Big data frameworks usually provide a large number of performance-related parameters. Online auto-tuning these parameters based on deep reinforcement learning (DRL) to achieve a better performance has shown their advantages over search-based and machine learning-based approaches. Unfortunately, the time cost during the online tuning phase of conventional DRL-based methods is still heavy, especially for Big Data applications. Therefore, in this paper, we propose DeepCAT $^+$ , a low-cost and transferrable deep reinforcement learning-based approach to achieve online configuration auto-tuning for Big Data frameworks. To reduce the total online tuning cost and increase the adaptability: 1) DeepCAT $^+$ utilizes the TD3 algorithm instead of DDPG to alleviate value overestimation; 2) DeepCAT $^+$ modifies the conventional experience replay to fully utilize the rare but valuable transitions via a novel reward-driven prioritized experience replay mechanism; 3) DeepCAT $^+$ designs a Twin-Q Optimizer to estimate the execution time of each action without the costly configuration evaluation and optimize the sub-optimal ones to achieve a low-cost exploration-exploitation tradeoff; 4) Furthermore, DeepCAT $^+$ also implements an Online Continual Learner module based on Progressive Neural Networks to transfer knowledge from historical tuning experiences. Experimental results based on a lab Spark cluster with HiBench benchmark applications show that DeepCAT $^+$ is able to speed up the best execution time by a factor of 1.49×, 1.63× and 1.65× on average respectively over the baselines, while consuming up to 50.08%, 53.39% and 70.79% less total tuning time. In addition, DeepCAT $^+$ also has a strong adaptability to the time-varying environment of Big Data frameworks.
{"title":"DeepCAT+: A Low-Cost and Transferrable Online Configuration Auto-Tuning Approach for Big Data Frameworks","authors":"Hui Dou;Yilun Wang;Yiwen Zhang;Pengfei Chen;Zibin Zheng","doi":"10.1109/TPDS.2024.3459889","DOIUrl":"10.1109/TPDS.2024.3459889","url":null,"abstract":"Big data frameworks usually provide a large number of performance-related parameters. Online auto-tuning these parameters based on deep reinforcement learning (DRL) to achieve a better performance has shown their advantages over search-based and machine learning-based approaches. Unfortunately, the time cost during the online tuning phase of conventional DRL-based methods is still heavy, especially for Big Data applications. Therefore, in this paper, we propose DeepCAT\u0000<inline-formula><tex-math>$^+$</tex-math></inline-formula>\u0000, a low-cost and transferrable deep reinforcement learning-based approach to achieve online configuration auto-tuning for Big Data frameworks. To reduce the total online tuning cost and increase the adaptability: 1) DeepCAT\u0000<inline-formula><tex-math>$^+$</tex-math></inline-formula>\u0000 utilizes the TD3 algorithm instead of DDPG to alleviate value overestimation; 2) DeepCAT\u0000<inline-formula><tex-math>$^+$</tex-math></inline-formula>\u0000 modifies the conventional experience replay to fully utilize the rare but valuable transitions via a novel reward-driven prioritized experience replay mechanism; 3) DeepCAT\u0000<inline-formula><tex-math>$^+$</tex-math></inline-formula>\u0000 designs a Twin-Q Optimizer to estimate the execution time of each action without the costly configuration evaluation and optimize the sub-optimal ones to achieve a low-cost exploration-exploitation tradeoff; 4) Furthermore, DeepCAT\u0000<inline-formula><tex-math>$^+$</tex-math></inline-formula>\u0000 also implements an Online Continual Learner module based on Progressive Neural Networks to transfer knowledge from historical tuning experiences. Experimental results based on a lab Spark cluster with HiBench benchmark applications show that DeepCAT\u0000<inline-formula><tex-math>$^+$</tex-math></inline-formula>\u0000 is able to speed up the best execution time by a factor of 1.49×, 1.63× and 1.65× on average respectively over the baselines, while consuming up to 50.08%, 53.39% and 70.79% less total tuning time. In addition, DeepCAT\u0000<inline-formula><tex-math>$^+$</tex-math></inline-formula>\u0000 also has a strong adaptability to the time-varying environment of Big Data frameworks.","PeriodicalId":13257,"journal":{"name":"IEEE Transactions on Parallel and Distributed Systems","volume":"35 11","pages":"2114-2131"},"PeriodicalIF":5.6,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142194351","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-09DOI: 10.1109/TPDS.2024.3456567
Biao Hou;Song Yang;Fan Li;Liehuang Zhu;Lei Jiao;Xu Chen;Xiaoming Fu
Nowadays, the emerging short video streaming applications have gained substantial attention. With the rapidly burgeoning demand for short video streaming services, maximizing their Quality of Experience (QoE) is an onerous challenge. Current video preloading algorithms cannot determine video preloading sequence decisions appropriately due to the impact of users’ swipes and bandwidth fluctuations. As a result, it is still ambiguous how to improve the overall QoE while mitigating bandwidth wastage to optimize short video streaming services. In this article, we devise Gamora, a buffer-aware short video streaming system to provide a high QoE of users. In Gamora, we first propose an unordered preloading algorithm that utilizes a Deep Reinforcement Learning (DRL) algorithm to make video preloading decisions. Then, we further devise an Asymmetric Imitation Learning (AIL) algorithm to guide the DRL-based preloading algorithm, which enables the agent to learn from expert demonstrations for fast convergence. Finally, we implement our proposed short video streaming system prototype and evaluate the performance of Gamora on various real-world network datasets. Our results demonstrate that Gamora significantly achieves QoE improvement by 28.7%–51.4% compared to state-of-the-art algorithms, while mitigating bandwidth wastage by 40.7%–83.2% without sacrificing video quality.
{"title":"Gamora: Learning-Based Buffer-Aware Preloading for Adaptive Short Video Streaming","authors":"Biao Hou;Song Yang;Fan Li;Liehuang Zhu;Lei Jiao;Xu Chen;Xiaoming Fu","doi":"10.1109/TPDS.2024.3456567","DOIUrl":"10.1109/TPDS.2024.3456567","url":null,"abstract":"Nowadays, the emerging short video streaming applications have gained substantial attention. With the rapidly burgeoning demand for short video streaming services, maximizing their Quality of Experience (QoE) is an onerous challenge. Current video preloading algorithms cannot determine video preloading sequence decisions appropriately due to the impact of users’ swipes and bandwidth fluctuations. As a result, it is still ambiguous how to improve the overall QoE while mitigating bandwidth wastage to optimize short video streaming services. In this article, we devise Gamora, a buffer-aware short video streaming system to provide a high QoE of users. In Gamora, we first propose an unordered preloading algorithm that utilizes a Deep Reinforcement Learning (DRL) algorithm to make video preloading decisions. Then, we further devise an Asymmetric Imitation Learning (AIL) algorithm to guide the DRL-based preloading algorithm, which enables the agent to learn from expert demonstrations for fast convergence. Finally, we implement our proposed short video streaming system prototype and evaluate the performance of Gamora on various real-world network datasets. Our results demonstrate that Gamora significantly achieves QoE improvement by 28.7%–51.4% compared to state-of-the-art algorithms, while mitigating bandwidth wastage by 40.7%–83.2% without sacrificing video quality.","PeriodicalId":13257,"journal":{"name":"IEEE Transactions on Parallel and Distributed Systems","volume":"35 11","pages":"2132-2146"},"PeriodicalIF":5.6,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142194355","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-06DOI: 10.1109/TPDS.2024.3455762
Renwen Ma;Kai Hwang;Mo Li;Yiming Miao
This paper proposes a new global model aggregation method based on using zero-knowledge federated learning (ZKFL). The purpose is to secure horizontal or P2P federated machine learning systems with shorter aggregation times, higher model accuracy, and lower system costs. We use a model parameter-sharing Chord overlay network among all client hosts. The overlay guarantees a trusted sharing of zero-knowledge proofs for aggregation integrity, even under malicious Byzantine attacks. We tested over popular datasets, Fashion-MNIST and CIFAR10, to prove the new system protection concept. Our benchmark experiments validate the claimed advantages of the ZKFL scheme in all objective functions. Our aggregation method can be applied to secure both rank-based and similarity-based aggregation schemes. For a large system with over 200 clients, our system takes only 3 seconds to yield high-precision global machine models under the ALIE attacks with the Fashion-MNIST dataset. We have achieved up to 85% model accuracy, compared to only 3% $sim$ 45% accuracy observed with federated schemes without protection. Moreover, our method demands a low memory overhead for handling zero-knowledge proofs as the system scales greatly to a larger number of client nodes.
{"title":"Trusted Model Aggregation With Zero-Knowledge Proofs in Federated Learning","authors":"Renwen Ma;Kai Hwang;Mo Li;Yiming Miao","doi":"10.1109/TPDS.2024.3455762","DOIUrl":"10.1109/TPDS.2024.3455762","url":null,"abstract":"This paper proposes a new global model aggregation method based on using zero-knowledge federated learning (ZKFL). The purpose is to secure horizontal or P2P federated machine learning systems with shorter aggregation times, higher model accuracy, and lower system costs. We use a model parameter-sharing Chord overlay network among all client hosts. The overlay guarantees a trusted sharing of zero-knowledge proofs for aggregation integrity, even under malicious Byzantine attacks. We tested over popular datasets, Fashion-MNIST and CIFAR10, to prove the new system protection concept. Our benchmark experiments validate the claimed advantages of the ZKFL scheme in all objective functions. Our aggregation method can be applied to secure both rank-based and similarity-based aggregation schemes. For a large system with over 200 clients, our system takes only 3 seconds to yield high-precision global machine models under the ALIE attacks with the Fashion-MNIST dataset. We have achieved up to 85% model accuracy, compared to only 3%\u0000<inline-formula><tex-math>$sim$</tex-math></inline-formula>\u000045% accuracy observed with federated schemes without protection. Moreover, our method demands a low memory overhead for handling zero-knowledge proofs as the system scales greatly to a larger number of client nodes.","PeriodicalId":13257,"journal":{"name":"IEEE Transactions on Parallel and Distributed Systems","volume":"35 11","pages":"2284-2296"},"PeriodicalIF":5.6,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142194354","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-04DOI: 10.1109/TPDS.2024.3454203
Ping Luo;Jieren Cheng;N. Xiong;Zhenhao Liu;Jie Wu
Federated Learning (FL) is a distributed machine learning framework in parallel and distributed systems. However, the systems’ Non-Independent and Identically Distributed (Non-IID) data negatively affect the communication efficiency, since clients with different datasets may cause significant gaps to the local gradients in each communication round. In this article, we propose a Federated Vectorized Averaging (FedVeca) method to optimize the FL communication system on Non-IID data. Specifically, we set a novel objective for the global model which is related to the local gradients. The local gradient is defined as a bi-directional vector with step size and direction, where the step size is the number of local updates and the direction is divided into positive and negative according to our definition. In FedVeca, the direction is influenced by the step size, thus we average the bi-directional vectors to reduce the effect of different step sizes. Then, we theoretically analyze the relationship between the step sizes and the global objective, and obtain upper bounds on the step sizes per communication round. Based on the upper bounds, we design an algorithm for the server and the client to adaptively adjusts the step sizes that make the objective close to the optimum. Finally, we conduct experiments on different datasets, models and scenarios by building a prototype system, and the experimental results demonstrate the effectiveness and efficiency of the FedVeca method.
{"title":"FedVeca: Federated Vectorized Averaging on Non-IID Data With Adaptive Bi-Directional Global Objective","authors":"Ping Luo;Jieren Cheng;N. Xiong;Zhenhao Liu;Jie Wu","doi":"10.1109/TPDS.2024.3454203","DOIUrl":"10.1109/TPDS.2024.3454203","url":null,"abstract":"Federated Learning (FL) is a distributed machine learning framework in parallel and distributed systems. However, the systems’ Non-Independent and Identically Distributed (Non-IID) data negatively affect the communication efficiency, since clients with different datasets may cause significant gaps to the local gradients in each communication round. In this article, we propose a Federated Vectorized Averaging (FedVeca) method to optimize the FL communication system on Non-IID data. Specifically, we set a novel objective for the global model which is related to the local gradients. The local gradient is defined as a bi-directional vector with step size and direction, where the step size is the number of local updates and the direction is divided into positive and negative according to our definition. In FedVeca, the direction is influenced by the step size, thus we average the bi-directional vectors to reduce the effect of different step sizes. Then, we theoretically analyze the relationship between the step sizes and the global objective, and obtain upper bounds on the step sizes per communication round. Based on the upper bounds, we design an algorithm for the server and the client to adaptively adjusts the step sizes that make the objective close to the optimum. Finally, we conduct experiments on different datasets, models and scenarios by building a prototype system, and the experimental results demonstrate the effectiveness and efficiency of the FedVeca method.","PeriodicalId":13257,"journal":{"name":"IEEE Transactions on Parallel and Distributed Systems","volume":"35 11","pages":"2102-2113"},"PeriodicalIF":5.6,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142194356","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Digital signatures are fundamental building blocks in various protocols to provide integrity and authenticity. The development of the quantum computing has raised concerns about the security guarantees afforded by classical signature schemes. CRYSTALS-Dilithium is an efficient post-quantum digital signature scheme based on lattice cryptography and has been selected as the primary algorithm for standardization by the National Institute of Standards and Technology. In this work, we present a high-throughput GPU implementation of Dilithium. For individual operations, we employ a range of computational and memory optimizations to overcome sequential constraints, reduce memory usage and IO latency, address bank conflicts, and mitigate pipeline stalls. This results in high and balanced compute throughput and memory throughput for each operation. In terms of concurrent task processing, we leverage task-level batching to fully utilize parallelism and implement a memory pool mechanism for rapid memory access. We propose a dynamic task scheduling mechanism to improve multiprocessor occupancy and significantly reduce execution time. Furthermore, we apply asynchronous computing and launch multiple streams to hide data transfer latencies and maximize the computing capabilities of both CPU and GPU. Across all three security levels, our GPU implementation achieves over 160× speedups for signing and over 80× speedups for verification on both commercial and server-grade GPUs. This achieves microsecond-level amortized execution times for each task, offering a high-throughput and quantum-resistant solution suitable for a wide array of applications in real systems.
{"title":"High-Throughput GPU Implementation of Dilithium Post-Quantum Digital Signature","authors":"Shiyu Shen;Hao Yang;Wangchen Dai;Hong Zhang;Zhe Liu;Yunlei Zhao","doi":"10.1109/TPDS.2024.3453289","DOIUrl":"10.1109/TPDS.2024.3453289","url":null,"abstract":"Digital signatures are fundamental building blocks in various protocols to provide integrity and authenticity. The development of the quantum computing has raised concerns about the security guarantees afforded by classical signature schemes. CRYSTALS-Dilithium is an efficient post-quantum digital signature scheme based on lattice cryptography and has been selected as the primary algorithm for standardization by the National Institute of Standards and Technology. In this work, we present a high-throughput GPU implementation of Dilithium. For individual operations, we employ a range of computational and memory optimizations to overcome sequential constraints, reduce memory usage and IO latency, address bank conflicts, and mitigate pipeline stalls. This results in high and balanced compute throughput and memory throughput for each operation. In terms of concurrent task processing, we leverage task-level batching to fully utilize parallelism and implement a memory pool mechanism for rapid memory access. We propose a dynamic task scheduling mechanism to improve multiprocessor occupancy and significantly reduce execution time. Furthermore, we apply asynchronous computing and launch multiple streams to hide data transfer latencies and maximize the computing capabilities of both CPU and GPU. Across all three security levels, our GPU implementation achieves over 160× speedups for signing and over 80× speedups for verification on both commercial and server-grade GPUs. This achieves microsecond-level amortized execution times for each task, offering a high-throughput and quantum-resistant solution suitable for a wide array of applications in real systems.","PeriodicalId":13257,"journal":{"name":"IEEE Transactions on Parallel and Distributed Systems","volume":"35 11","pages":"1964-1976"},"PeriodicalIF":5.6,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142194358","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-03DOI: 10.1109/TPDS.2024.3453310
Di Mou;Bo Wang;Dajiang Liu
Stochastic Computing (SC) offers a promising computing paradigm for low-power and cost-effective applications, with the added advantage of high error tolerance. In parallel, Coarse-Grained Reconfigurable Arrays (CGRA) prove to be a highly promising platform for domain-specific applications due to their combination of energy efficiency and flexibility. Intuitively, introducing SC to CGRA would significantly reinforce the strengths of both paradigms. However, existing SC-based architectures often encounter inherent computation errors, while the stochastic number generators employed in SC result in exponentially growing latency, which is deemed unacceptable in CGRA. In this work, we propose an SC-based CGRA by replacing the exact multiplication in traditional CGRA with an SC-based multiplication. To improve the accuracy of SC and shorten the latency of Stochastic Number Generators (SNG), we introduce the leading zero shifting and comparator truncation, while keeping the length of bitstream fixed. In addition, due to the flexible interconnections among PEs, we propose a quality scaling strategy that combines neighbor PEs to achieve high-accuracy operations without switching costs like power-gating. Compared to the state-of-the-art approximate computing design of CGRA, our proposed CGRA can averagely achieve a 65.3% reduction in output error while having a 21.2% reduction in energy consumption and a noteworthy 28.37% area savings.
{"title":"SC-CGRA: An Energy-Efficient CGRA Using Stochastic Computing","authors":"Di Mou;Bo Wang;Dajiang Liu","doi":"10.1109/TPDS.2024.3453310","DOIUrl":"10.1109/TPDS.2024.3453310","url":null,"abstract":"Stochastic Computing (SC) offers a promising computing paradigm for low-power and cost-effective applications, with the added advantage of high error tolerance. In parallel, Coarse-Grained Reconfigurable Arrays (CGRA) prove to be a highly promising platform for domain-specific applications due to their combination of energy efficiency and flexibility. Intuitively, introducing SC to CGRA would significantly reinforce the strengths of both paradigms. However, existing SC-based architectures often encounter inherent computation errors, while the stochastic number generators employed in SC result in exponentially growing latency, which is deemed unacceptable in CGRA. In this work, we propose an SC-based CGRA by replacing the exact multiplication in traditional CGRA with an SC-based multiplication. To improve the accuracy of SC and shorten the latency of Stochastic Number Generators (SNG), we introduce the leading zero shifting and comparator truncation, while keeping the length of bitstream fixed. In addition, due to the flexible interconnections among PEs, we propose a quality scaling strategy that combines neighbor PEs to achieve high-accuracy operations without switching costs like power-gating. Compared to the state-of-the-art approximate computing design of CGRA, our proposed CGRA can averagely achieve a 65.3% reduction in output error while having a 21.2% reduction in energy consumption and a noteworthy 28.37% area savings.","PeriodicalId":13257,"journal":{"name":"IEEE Transactions on Parallel and Distributed Systems","volume":"35 11","pages":"2023-2038"},"PeriodicalIF":5.6,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142194357","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Iterative graph processing is widely used as a significant paradigm for large-scale data analysis. In many global businesses of multinational enterprises, graph-structure data is usually geographically distributed in different regions to support low-latency services. Geo-distributed graph processing suffers from the Wide Area Networks (WANs) with scarce and heterogeneous bandwidth, thus essentially differs from traditional distributed graph processing. In this paper, we propose RAGraph, a Region-Aware framework for geo-distributed graph processing . At the core of RAGraph, we design a region-aware graph processing framework that allows advancing inefficient global updates locally and enables sensible coordination-free message interactions and flexible replaceable communication module. In terms of graph data preprocessing, RAGraph introduces a contribution-driven edge migration algorithm to effectively utilize network resources. RAGraph also contains an adaptive hierarchical message interaction engine to switch interaction modes adaptively based on network heterogeneity and fluctuation, and a discrepancy-aware message filtering strategy to filter important messages. Experimental results show that RAGraph can achieve an average speedup of 9.7× (up to 98×) and an average WAN cost reduction of 78.5 $%$ (up to 97.3 $%$ ) compared with state-of-the-art systems.
{"title":"Towards Efficient Graph Processing in Geo-Distributed Data Centers","authors":"Feng Yao;Qian Tao;Shengyuan Lin;Yanfeng Zhang;Wenyuan Yu;Shufeng Gong;Qiange Wang;Ge Yu;Jingren Zhou","doi":"10.1109/TPDS.2024.3453872","DOIUrl":"10.1109/TPDS.2024.3453872","url":null,"abstract":"Iterative graph processing is widely used as a significant paradigm for large-scale data analysis. In many global businesses of multinational enterprises, graph-structure data is usually geographically distributed in different regions to support low-latency services. Geo-distributed graph processing suffers from the Wide Area Networks (WANs) with scarce and heterogeneous bandwidth, thus essentially differs from traditional distributed graph processing. In this paper, we propose RAGraph, a \u0000<i><u>R</u>egion-<u>A</u>ware framework for geo-distributed <u>graph</u> processing</i>\u0000. At the core of RAGraph, we design a region-aware graph processing framework that allows advancing inefficient global updates locally and enables sensible coordination-free message interactions and flexible replaceable communication module. In terms of graph data preprocessing, RAGraph introduces a contribution-driven edge migration algorithm to effectively utilize network resources. RAGraph also contains an adaptive hierarchical message interaction engine to switch interaction modes adaptively based on network heterogeneity and fluctuation, and a discrepancy-aware message filtering strategy to filter important messages. Experimental results show that RAGraph can achieve an average speedup of 9.7× (up to 98×) and an average WAN cost reduction of 78.5\u0000<inline-formula><tex-math>$%$</tex-math></inline-formula>\u0000 (up to 97.3\u0000<inline-formula><tex-math>$%$</tex-math></inline-formula>\u0000) compared with state-of-the-art systems.","PeriodicalId":13257,"journal":{"name":"IEEE Transactions on Parallel and Distributed Systems","volume":"35 11","pages":"2147-2160"},"PeriodicalIF":5.6,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142194363","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-03DOI: 10.1109/TPDS.2024.3454071
Zhaojie Wen;Qiong Chen;Quanfeng Deng;Yipei Niu;Zhen Song;Fangming Liu
Serverless computing provides developers with a maintenance-free approach to resource usage, but it also transfers resource management responsibility to the cloud platform. However, the fine granularity of serverless function resources can lead to performance bottlenecks and resource fragmentation on nodes when creating many function containers. This poses challenges in effectively scaling function resources and optimizing node resource allocation, hindering overall agility. To address these challenges, we have introduced ComboFunc, an innovative resource scaling system for serverless platforms. ComboFunc associates function with heterogeneous containers of varying specifications and optimizes their resource combination and placement. This approach not only selects appropriate nodes for container creation, but also leverages the new feature of Kubernetes In-place Pod Vertical Scaling to enhance resource scaling agility and efficiency. By allowing a single function to correspond to heterogeneous containers with varying resource specifications and providing the ability to modify the resource specifications of existing containers in place, ComboFunc effectively utilizes fragmented resources on nodes. This, in turn, enhances the overall resource utilization of the entire cluster and improves scaling agility. We also model the problem of combining and placing heterogeneous containers as an NP-hard problem and design a heuristic solution based on a greedy algorithm that solves it in polynomial time. We implemented a prototype of ComboFunc on the Kubernetes platform and conducted experiments using real traces on a local cluster. The results demonstrate that, compared to existing strategies, ComboFunc achieves up to 3.01 × faster function resource scaling and reduces resource costs by up to 42.6%.
{"title":"ComboFunc: Joint Resource Combination and Container Placement for Serverless Function Scaling With Heterogeneous Container","authors":"Zhaojie Wen;Qiong Chen;Quanfeng Deng;Yipei Niu;Zhen Song;Fangming Liu","doi":"10.1109/TPDS.2024.3454071","DOIUrl":"10.1109/TPDS.2024.3454071","url":null,"abstract":"Serverless computing provides developers with a maintenance-free approach to resource usage, but it also transfers resource management responsibility to the cloud platform. However, the fine granularity of serverless function resources can lead to performance bottlenecks and resource fragmentation on nodes when creating many function containers. This poses challenges in effectively scaling function resources and optimizing node resource allocation, hindering overall agility. To address these challenges, we have introduced ComboFunc, an innovative resource scaling system for serverless platforms. ComboFunc associates function with heterogeneous containers of varying specifications and optimizes their resource combination and placement. This approach not only selects appropriate nodes for container creation, but also leverages the new feature of Kubernetes In-place Pod Vertical Scaling to enhance resource scaling agility and efficiency. By allowing a single function to correspond to heterogeneous containers with varying resource specifications and providing the ability to modify the resource specifications of existing containers in place, ComboFunc effectively utilizes fragmented resources on nodes. This, in turn, enhances the overall resource utilization of the entire cluster and improves scaling agility. We also model the problem of combining and placing heterogeneous containers as an NP-hard problem and design a heuristic solution based on a greedy algorithm that solves it in polynomial time. We implemented a prototype of ComboFunc on the Kubernetes platform and conducted experiments using real traces on a local cluster. The results demonstrate that, compared to existing strategies, ComboFunc achieves up to 3.01 × faster function resource scaling and reduces resource costs by up to 42.6%.","PeriodicalId":13257,"journal":{"name":"IEEE Transactions on Parallel and Distributed Systems","volume":"35 11","pages":"1989-2005"},"PeriodicalIF":5.6,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142194387","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In distributed IoT data systems, full-size data collection is impractical due to the energy constraints and large system scales. Our previous work has investigated the advantages of integrating matrix sampling and inference for compact distributed IoT data collection, to minimize the data collection cost while guaranteeing the data benefits. This paper further advances the technology by boosting fast and accurate inference for those distributed IoT data systems that are sensitive to computation time, training stability, and inference accuracy. Particularly, we propose CODE$^{+}$+ , i.e., C ompact Distributed I O T D ata Coll E ction Plus, which features a cluster-based sampling module and a Convolutional Neural Network (CNN)-Transformer Autoencoders-based inference module, to reduce cost and guarantee the data benefits. The sampling component employs a cluster-based matrix sampling approach, in which data clustering is first conducted and then a two-step sampling is performed in accordance with the number of clusters and clustering errors. The inference component integrates a CNN-Transformer Autoencoders-based matrix inference model to estimate the full-size spatio-temporal data matrix, which consists of a CNN-Transformer encoder that extracts the underlying features from the sampled data matrix and a lightweight decoder that maps the learned latent features back to the original full-size data matrix. We implement CODE$^{+}$+ under three operational large-scale IoT systems and one synthetic Gaussian distribution dataset, and extensive experiments are provided to demonstrate its efficiency and robustness. With a 20% sampling ratio, CODE$^{+}$+ achieves an average data reconstruction accuracy of 94% across four datasets, outperforming our previous version of 87% and state-of-the-art baseline of 71%.
{"title":"CODE$^{+}$+: Fast and Accurate Inference for Compact Distributed IoT Data Collection","authors":"Huali Lu;Feng Lyu;Ju Ren;Huaqing Wu;Conghao Zhou;Zhongyuan Liu;Yaoxue Zhang;Xuemin Shen","doi":"10.1109/TPDS.2024.3453607","DOIUrl":"10.1109/TPDS.2024.3453607","url":null,"abstract":"In distributed IoT data systems, full-size data collection is impractical due to the energy constraints and large system scales. Our previous work has investigated the advantages of integrating matrix sampling and inference for compact distributed IoT data collection, to minimize the data collection cost while guaranteeing the data benefits. This paper further advances the technology by boosting fast and accurate inference for those distributed IoT data systems that are sensitive to computation time, training stability, and inference accuracy. Particularly, we propose \u0000<italic>CODE<inline-formula><tex-math>$^{+}$</tex-math><alternatives><mml:math><mml:msup><mml:mrow/><mml:mo>+</mml:mo></mml:msup></mml:math><inline-graphic></alternatives></inline-formula></i>\u0000, i.e., \u0000<underline>C</u>\u0000ompact Distributed I\u0000<underline>O</u>\u0000T \u0000<underline>D</u>\u0000ata Coll\u0000<underline>E</u>\u0000ction Plus, which features a cluster-based sampling module and a Convolutional Neural Network (CNN)-Transformer Autoencoders-based inference module, to reduce cost and guarantee the data benefits. The sampling component employs a cluster-based matrix sampling approach, in which data clustering is first conducted and then a two-step sampling is performed in accordance with the number of clusters and clustering errors. The inference component integrates a CNN-Transformer Autoencoders-based matrix inference model to estimate the full-size spatio-temporal data matrix, which consists of a CNN-Transformer encoder that extracts the underlying features from the sampled data matrix and a lightweight decoder that maps the learned latent features back to the original full-size data matrix. We implement \u0000<italic>CODE<inline-formula><tex-math>$^{+}$</tex-math><alternatives><mml:math><mml:msup><mml:mrow/><mml:mo>+</mml:mo></mml:msup></mml:math><inline-graphic></alternatives></inline-formula></i>\u0000 under three operational large-scale IoT systems and one synthetic Gaussian distribution dataset, and extensive experiments are provided to demonstrate its efficiency and robustness. With a 20% sampling ratio, \u0000<italic>CODE<inline-formula><tex-math>$^{+}$</tex-math><alternatives><mml:math><mml:msup><mml:mrow/><mml:mo>+</mml:mo></mml:msup></mml:math><inline-graphic></alternatives></inline-formula></i>\u0000 achieves an average data reconstruction accuracy of 94% across four datasets, outperforming our previous version of 87% and state-of-the-art baseline of 71%.","PeriodicalId":13257,"journal":{"name":"IEEE Transactions on Parallel and Distributed Systems","volume":"35 11","pages":"2006-2022"},"PeriodicalIF":5.6,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142194359","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-30DOI: 10.1109/TPDS.2024.3452478
Hua Huang;Edmond Chow
We consider the distributed memory parallel multiplication of a sparse matrix by a dense matrix (SpMM). The dense matrix is often a collection of dense vectors. Standard implementations will multiply the sparse matrix by multiple dense vectors at the same time, to exploit the computational efficiencies therein. But such approaches generally utilize the same sparse matrix partitioning as if multiplying by a single vector. This article explores the design space of parallelizing SpMM and shows that a coarser grain partitioning of the matrix combined with a column-wise partitioning of the block of vectors can often require less communication volume and achieve higher SpMM performance. An algorithm is presented that chooses a process grid geometry for a given number of processes to optimize the performance of parallel SpMM. The algorithm can augment existing graph partitioners by utilizing the additional concurrency available when multiplying by multiple dense vectors to further reduce communication.
{"title":"Exploring the Design Space of Distributed Parallel Sparse Matrix-Multiple Vector Multiplication","authors":"Hua Huang;Edmond Chow","doi":"10.1109/TPDS.2024.3452478","DOIUrl":"10.1109/TPDS.2024.3452478","url":null,"abstract":"We consider the distributed memory parallel multiplication of a sparse matrix by a dense matrix (SpMM). The dense matrix is often a collection of dense vectors. Standard implementations will multiply the sparse matrix by multiple dense vectors at the same time, to exploit the computational efficiencies therein. But such approaches generally utilize the same sparse matrix partitioning as if multiplying by a single vector. This article explores the design space of parallelizing SpMM and shows that a coarser grain partitioning of the matrix combined with a column-wise partitioning of the block of vectors can often require less communication volume and achieve higher SpMM performance. An algorithm is presented that chooses a process grid geometry for a given number of processes to optimize the performance of parallel SpMM. The algorithm can augment existing graph partitioners by utilizing the additional concurrency available when multiplying by multiple dense vectors to further reduce communication.","PeriodicalId":13257,"journal":{"name":"IEEE Transactions on Parallel and Distributed Systems","volume":"35 11","pages":"1977-1988"},"PeriodicalIF":5.6,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142227710","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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