nations. With the ability to roam and provide backhaul capabilities, Starlink can deliver high-speed internet to even the most remote locations, often at a fraction of the cost of traditional infrastructure. The earthquakes in Vanuatu and the recurring issues in Tonga have highlighted this shift, demonstrating that when disaster strikes, the ability to connect via satellite can be more effective than relying on undersea cables that can easily be severed. As we embrace this new era of connectivity, we're seeing a convergence of solutions. Starlink has proven the viability of satellite-based internet in crisis situations, while Spacecoin's blockchain-powered approach promises to take this decentralization even further. For regions like Vanuatu and Tonga, this combination of technologies offers not just an alternative to traditional infrastructure, but a path toward a truly decentralized, resilient, and democratic internet. The dawn of 2025 brings with it not just the promise of better connectivity, but of a fundamental transformation in how we think about internet infrastructure. As Starlink continues to demonstrate the practical benefits of satellite-based internet, and as Spacecoin develops its blockchain-powered satellite network, we're moving toward a future where the question isn't whether we can survive without submarine cables, but how quickly we can embrace these new decentralized technologies that promise to revolutionize global connectivity. As we step into 2025, the Pacific region continues to grapple with the impacts of natural disasters, and recently, both Vanuatu and Tonga found themselves at the center of significant crises. In Vanuatu, a powerful earthquake struck the archipelago, causing widespread destruction and severing vital submarine fiber optic cable that had long been the lifeline for communication and connectivity. In Tonga, the country has experienced multiple internet outages related to its submarine cable infrastructure, with an 80% probability of such incidents recurring in the future. In these critical moments, Starlink emerged as a beacon of hope, showcasing its remarkable capabilities in restoring connectivity and ultimately saving lives. Rather than relying solely on traditional infrastructure, which had been compromised, the people of Vanuatu and Tonga turned to Starlink's innovative satellite technology. With a network of satellites orbiting the Earth, Starlink provided fast, reliable internet access that transcended the limitations of submarine cables. Imagine families separated by disaster, unable to communicate with loved ones or access essential services. With Starlink's deployment, individuals could connect to emergency services, share information, and reach out for help. In regions where every second counts, this connectivity was not just a convenience; it was a lifeline. Starlink's technology facilitated critical communication, allowing first responders to coordinate rescue efforts effectively and ensuring that medical facilities could operate without interruption. In Tonga, where repeated outages had left communities disconnected, Starlink offered a more resilient alternative, ensuring that people could stay connected even during the most challenging times. Reflecting on these recent events, my perspective on connectivity infrastructure is shifting. While Starlink has demonstrated the immediate practical benefits of satellite-based internet, emerging technology like Spacecoin is taking this concept even further by combining satellite connectivity with blockchain technology. This convergence of technologies suggests that not only do we not need traditional submarine cables, but we're on the cusp of a truly disruptive approach to internet infrastructure. Spacecoin The traditional internet infrastructure, dominated by submarine cables and controlled by a handful of major telecommunications companies, has created a centralized system vulnerable to both natural disasters and human intervention. While Starlink's satellite network offers one path to decentralization, Spacecoin's vision of a blockchain-powered satellite network represents the next evolution in this journey. By combining Low-Earth Orbit (LEO) satellites with blockchain technology, Spacecoin aims to create a trustless, decentralized network that could further democratize internet access and ensure complete freedom from centralized control. This shift towards decentralization through both satellite technology and blockchain could dramatically impact global accessibility and free speech, particularly in regions like the Pacific Islands. Starlink has already shown how satellite-based systems can maintain connectivity during infrastructure failures. Spacecoin's blockchain integration could take this further by ensuring that this connectivity isn't just resilient but also resistant to censorship and control through its decentralized governance structure. The crux of the connectivity challenge in the Pacific Islands lies not only in the "last mile" problem but also in the network effect that has kept communities tethered to traditional infrastructure solutions. While Starlink's Direct to Cell capability offers one solution, Spacecoin's blockchain-based approach could provide additional benefits by enabling peer-to-peer connectivity and removing the need for traditional telecommunications intermediaries entirely. In light of these challenges, Starlink's Direct to Cell capability presents an innovative solution for enhancing connectivity in the Pacific. This technology allows individuals to connect their mobile devices directly to satellites, eliminating the need for traditional ground-based infrastructure entirely. In areas where terrestrial networks are sparse or unreliable, Direct to Cell provides a lifeline, enabling seamless communication even in the most remote locations. As this capability expands, it reinforces the notion that satellite solutions can effectively meet the unique needs of small island communities, offering a reliable alternative that avoids the pitfalls of underutilized submarine systems. In the current internet landscape, algorithms controlled by major platforms significantly influence public perception and information flow. The combination of Starlink's satellite network and Spacecoin's blockchain technology could offer a powerful counter to this centralized control. While Starlink provides the physical infrastructure for decentralized access, Spacecoin's blockchain framework could ensure that the governance and operation of this infrastructure remain truly decentralized and community-driven. While fully decentralized internet through satellite technology promises greater freedom from traditional forms of censorship, new challenges may emerge. The key will be ensuring that satellite providers themselves don't become new centralized authorities. This is where Spacecoin's blockchain governance model becomes particularly relevant, offering a transparent and democratic approach to network management that complements Starlink's physical infrastructure. Starlink's satellites offer a more resilient and adaptable solution for small island nations. With the ability to roam and provide backhaul capabilities, Starlink can deliver high-speed internet to even the most remote locations, often at a fraction of the cost of traditional infrastructure. The earthquakes in Vanuatu and the recurring issues in Tonga have highlighted this shift, demonstrating that when disaster strikes, the ability to connect via satellite can be more effective than relying on undersea cables that can easily be severed. As we embrace this new era of connectivity, we're seeing a convergence of solutions. Starlink has proven the viability of satellite-based internet in crisis situations, while Spacecoin's blockchain-powered approach promises to take this decentralization even further. For regions like Vanuatu and Tonga, this combination of technologies offers not just an alternative to traditional infrastructure, but a path toward a truly decentralized, resilient, and democratic internet. The dawn of 2025 brings with it not just the promise of better connectivity, but of a fundamental transformation in how we think about internet infrastructure. As Starlink continues to demonstrate the practical benefits of satellite-based internet, and as Spacecoin develops its blockchain-powered satellite network, we're moving toward a future where the question isn't whether we can survive without submarine cables, but how quickly we can embrace these new decentralized technologies that promise to revolutionize global connectivity.

Who Even Needs Undersea Cables for Internet Anymore? 

Streaming in Next.js 15: WebSockets vs Server-Sent Events

The Quantum Apocalypse: Your Encryption Will Fail And Even Your Bitcoin Will Be At Risk

An Antipattern describes a common response to a problem that is ineffective and creates negative consequences. Alternatively, Design Patterns are common approaches to common problems which have been formalized and are generally considered a good practice. Antipatterns are the opposite and are undesirable. The Chatty Input / Output (I/O) Anti pattern occurs when the cumulative effect of a large number of Input and Output requests has a significant negative impact on the performance and responsiveness of a service. Network calls and other I/O operations are inherently slow compared to compute tasks. Each I/O request typically has significant overhead, and the cumulative effect of numerous I/O operations can slow down the service. Potential Sources Application components that can be the source of chatty I/O include: Databases: Can become chatty when a single use case or business transaction results in multiple queries to the same set of data.
APIs: Requiring multiple network calls for a single user operation will slow down an application as each call contains data overhead (i.e. sender information, headers, authentication).
File Operations: An application that continually reads and writes small amounts of information to a file will generate significant I/O overhead. Small write requests can also lead to file fragmentation, slowing subsequent I/O operations still further. Examples of Chatty I/O The following examples demonstrate the Antipattern. Reading and writing individual records to a database as distinct requests The following example reads from a database of products. There are three tables, Product, ProductSubcategory, and ProductPriceListHistory. The code retrieves all of the products in a subcategory, along with the pricing information, by executing a series of queries: Query the subcategory from the ProductSubcategory table.
Find all products in that subcategory by querying the Product table.
For each product, query the pricing data from the ProductListPriceHistory table. The application uses Entity Framework, a layer of software that enables developers to work with data using objects without focusing on the underlying database where this data is stored. The N+1 Problem The example above shows the problem explicitly, but sometimes an Object-Relational Mapping (ORM) can mask the problem, if it implicitly fetches child records one at a time. This is known as the "N+1 problem". The N+1 query problem happens when the data access framework executed N additional SQL statements to fetch the same data that could have been retrieved when executing the primary SQL query. The larger the value of N, the more queries will be executed and the larger the performance impact. Implementing a single logical operation as a series of HTTP requests This often happens when developers try to follow an object-oriented paradigm and treat remote objects as if they were local objects in memory. This can result in too many network round trips. For example, the following web API exposes the individual properties of User objects through individual HTTP GET methods. While there's nothing technically wrong with this approach, most clients will probably need to get several properties for each User, resulting in client code like the following. Reading and writing to a file on disk File I/O involves opening a file and moving to the appropriate point before reading or writing data. When the operation is complete, the file might be closed to save operating system resources. An application that continually reads and writes small amounts of information to a file will generate significant I/O overhead. Small write requests can also lead to file fragmentation, slowing subsequent I/O operations still further. The following example uses a FileStream to write a Customer object to a file. Creating the FileStream opens the file, and disposing it closes the file. (The using statement automatically disposes the FileStream object.) If the application calls this method repeatedly as new customers are added, the I/O overhead can accumulate quickly. How to Fix the Problem Some of the remedies for fixing this Antipattern include: Fetch data from a database as a single query, instead of several smaller queries.
Follow REST design principles for web APIs.
Buffer data in memory for file operations and then write data once Database: Fetch Data in a Single Query Fetch data from a database as a single query, instead of several smaller queries. Here's a revised version of the code that retrieves product information. API: Use REST Principles Follow REST design principles for web APIs. Here's a revised version of the web API from the earlier example. Instead of separate GET methods for each property, there is a single GET method that returns the User. This results in a larger response body per request, but each client is likely to make fewer API calls. File: Buffer Data in Memory For file I/O, consider buffering data in memory and then writing the buffered data to a file as a single operation. This approach reduces the overhead from repeatedly opening and closing the file and helps to reduce fragmentation of the file on disk. Service Granularity The Service Granularity Principle is a software design concern that specifies the scope of business functionality and the structure of the message payload in a service operation. Achieving optimal service granularity can be difficult. Too granular and the service can become chatty while too course can be inefficient by retrieving more data than is required. The Use Case for the service must be matched with the proper service granularity to ensure an efficient design which avoids the Chatty IO AntiPattern. Detecting the Problem Symptoms of chatty I/O include high latency and low throughput. End users are likely to report extended response times or failures caused by services timing out, due to increased contention for I/O resources. Process Monitoring Perform process monitoring of the production system to identify operations with poor response times.
Perform load testing of each operation identified in the previous step.
During the load tests, gather telemetry data about the data access requests made by each operation.
Gather detailed statistics for each request sent to a data store.
Profile the application in the test environment to detect possible, I/O bottlenecks Look for any of these symptoms: large number of small I/O requests made to the same file.
large number of small network requests made by an application instance to the same service.
large number of small requests made by an application instance to the same data store.
Applications and services becoming I/O bound. Monitor the Application Using App Dynamics Using an Application Performance Monitoring (APM) package such as App Dynamics to capture and analyze key metrics that can identify chatty I/O. The key performance metrics will vary depending on the I/O workload. For example, database, file or API. Summary Chatty I/O is an Anti pattern that can occur within several parts of an application or service including the database, file operations and inter-process communications with APIs. The cumulative effect of a large number of I/O requests can have a significant impact on performance and responsiveness. Several monitoring techniques can be used to find the root cause of the chatty component. Software designs should consider the Service Granularity Principle. That is, scoping data retrieval to match the Use Case so the appropriate amount of data is retrieved in each request. Thanks & Happy reading! An Antipattern describes a common response to a problem that is ineffective and creates negative consequences. Alternatively, Design Patterns are common approaches to common problems which have been formalized and are generally considered a good practice. Antipatterns are the opposite and are undesirable . undesirable The Chatty Input / Output (I/O) Anti pattern occurs when the cumulative effect of a large number of Input and Output requests has a significant negative impact on the performance and responsiveness of a service. Network calls and other I/O operations are inherently slow compared to compute tasks. Each I/O request typically has significant overhead, and the cumulative effect of numerous I/O operations can slow down the service. Potential Sources Application components that can be the source of chatty I/O include: Databases: Can become chatty when a single use case or business transaction results in multiple queries to the same set of data. APIs: Requiring multiple network calls for a single user operation will slow down an application as each call contains data overhead (i.e. sender information, headers, authentication). File Operations: An application that continually reads and writes small amounts of information to a file will generate significant I/O overhead. Small write requests can also lead to file fragmentation, slowing subsequent I/O operations still further. Databases: Can become chatty when a single use case or business transaction results in multiple queries to the same set of data. Databases: Databases: APIs: Requiring multiple network calls for a single user operation will slow down an application as each call contains data overhead (i.e. sender information, headers, authentication). APIs: APIs: File Operations: An application that continually reads and writes small amounts of information to a file will generate significant I/O overhead. Small write requests can also lead to file fragmentation, slowing subsequent I/O operations still further. File Operations: File Operations: Examples of Chatty I/O The following examples demonstrate the Antipattern. Reading and writing individual records to a database as distinct requests The following example reads from a database of products. There are three tables, Product, ProductSubcategory, and ProductPriceListHistory. The code retrieves all of the products in a subcategory, along with the pricing information, by executing a series of queries: Query the subcategory from the ProductSubcategory table. Find all products in that subcategory by querying the Product table. For each product, query the pricing data from the ProductListPriceHistory table. Query the subcategory from the ProductSubcategory table. Find all products in that subcategory by querying the Product table. For each product, query the pricing data from the ProductListPriceHistory table. The application uses Entity Framework, a layer of software that enables developers to work with data using objects without focusing on the underlying database where this data is stored. The N+1 Problem The example above shows the problem explicitly, but sometimes an Object-Relational Mapping (ORM) can mask the problem, if it implicitly fetches child records one at a time. This is known as the "N+1 problem". The N+1 query problem happens when the data access framework executed N additional SQL statements to fetch the same data that could have been retrieved when executing the primary SQL query. The larger the value of N, the more queries will be executed and the larger the performance impact. Implementing a single logical operation as a series of HTTP requests This often happens when developers try to follow an object-oriented paradigm and treat remote objects as if they were local objects in memory. This can result in too many network round trips. For example, the following web API exposes the individual properties of User objects through individual HTTP GET methods. While there's nothing technically wrong with this approach, most clients will probably need to get several properties for each User, resulting in client code like the following. Reading and writing to a file on disk File I/O involves opening a file and moving to the appropriate point before reading or writing data. When the operation is complete, the file might be closed to save operating system resources. An application that continually reads and writes small amounts of information to a file will generate significant I/O overhead. Small write requests can also lead to file fragmentation, slowing subsequent I/O operations still further. The following example uses a FileStream to write a Customer object to a file. Creating the FileStream opens the file, and disposing it closes the file. (The using statement automatically disposes the FileStream object.) If the application calls this method repeatedly as new customers are added, the I/O overhead can accumulate quickly. How to Fix the Problem Some of the remedies for fixing this Antipattern include: Fetch data from a database as a single query, instead of several smaller queries. Follow REST design principles for web APIs. Buffer data in memory for file operations and then write data once Fetch data from a database as a single query, instead of several smaller queries. Follow REST design principles for web APIs. Buffer data in memory for file operations and then write data once Database: Fetch Data in a Single Query Fetch data from a database as a single query, instead of several smaller queries. Here's a revised version of the code that retrieves product information. API: Use REST Principles Follow REST design principles for web APIs. Here's a revised version of the web API from the earlier example. Instead of separate GET methods for each property, there is a single GET method that returns the User. This results in a larger response body per request, but each client is likely to make fewer API calls. File: Buffer Data in Memory For file I/O, consider buffering data in memory and then writing the buffered data to a file as a single operation. This approach reduces the overhead from repeatedly opening and closing the file and helps to reduce fragmentation of the file on disk. Service Granularity The Service Granularity Principle is a software design concern that specifies the scope of business functionality and the structure of the message payload in a service operation. Achieving optimal service granularity can be difficult. Too granular and the service can become chatty while too course can be inefficient by retrieving more data than is required. The Use Case for the service must be matched with the proper service granularity to ensure an efficient design which avoids the Chatty IO AntiPattern. Detecting the Problem Symptoms of chatty I/O include high latency and low throughput. End users are likely to report extended response times or failures caused by services timing out, due to increased contention for I/O resources. Process Monitoring Perform process monitoring of the production system to identify operations with poor response times. Perform load testing of each operation identified in the previous step. During the load tests, gather telemetry data about the data access requests made by each operation. Gather detailed statistics for each request sent to a data store. Profile the application in the test environment to detect possible, I/O bottlenecks Perform process monitoring of the production system to identify operations with poor response times. Perform load testing of each operation identified in the previous step. During the load tests, gather telemetry data about the data access requests made by each operation. Gather detailed statistics for each request sent to a data store. Profile the application in the test environment to detect possible, I/O bottlenecks Look for any of these symptoms: large number of small I/O requests made to the same file. large number of small network requests made by an application instance to the same service. large number of small requests made by an application instance to the same data store. Applications and services becoming I/O bound. large number of small I/O requests made to the same file. large number of small network requests made by an application instance to the same service. large number of small requests made by an application instance to the same data store. Applications and services becoming I/O bound. Monitor the Application Using App Dynamics Using an Application Performance Monitoring (APM) package such as App Dynamics to capture and analyze key metrics that can identify chatty I/O. The key performance metrics will vary depending on the I/O workload. For example, database, file or API. Summary Chatty I/O is an Anti pattern that can occur within several parts of an application or service including the database, file operations and inter-process communications with APIs. The cumulative effect of a large number of I/O requests can have a significant impact on performance and responsiveness. Several monitoring techniques can be used to find the root cause of the chatty component. Software designs should consider the Service Granularity Principle. That is, scoping data retrieval to match the Use Case so the appropriate amount of data is retrieved in each request. Thanks & Happy reading!

Chatty I/O Is Killing Your App's Performance Without You Even Realizing It

This is Part 8 of a 11-part series based on the research paper “Reinforcement Learning In Agent-based Market Simulation: Unveiling Realistic Stylized Facts And Behavior”. Use the table of links below to navigate to the next part. Table of Links Part 1: Abstract & Introduction Part 2: Important Concepts Part 3: System Description Part 4: Agents & Simulation Details Part 5: Experiment Design Part 6: Continual Learning Part 7: Experiment Results Part 8: Market and Agent Responsiveness to External Events Part 9: Conclusion & References Part 10: Additional Simulation Results Part 11: Simulation Configuration 5.2 Market and Agent Responsiveness to External Events In the first experiment, we introduce a rule-based agent which performs a sequence of “flash sales”. During a flash sale event, this agent continuously sends sell orders (300 lots each) every second for a duration of 5 seconds. Then, the agent remains inactive for 400 seconds to allow the market to recover. The goal is to study how the market moves during and after the flash sale events and how the agents’ behavior changes after training with these events. Figure 7 depicts the price movement after the start of flash sale events. To create these plots we use the average price of multiple simulations normalized by the price at which flash sale started. The untrained group shows a significant and permanent price drop in contrast to the testing and continual training groups. In the testing group, prices recover to pre-flash-sale levels. However, the continual training group shows more realistic trajectories, with prices dropping and recovering to levels lower than the pre-sale prices. This price pattern aligns with descriptions of permanent and temporary price impact in [24]. We are also interested in how RL agents’ policy functions are affected when trained with flash sale events. Recall the action symmetric tweak and asymmetric tweak parameters control the prices of the limit orders sent by MM’s (see (1) and illustration in Figure 1). Intuitively, a larger symmetric tweak makes the agent place orders away from the best bid and ask (more conservatively). A positive asymmetric tweak indicates that the MM expects future higher prices than the current one, while a negative value indicates the MM expects the market to go down. When a flash sale event happens, market sell orders take liquidity from the bid side of the LOB and this results in an imbalanced LOB. In contrast, a balanced LOB means the volumes on the buy and sell sides are comparable. To determine the MM agents’ reaction we collect the states with balanced and imbalanced LOBs, and feed these states to the different agents’ policy functions. Figure 8 shows the action symmetric tweak values in the top row and the asymmetric tweak in the bottom row. We can see that when the order book is balanced the three groups output a similar distribution of actions (Figure 8b and 8d). However, when fed imbalanced LOBs (Figures 8a and 8c), the agents in the continual training group output higher symmetric price tweaks and lower asymmetric price tweaks, compared with the agents in the two other groups. Thus when a flash sale event is detected, the MM agents in the continual learning group tend to place orders deeper into the LOB and move the expected price equilibrium downward. This behavior aligns with the findings in literature [25, 26, 27]. These results show that the agents can adapt to different market conditions through continual learning. In this experiment, we only use continual learning Group A MM agents, as they need to adapt to changing market conditions. The LT agents are also from Group A but their reward function is changed through the evolution of the target buy/sell parameters. Specifically, Figure 9 shows the price process resulting from the activity of these informed LT agents. The four phases separated by dashed red lines are: Sell (0.3/0.4), Buy (0.4/0.35), Balanced buy and sell (0.4/0.4), and last Buy (0.4/0.3), the numbers in parenthesis indicate (buy fraction/sell fraction). We expect to see the price movement aligning with the target buy/sell parameters. Additionally, similar to the previous study we collect states in the first and the last phases (i.e., steps 0-10,000 when the price goes down and steps 30,000-36,000 when the price goes up). We feed these states to the MM’s policy function at the beginning of the day (Before) and at the end of the trading day (After). Figure 10 shows the distributions of the outputs in action symmetric and asymmetric tweak. In both scenarios when the price is going down and when the price is going up, the action symmetric tweak (top row) gets larger, thus the MM agent becomes more conservative and tends to enlarge the spread. This aligns with the findings in the previous experiment. We can also see that the distribution of price asymmetric tweak becomes negative when prices are going down and becomes positive when prices are going up. This means the learned agents change their expectations of future prices along with the observed market direction. In contrast, the asymmetric tweak average is close to zero for agents that are not continuously trained. This helps explain why the price tends to come back to pre-flash-sale values for the agents in Group B in Figure 7. Authors:
(1) Zhiyuan Yao, Stevens Institute of Technology, Hoboken, New Jersey, USA (zyao9@stevens.edu);
(2) Zheng Li, Stevens Institute of Technology, Hoboken, New Jersey, USA (zli149@stevens.edu);
(3) Matthew Thomas, Stevens Institute of Technology, Hoboken, New Jersey, USA (mthomas3@stevens.edu);
(4) Ionut Florescu, Stevens Institute of Technology, Hoboken, New Jersey, USA (ifloresc@stevens.edu). This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. This is Part 8 of a 11-part series based on the research paper “Reinforcement Learning In Agent-based Market Simulation: Unveiling Realistic Stylized Facts And Behavior”. Use the table of links below to navigate to the next part. This is Part 8 of a 11-part series based on the research paper “ Reinforcement Learning In Agent-based Market Simulation: Unveiling Realistic Stylized Facts And Behavior ”. Use the table of links below to navigate to the next part. Part 8 Reinforcement Learning In Agent-based Market Simulation: Unveiling Realistic Stylized Facts And Behavior Table of Links Part 1: Abstract & Introduction Part 1: Abstract & Introduction Part 2: Important Concepts Part 2: Important Concepts Part 3: System Description Part 3: System Description Part 4: Agents & Simulation Details Part 4: Agents & Simulation Details Part 5: Experiment Design Part 5: Experiment Design Part 6: Continual Learning Part 6: Continual Learning Part 7: Experiment Results Part 7: Experiment Results Part 8: Market and Agent Responsiveness to External Events Part 8: Market and Agent Responsiveness to External Events Part 9: Conclusion & References Part 9: Conclusion & References Part 10: Additional Simulation Results Part 10: Additional Simulation Results Part 11: Simulation Configuration Part 11: Simulation Configuration 5.2 Market and Agent Responsiveness to External Events In the first experiment, we introduce a rule-based agent which performs a sequence of “flash sales”. During a flash sale event, this agent continuously sends sell orders (300 lots each) every second for a duration of 5 seconds. Then, the agent remains inactive for 400 seconds to allow the market to recover. The goal is to study how the market moves during and after the flash sale events and how the agents’ behavior changes after training with these events. Figure 7 depicts the price movement after the start of flash sale events. To create these plots we use the average price of multiple simulations normalized by the price at which flash sale started. The untrained group shows a significant and permanent price drop in contrast to the testing and continual training groups. In the testing group, prices recover to pre-flash-sale levels. However, the continual training group shows more realistic trajectories, with prices dropping and recovering to levels lower than the pre-sale prices. This price pattern aligns with descriptions of permanent and temporary price impact in [24]. We are also interested in how RL agents’ policy functions are affected when trained with flash sale events. Recall the action symmetric tweak and asymmetric tweak parameters control the prices of the limit orders sent by MM’s (see (1) and illustration in Figure 1). Intuitively, a larger symmetric tweak makes the agent place orders away from the best bid and ask (more conservatively). A positive asymmetric tweak indicates that the MM expects future higher prices than the current one, while a negative value indicates the MM expects the market to go down. When a flash sale event happens, market sell orders take liquidity from the bid side of the LOB and this results in an imbalanced LOB. In contrast, a balanced LOB means the volumes on the buy and sell sides are comparable. To determine the MM agents’ reaction we collect the states with balanced and imbalanced LOBs, and feed these states to the different agents’ policy functions. Figure 8 shows the action symmetric tweak values in the top row and the asymmetric tweak in the bottom row. We can see that when the order book is balanced the three groups output a similar distribution of actions (Figure 8b and 8d). However, when fed imbalanced LOBs (Figures 8a and 8c), the agents in the continual training group output higher symmetric price tweaks and lower asymmetric price tweaks, compared with the agents in the two other groups. Thus when a flash sale event is detected, the MM agents in the continual learning group tend to place orders deeper into the LOB and move the expected price equilibrium downward. This behavior aligns with the findings in literature [25, 26, 27]. These results show that the agents can adapt to different market conditions through continual learning. In this experiment, we only use continual learning Group A MM agents, as they need to adapt to changing market conditions. The LT agents are also from Group A but their reward function is changed through the evolution of the target buy/sell parameters. Specifically, Figure 9 shows the price process resulting from the activity of these informed LT agents. The four phases separated by dashed red lines are: Sell (0.3/0.4), Buy (0.4/0.35), Balanced buy and sell (0.4/0.4), and last Buy (0.4/0.3), the numbers in parenthesis indicate (buy fraction/sell fraction). We expect to see the price movement aligning with the target buy/sell parameters. Additionally, similar to the previous study we collect states in the first and the last phases (i.e., steps 0-10,000 when the price goes down and steps 30,000-36,000 when the price goes up). We feed these states to the MM’s policy function at the beginning of the day (Before) and at the end of the trading day (After). Figure 10 shows the distributions of the outputs in action symmetric and asymmetric tweak. In both scenarios when the price is going down and when the price is going up, the action symmetric tweak (top row) gets larger, thus the MM agent becomes more conservative and tends to enlarge the spread. This aligns with the findings in the previous experiment. We can also see that the distribution of price asymmetric tweak becomes negative when prices are going down and becomes positive when prices are going up. This means the learned agents change their expectations of future prices along with the observed market direction. In contrast, the asymmetric tweak average is close to zero for agents that are not continuously trained. This helps explain why the price tends to come back to pre-flash-sale values for the agents in Group B in Figure 7. Authors: (1) Zhiyuan Yao, Stevens Institute of Technology, Hoboken, New Jersey, USA (zyao9@stevens.edu); (2) Zheng Li, Stevens Institute of Technology, Hoboken, New Jersey, USA (zli149@stevens.edu); (3) Matthew Thomas, Stevens Institute of Technology, Hoboken, New Jersey, USA (mthomas3@stevens.edu); (4) Ionut Florescu, Stevens Institute of Technology, Hoboken, New Jersey, USA (ifloresc@stevens.edu). Authors: Authors: (1) Zhiyuan Yao, Stevens Institute of Technology, Hoboken, New Jersey, USA (zyao9@stevens.edu); (2) Zheng Li, Stevens Institute of Technology, Hoboken, New Jersey, USA (zli149@stevens.edu); (3) Matthew Thomas, Stevens Institute of Technology, Hoboken, New Jersey, USA (mthomas3@stevens.edu); (4) Ionut Florescu, Stevens Institute of Technology, Hoboken, New Jersey, USA (ifloresc@stevens.edu). This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. available on arxiv

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