Pub Date : 2024-11-07DOI: 10.1109/TPDS.2024.3493631
Jonatha Anselmi;Josu Doncel
We present a new framework for designing nonpreemptive and job-size oblivious scheduling policies in the multiserver-job queueing model. The main requirement is to identify a static and balanced sub-partition of the server set and ensure that the servers in each set of that sub-partition can only handle jobs of a given class and in a first-come first-served order. A job class is determined by the number of servers to which it has exclusive access during its entire execution and the probability distribution of its service time. This approach aims to reduce delays by preventing small jobs from being blocked by larger ones that arrived first, and it is particularly beneficial when the job size variability intra resp. inter classes is small resp. large. In this setting, we propose a new scheduling policy, Balanced-Splitting. In our main results, we provide a sufficient condition for the stability of Balanced-Splitting and show that the resulting queueing probability, i.e., the probability that an arriving job needs to wait for processing upon arrival, vanishes in both the subcritical (the load is kept fixed to a constant less than one) and critical (the load approaches one from below) many-server limiting regimes. Crucial to our analysis is a connection with the M/GI/ $s$ / $s$ queue and Erlang’s loss formula, which allows our analysis to rely on fundamental results from queueing theory. Numerical simulations show that the proposed policy performs better than several preemptive/nonpreemptive size-aware/oblivious policies in various practical scenarios. This is also confirmed by simulations running on real traces from High Performance Computing (HPC) workloads. The delays induced by Balanced-Splitting are also competitive with those induced by state-of-the-art policies such as First-Fit-SRPT and ServerFilling-SRPT, though our approach has the advantage of not requiring preemption, nor the knowledge of job sizes.
{"title":"Balanced Splitting: A Framework for Achieving Zero-Wait in the Multiserver-Job Model","authors":"Jonatha Anselmi;Josu Doncel","doi":"10.1109/TPDS.2024.3493631","DOIUrl":"https://doi.org/10.1109/TPDS.2024.3493631","url":null,"abstract":"We present a new framework for designing nonpreemptive and job-size oblivious scheduling policies in the multiserver-job queueing model. The main requirement is to identify a \u0000<i>static and balanced sub-partition</i>\u0000 of the server set and ensure that the servers in each set of that sub-partition can only handle jobs of a given \u0000<i>class</i>\u0000 and in a first-come first-served order. A job class is determined by the number of servers to which it has exclusive access during its entire execution and the probability distribution of its service time. This approach aims to reduce delays by preventing small jobs from being blocked by larger ones that arrived first, and it is particularly beneficial when the job size variability intra resp. inter classes is small resp. large. In this setting, we propose a new scheduling policy, Balanced-Splitting. In our main results, we provide a sufficient condition for the stability of Balanced-Splitting and show that the resulting queueing probability, i.e., the probability that an arriving job needs to wait for processing upon arrival, vanishes in both the subcritical (the load is kept fixed to a constant less than one) and critical (the load approaches one from below) many-server limiting regimes. Crucial to our analysis is a connection with the M/GI/\u0000<inline-formula><tex-math>$s$</tex-math></inline-formula>\u0000/\u0000<inline-formula><tex-math>$s$</tex-math></inline-formula>\u0000 queue and Erlang’s loss formula, which allows our analysis to rely on fundamental results from queueing theory. Numerical simulations show that the proposed policy performs better than several preemptive/nonpreemptive size-aware/oblivious policies in various practical scenarios. This is also confirmed by simulations running on real traces from High Performance Computing (HPC) workloads. The delays induced by Balanced-Splitting are also competitive with those induced by state-of-the-art policies such as First-Fit-SRPT and ServerFilling-SRPT, though our approach has the advantage of not requiring preemption, nor the knowledge of job sizes.","PeriodicalId":13257,"journal":{"name":"IEEE Transactions on Parallel and Distributed Systems","volume":"36 1","pages":"43-54"},"PeriodicalIF":5.6,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142672046","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 : 2024-11-07DOI: 10.1109/TPDS.2024.3493953
Ruikun Luo;Qiang He;Feifei Chen;Song Wu;Hai Jin;Yun Yang
With its advantages in ensuring low data retrieval latency and reducing backhaul network traffic, edge computing is becoming a backbone solution for many latency-sensitive applications. An increasingly large number of data is being generated at the edge, stretching the limited capacity of edge storage systems. Improving resource utilization for edge storage systems has become a significant challenge in recent years. Existing solutions attempt to achieve this goal through data placement optimization, data partitioning, data sharing, etc. These approaches overlook the data redundancy in edge storage systems, which produces substantial storage resource wastage. This motivates the need for an approach for data deduplication at the edge. However, existing data deduplication methods rely on centralized control, which is not always feasible in practical edge computing environments. This article presents Ripple, the first approach that enables edge servers to deduplicate their data in a decentralized manner. At its core, it builds a data index for each edge server, enabling them to deduplicate data without central control. With Ripple, edge servers can 1) identify data duplicates; 2) remove redundant data without violating data retrieval latency constraints; and 3) ensure data availability after deduplication. The results of trace-driven experiments conducted in a testbed system demonstrate the usefulness of Ripple in practice. Compared with the state-of-the-art approach, Ripple improves the deduplication ratio by up to 16.79% and reduces data retrieval latency by an average of 60.42%.
{"title":"Ripple: Enabling Decentralized Data Deduplication at the Edge","authors":"Ruikun Luo;Qiang He;Feifei Chen;Song Wu;Hai Jin;Yun Yang","doi":"10.1109/TPDS.2024.3493953","DOIUrl":"https://doi.org/10.1109/TPDS.2024.3493953","url":null,"abstract":"With its advantages in ensuring low data retrieval latency and reducing backhaul network traffic, edge computing is becoming a backbone solution for many latency-sensitive applications. An increasingly large number of data is being generated at the edge, stretching the limited capacity of edge storage systems. Improving resource utilization for edge storage systems has become a significant challenge in recent years. Existing solutions attempt to achieve this goal through data placement optimization, data partitioning, data sharing, etc. These approaches overlook the data redundancy in edge storage systems, which produces substantial storage resource wastage. This motivates the need for an approach for data deduplication at the edge. However, existing data deduplication methods rely on centralized control, which is not always feasible in practical edge computing environments. This article presents Ripple, the first approach that enables edge servers to deduplicate their data in a decentralized manner. At its core, it builds a data index for each edge server, enabling them to deduplicate data without central control. With Ripple, edge servers can 1) identify data duplicates; 2) remove redundant data without violating data retrieval latency constraints; and 3) ensure data availability after deduplication. The results of trace-driven experiments conducted in a testbed system demonstrate the usefulness of Ripple in practice. Compared with the state-of-the-art approach, Ripple improves the deduplication ratio by up to 16.79% and reduces data retrieval latency by an average of 60.42%.","PeriodicalId":13257,"journal":{"name":"IEEE Transactions on Parallel and Distributed Systems","volume":"36 1","pages":"55-66"},"PeriodicalIF":5.6,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10747114","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142672007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-07DOI: 10.1109/TPDS.2024.3493034
Qiang He;Guobiao Zhang;Jiawei Wang;Ruikun Luo;Xiaohai Dai;Yuchong Hu;Feifei Chen;Hai Jin;Yun Yang
In the edge computing environment, app vendors can distribute popular data from the cloud to edge servers to provide low-latency data retrieval. A key problem is how to distribute these data from the cloud to edge servers cost-effectively. Under current schemes, a file is divided into some data blocks for parallel transmissions from the cloud to target edge servers. Edge servers can then combine received data blocks to reconstruct the file. While this method expedites the data distribution process, it presents potential drawbacks. It is sensitive to transmission delays and transmission failures caused by runtime exceptions like network fluctuations and server failures. This paper presents EdgeHydra, the first edge data distribution scheme that tackles this challenge through fault tolerance based on erasure coding. Under EdgeHydra, a file is encoded into data blocks and parity blocks for parallel transmission from the cloud to target edge servers. An edge server can reconstruct the file upon the receipt of a sufficient number of these blocks without having to wait for all the blocks in transmission. It also innovatively employs a leaderless block supplement mechanism to ensure the receipt of sufficient blocks for individual target edge servers. These improve the robustness of the data distribution process significantly. Extensive experiments show that EdgeHydra can tolerate delays and failures in individual transmission links effectively, outperforming the state-of-the-art scheme by up to 50.54% in distribution time.
{"title":"EdgeHydra: Fault-Tolerant Edge Data Distribution Based on Erasure Coding","authors":"Qiang He;Guobiao Zhang;Jiawei Wang;Ruikun Luo;Xiaohai Dai;Yuchong Hu;Feifei Chen;Hai Jin;Yun Yang","doi":"10.1109/TPDS.2024.3493034","DOIUrl":"https://doi.org/10.1109/TPDS.2024.3493034","url":null,"abstract":"In the edge computing environment, app vendors can distribute popular data from the cloud to edge servers to provide low-latency data retrieval. A key problem is how to distribute these data from the cloud to edge servers cost-effectively. Under current schemes, a file is divided into some data blocks for parallel transmissions from the cloud to target edge servers. Edge servers can then combine received data blocks to reconstruct the file. While this method expedites the data distribution process, it presents potential drawbacks. It is sensitive to transmission delays and transmission failures caused by runtime exceptions like network fluctuations and server failures. This paper presents EdgeHydra, the first edge data distribution scheme that tackles this challenge through fault tolerance based on erasure coding. Under EdgeHydra, a file is encoded into data blocks and parity blocks for parallel transmission from the cloud to target edge servers. An edge server can reconstruct the file upon the receipt of a sufficient number of these blocks without having to wait for all the blocks in transmission. It also innovatively employs a leaderless block supplement mechanism to ensure the receipt of sufficient blocks for individual target edge servers. These improve the robustness of the data distribution process significantly. Extensive experiments show that EdgeHydra can tolerate delays and failures in individual transmission links effectively, outperforming the state-of-the-art scheme by up to 50.54% in distribution time.","PeriodicalId":13257,"journal":{"name":"IEEE Transactions on Parallel and Distributed Systems","volume":"36 1","pages":"29-42"},"PeriodicalIF":5.6,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10746622","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142672047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper introduces a novel Real Relative encoding Genetic Algorithm (R $^{2}$ GA) to tackle the workflow scheduling problem in heterogeneous distributed computing systems (HDCS). R $^{2}$ GA employs a unique encoding mechanism, using real numbers to represent the relative positions of tasks in the schedulable task set. Decoding is performed by interpreting these real numbers in relation to the directed acyclic graph (DAG) of the workflow. This approach ensures that any sequence of randomly generated real numbers, produced by cross-over and mutation operations, can always be decoded into a valid solution, as the precedence constraints between tasks are explicitly defined by the DAG. The proposed encoding and decoding mechanism simplifies genetic operations and facilitates efficient exploration of the solution space. This inherent flexibility also allows R $^{2}$ GA to be easily adapted to various optimization scenarios in workflow scheduling within HDCS. Additionally, R $^{2}$ GA overcomes several issues associated with traditional genetic algorithms (GAs) and existing real-number encoding GAs, such as the generation of chromosomes that violate task precedence constraints and the strict limitations on gene value ranges. Experimental results show that R $^{2}$ GA consistently delivers superior performance in terms of solution quality and efficiency compared to existing techniques.
本文介绍了一种新颖的实数相对编码遗传算法（R$^{2}$GA），用于解决异构分布式计算系统（HDCS）中的工作流调度问题。R$^{2}$GA 采用独特的编码机制，用实数表示可调度任务集中任务的相对位置。解码是根据工作流的有向无环图（DAG）来解释这些实数的。这种方法可确保任何由交叉和突变操作随机生成的实数序列总能被解码为有效的解决方案，因为任务之间的优先级约束是由 DAG 明确定义的。所提出的编码和解码机制简化了遗传操作，有利于高效探索解空间。这种固有的灵活性也使得 R$^{2}$GA 可以轻松适应 HDCS 中工作流调度的各种优化方案。此外，R$^{2}$GA 还克服了与传统遗传算法（GA）和现有实数编码 GA 相关的几个问题，如生成的染色体违反任务优先级约束以及基因值范围的严格限制。实验结果表明，与现有技术相比，R$^{2}$GA 在解决方案的质量和效率方面始终表现出色。
{"title":"Real Relative Encoding Genetic Algorithm for Workflow Scheduling in Heterogeneous Distributed Computing Systems","authors":"Junqiang Jiang;Zhifang Sun;Ruiqi Lu;Li Pan;Zebo Peng","doi":"10.1109/TPDS.2024.3492210","DOIUrl":"https://doi.org/10.1109/TPDS.2024.3492210","url":null,"abstract":"This paper introduces a novel Real Relative encoding Genetic Algorithm (R\u0000<inline-formula><tex-math>$^{2}$</tex-math></inline-formula>\u0000GA) to tackle the workflow scheduling problem in heterogeneous distributed computing systems (HDCS). R\u0000<inline-formula><tex-math>$^{2}$</tex-math></inline-formula>\u0000GA employs a unique encoding mechanism, using real numbers to represent the relative positions of tasks in the schedulable task set. Decoding is performed by interpreting these real numbers in relation to the directed acyclic graph (DAG) of the workflow. This approach ensures that any sequence of randomly generated real numbers, produced by cross-over and mutation operations, can always be decoded into a valid solution, as the precedence constraints between tasks are explicitly defined by the DAG. The proposed encoding and decoding mechanism simplifies genetic operations and facilitates efficient exploration of the solution space. This inherent flexibility also allows R\u0000<inline-formula><tex-math>$^{2}$</tex-math></inline-formula>\u0000GA to be easily adapted to various optimization scenarios in workflow scheduling within HDCS. Additionally, R\u0000<inline-formula><tex-math>$^{2}$</tex-math></inline-formula>\u0000GA overcomes several issues associated with traditional genetic algorithms (GAs) and existing real-number encoding GAs, such as the generation of chromosomes that violate task precedence constraints and the strict limitations on gene value ranges. Experimental results show that R\u0000<inline-formula><tex-math>$^{2}$</tex-math></inline-formula>\u0000GA consistently delivers superior performance in terms of solution quality and efficiency compared to existing techniques.","PeriodicalId":13257,"journal":{"name":"IEEE Transactions on Parallel and Distributed Systems","volume":"36 1","pages":"1-14"},"PeriodicalIF":5.6,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142672036","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}
Sparse matrix-vector multiplication (SpMV) is crucial in many scientific and engineering applications, particularly concerning the effectiveness of different sparse matrix storage formats for various architectures, no single format excels across all hardware. Previous research has focused on trying different algorithms to build predictors for the best format, yet it overlooked how to address the issue of the best format changing in the same hardware environment and how to reduce prediction overhead rather than merely considering the overhead in building predictors. This paper proposes a novel classification algorithm for optimizing sparse matrix storage formats, DyLaClass, based on dynamic labeling and flexible feature selection. Particularly, we introduce mixed labels and features with strong correlations, allowing us to achieve ultra-high prediction accuracy with minimal feature inputs, significantly reducing feature extraction overhead. For the first time, we propose the concept of the most suitable storage format rather than the best storage format, which can stably predict changes in the best format for the same matrix across multiple SpMV executions. We further demonstrate the proposed method on the University of Florida’s public sparse matrix collection dataset. Experimental results show that compared to existing work, our method achieves up to 91% classification accuracy. Using two different hardware platforms for verification, the proposed method outperforms existing methods by 1.26 to 1.43 times. Most importantly, the stability of the proposed prediction model is 25.5% higher than previous methods, greatly increasing the feasibility of the model in practical field applications.
{"title":"DyLaClass: Dynamic Labeling Based Classification for Optimal Sparse Matrix Format Selection in Accelerating SpMV","authors":"Zheng Shi;Yi Zou;Xianfeng Song;Shupeng Li;Fangming Liu;Quan Xue","doi":"10.1109/TPDS.2024.3488053","DOIUrl":"https://doi.org/10.1109/TPDS.2024.3488053","url":null,"abstract":"Sparse matrix-vector multiplication (SpMV) is crucial in many scientific and engineering applications, particularly concerning the effectiveness of different sparse matrix storage formats for various architectures, no single format excels across all hardware. Previous research has focused on trying different algorithms to build predictors for the best format, yet it overlooked how to address the issue of the best format changing in the same hardware environment and how to reduce prediction overhead rather than merely considering the overhead in building predictors. This paper proposes a novel classification algorithm for optimizing sparse matrix storage formats, DyLaClass, based on dynamic labeling and flexible feature selection. Particularly, we introduce mixed labels and features with strong correlations, allowing us to achieve ultra-high prediction accuracy with minimal feature inputs, significantly reducing feature extraction overhead. For the first time, we propose the concept of the most suitable storage format rather than the best storage format, which can stably predict changes in the best format for the same matrix across multiple SpMV executions. We further demonstrate the proposed method on the University of Florida’s public sparse matrix collection dataset. Experimental results show that compared to existing work, our method achieves up to 91% classification accuracy. Using two different hardware platforms for verification, the proposed method outperforms existing methods by 1.26 to 1.43 times. Most importantly, the stability of the proposed prediction model is 25.5% higher than previous methods, greatly increasing the feasibility of the model in practical field applications.","PeriodicalId":13257,"journal":{"name":"IEEE Transactions on Parallel and Distributed Systems","volume":"35 12","pages":"2624-2639"},"PeriodicalIF":5.6,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142595927","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}
Emerging high-performance computing (HPC) applications with diverse workload characteristics impose greater demands on parallel file systems (PFSs). PFSs also require more efficient software designs to fully utilize the performance of modern hardware, such as multi-core CPUs, Remote Direct Memory Access (RDMA), and NVMe SSDs. However, existing PFSs expose great limitations under these requirements due to limited multi-core scalability, unaware of HPC workloads, and disjointed network-storage optimizations. In this article, we present PeakFS, an ultra-high performance parallel file system via computing-network-storage co-optimization for HPC applications. PeakFS designs a shared-nothing scheduling system based on link-reduced task dispatching with lock-free queues to reduce concurrency overhead. Besides, PeakFS improves I/O performance with flexible distribution strategies, memory-efficient indexing, and metadata caching according to HPC I/O characteristics. Finally, PeakFS shortens the critical path of request processing through network-storage co-optimizations. Experimental results show that the metadata and data performance of PeakFS reaches more than 90% of the hardware limits. For metadata throughput, PeakFS achieves a 3.5–19× improvement over GekkoFS and outperforms BeeGFS by three orders of magnitude.
{"title":"PeakFS: An Ultra-High Performance Parallel File System via Computing-Network-Storage Co-Optimization for HPC Applications","authors":"Yixiao Chen;Haomai Yang;Kai Lu;Wenlve Huang;Jibin Wang;Jiguang Wan;Jian Zhou;Fei Wu;Changsheng Xie","doi":"10.1109/TPDS.2024.3485754","DOIUrl":"https://doi.org/10.1109/TPDS.2024.3485754","url":null,"abstract":"Emerging high-performance computing (HPC) applications with diverse workload characteristics impose greater demands on parallel file systems (PFSs). PFSs also require more efficient software designs to fully utilize the performance of modern hardware, such as multi-core CPUs, Remote Direct Memory Access (RDMA), and NVMe SSDs. However, existing PFSs expose great limitations under these requirements due to limited multi-core scalability, unaware of HPC workloads, and disjointed network-storage optimizations. In this article, we present PeakFS, an ultra-high performance parallel file system via computing-network-storage co-optimization for HPC applications. PeakFS designs a shared-nothing scheduling system based on link-reduced task dispatching with lock-free queues to reduce concurrency overhead. Besides, PeakFS improves I/O performance with flexible distribution strategies, memory-efficient indexing, and metadata caching according to HPC I/O characteristics. Finally, PeakFS shortens the critical path of request processing through network-storage co-optimizations. Experimental results show that the metadata and data performance of PeakFS reaches more than 90% of the hardware limits. For metadata throughput, PeakFS achieves a 3.5–19× improvement over GekkoFS and outperforms BeeGFS by three orders of magnitude.","PeriodicalId":13257,"journal":{"name":"IEEE Transactions on Parallel and Distributed Systems","volume":"35 12","pages":"2578-2595"},"PeriodicalIF":5.6,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142595929","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}
Edge computing has been developed as a low-latency data driven computation paradigm close to the end user to maximize profit, and/or minimize energy consumption. Edge computing allows each user’s task to analyze locally-acquired sensor data at the edge to reduce the resource congestion and improve the efficiency of data processing. To reduce application latency and data transferred to edge servers it is essential to consider data sharing for some user tasks that operate on the same data items. In this article, we formulate the data sharing-aware allocation problem which has as objectives the maximization of profit and minimization of network traffic by considering data-sharing characteristics of tasks on servers. Because the problem is ${sf NP-hard}$ , we design the ${sf DSTA}$ algorithm to find a feasible solution in polynomial time. We investigate the approximation guarantees of ${sf DSTA}$ by determining the approximation ratios with respect to the total profit and the amount of total data traffic in the edge network. We also design a variant of ${sf DSTA}$ , called ${sf DSTAR}$ that uses a smart rearrangement of tasks to allocate some of the unallocated tasks for increased total profit. We perform extensive experiments to investigate the performance of ${sf DSTA}$ and ${sf DSTAR}$ , and compare them with a representative greedy baseline that only maximizes profit. Our experimental analysis shows that, compared to the baseline, ${sf DSTA}$ reduces the total data traffic in the edge network by up to 20% across 45 case study instances at a small profit loss. In addition, ${sf DSTAR}$ increases the total profit by up to 27% and the number of allocated tasks by 25% compared to ${sf DSTA}$ , all while limiting the increase of total data traffic in the network.
{"title":"Algorithms for Data Sharing-Aware Task Allocation in Edge Computing Systems","authors":"Sanaz Rabinia;Niloofar Didar;Marco Brocanelli;Daniel Grosu","doi":"10.1109/TPDS.2024.3486184","DOIUrl":"https://doi.org/10.1109/TPDS.2024.3486184","url":null,"abstract":"Edge computing has been developed as a low-latency data driven computation paradigm close to the end user to maximize profit, and/or minimize energy consumption. Edge computing allows each user’s task to analyze locally-acquired sensor data at the edge to reduce the resource congestion and improve the efficiency of data processing. To reduce application latency and data transferred to edge servers it is essential to consider data sharing for some user tasks that operate on the same data items. In this article, we formulate the data sharing-aware allocation problem which has as objectives the maximization of profit and minimization of network traffic by considering data-sharing characteristics of tasks on servers. Because the problem is \u0000<inline-formula><tex-math>${sf NP-hard}$</tex-math></inline-formula>\u0000, we design the \u0000<inline-formula><tex-math>${sf DSTA}$</tex-math></inline-formula>\u0000 algorithm to find a feasible solution in polynomial time. We investigate the approximation guarantees of \u0000<inline-formula><tex-math>${sf DSTA}$</tex-math></inline-formula>\u0000 by determining the approximation ratios with respect to the total profit and the amount of total data traffic in the edge network. We also design a variant of \u0000<inline-formula><tex-math>${sf DSTA}$</tex-math></inline-formula>\u0000, called \u0000<inline-formula><tex-math>${sf DSTAR}$</tex-math></inline-formula>\u0000 that uses a smart rearrangement of tasks to allocate some of the unallocated tasks for increased total profit. We perform extensive experiments to investigate the performance of \u0000<inline-formula><tex-math>${sf DSTA}$</tex-math></inline-formula>\u0000 and \u0000<inline-formula><tex-math>${sf DSTAR}$</tex-math></inline-formula>\u0000, and compare them with a representative greedy baseline that only maximizes profit. Our experimental analysis shows that, compared to the baseline, \u0000<inline-formula><tex-math>${sf DSTA}$</tex-math></inline-formula>\u0000 reduces the total data traffic in the edge network by up to 20% across 45 case study instances at a small profit loss. In addition, \u0000<inline-formula><tex-math>${sf DSTAR}$</tex-math></inline-formula>\u0000 increases the total profit by up to 27% and the number of allocated tasks by 25% compared to \u0000<inline-formula><tex-math>${sf DSTA}$</tex-math></inline-formula>\u0000, all while limiting the increase of total data traffic in the network.","PeriodicalId":13257,"journal":{"name":"IEEE Transactions on Parallel and Distributed Systems","volume":"36 1","pages":"15-28"},"PeriodicalIF":5.6,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142672115","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 : 2024-10-24DOI: 10.1109/TPDS.2024.3486219
Savita Gautam;Abdus Samad;Mohammad S. Umar
High-performance interconnection networks are currently being used to design Massively Parallel Computers. Selecting the set of nodes on which parallel tasks execute plays a vital role in the performance of such systems. These networks when deployed to run large parallel applications suffer from communication latencies which ultimately affect the system throughput. Mesh and Torus are primary examples of topologies used in such systems. However, these are being replaced with more efficient and complicated hybrid topologies such as ZMesh and x-Folded TM networks. This paper presents a new topology named as Linearly Extensible Cube-Triangle (LECΔ) which focuses on low latency, lesser average distance and improved throughput. It is symmetrical in nature and exhibits the desirable properties of similar networks with lesser complexity and cost. For N x N network, the LECΔ topology has lesser network latency than that of Mesh, ZMesh, Torus and x-Folded networks. The proposed LECΔ network produces reduced average distance, diameter and cost. It has a high value of bisection width and good scalability. The simulation results show that the performance of LECΔ network is similar to that of Mesh, ZMesh, Torus and x-Folded networks. The results verify the efficiency of the LECΔ network as evaluated and compared with similar networks.
高性能互连网络目前正被用于设计大规模并行计算机。选择执行并行任务的节点集对此类系统的性能起着至关重要的作用。这些网络在部署用于运行大型并行应用时会出现通信延迟，最终影响系统吞吐量。网格和 Torus 是此类系统中使用的拓扑结构的主要例子。然而，这些拓扑结构正在被 ZMesh 和 x-Folded TM 网络等更高效、更复杂的混合拓扑结构所取代。本文提出了一种名为线性可扩展立方体-三角形（LECΔ）的新拓扑结构，其重点是低延迟、减少平均距离和提高吞吐量。它在本质上是对称的，具有类似网络的理想特性，但复杂性和成本较低。对于 N x N 网络，LECΔ 拓扑的网络延迟低于 Mesh、ZMesh、Torus 和 x-Folded 网络。拟议的 LECΔ 网络可减少平均距离、直径和成本。它具有较高的分段宽度值和良好的可扩展性。仿真结果表明，LECΔ 网络的性能与 Mesh、ZMesh、Torus 和 x-Folded 网络相似。这些结果验证了 LECΔ 网络的效率，并将其与类似网络进行了评估和比较。
{"title":"Design and Performance Evaluation of Linearly Extensible Cube-Triangle Network for Multicore Systems","authors":"Savita Gautam;Abdus Samad;Mohammad S. Umar","doi":"10.1109/TPDS.2024.3486219","DOIUrl":"https://doi.org/10.1109/TPDS.2024.3486219","url":null,"abstract":"High-performance interconnection networks are currently being used to design Massively Parallel Computers. Selecting the set of nodes on which parallel tasks execute plays a vital role in the performance of such systems. These networks when deployed to run large parallel applications suffer from communication latencies which ultimately affect the system throughput. Mesh and Torus are primary examples of topologies used in such systems. However, these are being replaced with more efficient and complicated hybrid topologies such as ZMesh and x-Folded TM networks. This paper presents a new topology named as Linearly Extensible Cube-Triangle (LECΔ) which focuses on low latency, lesser average distance and improved throughput. It is symmetrical in nature and exhibits the desirable properties of similar networks with lesser complexity and cost. For N x N network, the LECΔ topology has lesser network latency than that of Mesh, ZMesh, Torus and x-Folded networks. The proposed LECΔ network produces reduced average distance, diameter and cost. It has a high value of bisection width and good scalability. The simulation results show that the performance of LECΔ network is similar to that of Mesh, ZMesh, Torus and x-Folded networks. The results verify the efficiency of the LECΔ network as evaluated and compared with similar networks.","PeriodicalId":13257,"journal":{"name":"IEEE Transactions on Parallel and Distributed Systems","volume":"35 12","pages":"2596-2607"},"PeriodicalIF":5.6,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142595892","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}
Federated Learning (FL) enables multiple devices to collaboratively train a shared model while preserving data privacy. Ever-increasing model complexity coupled with limited memory resources on the participating devices severely bottlenecks the deployment of FL in real-world scenarios. Thus, a framework that can effectively break the memory wall while jointly taking into account the hardware and statistical heterogeneity in FL is urgently required. In this article, we propose SmartSplit a framework that effectively reduces the memory footprint on the device side while guaranteeing the training progress and model accuracy for heterogeneous FL through model splitting. Towards this end, SmartSplit employs a hierarchical structure to adaptively guide the overall training process. In each training round, the central manager, hosted on the server, dynamically selects the participating devices and sets the cutting layer by jointly considering the memory budget, training capacity, and data distribution of each device. The MEC manager, deployed within the edge server, proceeds to split the local model and perform training of the server-side portion. Meanwhile, it fine-tunes the splitting points based on the time-evolving statistical importance. The on-device manager, embedded inside each mobile device, continuously monitors the local training status while employing cost-aware checkpointing to match the runtime dynamic memory budget. Extensive experiments on representative datasets are conducted on both commercial off-the-shelf mobile device testbeds. The experimental results show that SmartSplit excels in FL training on highly memory-constrained mobile SoCs, offering up to a 94% peak latency reduction and 100-fold memory savings. It enhances accuracy performance by 1.49%-57.18% and adaptively adjusts to dynamic memory budgets through cost-aware recomputation
{"title":"Breaking the Memory Wall for Heterogeneous Federated Learning via Model Splitting","authors":"Chunlin Tian;Li Li;Kahou Tam;Yebo Wu;Cheng-Zhong Xu","doi":"10.1109/TPDS.2024.3480115","DOIUrl":"https://doi.org/10.1109/TPDS.2024.3480115","url":null,"abstract":"Federated Learning (FL) enables multiple devices to collaboratively train a shared model while preserving data privacy. Ever-increasing model complexity coupled with limited memory resources on the participating devices severely bottlenecks the deployment of FL in real-world scenarios. Thus, a framework that can effectively break the memory wall while jointly taking into account the hardware and statistical heterogeneity in FL is urgently required. In this article, we propose \u0000<italic>SmartSplit</i>\u0000 a framework that effectively reduces the memory footprint on the device side while guaranteeing the training progress and model accuracy for heterogeneous FL through model splitting. Towards this end, \u0000<italic>SmartSplit</i>\u0000 employs a hierarchical structure to adaptively guide the overall training process. In each training round, the central manager, hosted on the server, dynamically selects the participating devices and sets the cutting layer by jointly considering the memory budget, training capacity, and data distribution of each device. The MEC manager, deployed within the edge server, proceeds to split the local model and perform training of the server-side portion. Meanwhile, it fine-tunes the splitting points based on the time-evolving statistical importance. The on-device manager, embedded inside each mobile device, continuously monitors the local training status while employing cost-aware checkpointing to match the runtime dynamic memory budget. Extensive experiments on representative datasets are conducted on both commercial off-the-shelf mobile device testbeds. The experimental results show that \u0000<italic>SmartSplit</i>\u0000 excels in FL training on highly memory-constrained mobile SoCs, offering up to a 94% peak latency reduction and 100-fold memory savings. It enhances accuracy performance by 1.49%-57.18% and adaptively adjusts to dynamic memory budgets through cost-aware recomputation","PeriodicalId":13257,"journal":{"name":"IEEE Transactions on Parallel and Distributed Systems","volume":"35 12","pages":"2513-2526"},"PeriodicalIF":5.6,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142524102","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 : 2024-10-14DOI: 10.1109/TPDS.2024.3480223
Keyuan Wang;Linpeng Jia;Zhaoxiong Song;Yi Sun
Sharding is a prevalent approach for addressing performance issues in blockchain. To reduce governance complexities and ensure system security, a common practice involves a relay chain to coordinate cross-shard transactions. However, with a growing number of shards and cross-shard transactions, the single relay chain usually first suffers from performance bottleneck and shows poor scalability, thus making the relay chain's scalability vital for sharding systems. To solve this, we propose Mitosis , the first multi-relay architecture to improve the relay chain's scalability by sharding the relay chain itself. Our proposed relay sharding algorithm dynamically adjusts the number of relays or optimizes the topology between relays and shards to adaptively scale up relay chain's performance. Furthermore, to guarantee the security of the multi-relay architecture, a new validator reconfiguration scheme is designed, accompanied by a comprehensive security analysis of Mitosis . Through simulation experiments on two mainstream relay chain paradigms, we demonstrate that Mitosis can achieve high scalability and outperform state-of-the-art baselines in terms of workload of relays, relay chain throughput, and transaction latency.
{"title":"Mitosis: A Scalable Sharding System Featuring Multiple Dynamic Relay Chains","authors":"Keyuan Wang;Linpeng Jia;Zhaoxiong Song;Yi Sun","doi":"10.1109/TPDS.2024.3480223","DOIUrl":"https://doi.org/10.1109/TPDS.2024.3480223","url":null,"abstract":"Sharding is a prevalent approach for addressing performance issues in blockchain. To reduce governance complexities and ensure system security, a common practice involves a relay chain to coordinate cross-shard transactions. However, with a growing number of shards and cross-shard transactions, the single relay chain usually first suffers from performance bottleneck and shows poor scalability, thus making the relay chain's scalability vital for sharding systems. To solve this, we propose \u0000<italic>Mitosis</i>\u0000, the first multi-relay architecture to improve the relay chain's scalability by sharding the relay chain itself. Our proposed relay sharding algorithm dynamically adjusts the number of relays or optimizes the topology between relays and shards to adaptively scale up relay chain's performance. Furthermore, to guarantee the security of the multi-relay architecture, a new validator reconfiguration scheme is designed, accompanied by a comprehensive security analysis of \u0000<italic>Mitosis</i>\u0000. Through simulation experiments on two mainstream relay chain paradigms, we demonstrate that \u0000<italic>Mitosis</i>\u0000 can achieve high scalability and outperform state-of-the-art baselines in terms of workload of relays, relay chain throughput, and transaction latency.","PeriodicalId":13257,"journal":{"name":"IEEE Transactions on Parallel and Distributed Systems","volume":"35 12","pages":"2497-2512"},"PeriodicalIF":5.6,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10716349","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142518166","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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