{"title":"A Worst-Case Latency and Age Analysis of Coded Distributed Computing With Unreliable Workers and Periodic Tasks","authors":"Federico Chiariotti;Beatriz Soret;Petar Popovski","doi":"10.1109/OJCOMS.2024.3458802","DOIUrl":null,"url":null,"abstract":"Over the past decade, the deep learning revolution has led to ever-increasing demands for computing power and working memory to support larger and larger neural networks. As this coincided with the end of Moore’s law, distributed solutions have emerged as a natural answer: in particular, the novel Coded Distributed Computing (CDC) paradigm exploits results from coding theory to divide large tasks into redundant sets of smaller subtasks to be processed across multiple workers, making the computation more robust to stragglers and malicious worker nodes. Optimizing the use of these distributed computing resources is critical, as excessive redundancy might impact on performance and energy consumption. This work considers a CDC system receiving periodic tasks, deriving the full distribution of the latency, reliability, and Peak Age of Information (PAoI) under worker diversity and random failures. The CDC system is modeled as a fork-join \n<inline-formula> <tex-math>$D/M/(K, N)/L$ </tex-math></inline-formula>\n queue, where only K of the coded N subtasks are necessary to solve the overall task, and workers can hold up to L subtasks in their queues. Our results are useful for resource optimization, showing the relationship between system load, redundancy, and latency, as well as the trade-off between latency, reliability, and age performance.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":null,"pages":null},"PeriodicalIF":6.3000,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10677483","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Open Journal of the Communications Society","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10677483/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
Over the past decade, the deep learning revolution has led to ever-increasing demands for computing power and working memory to support larger and larger neural networks. As this coincided with the end of Moore’s law, distributed solutions have emerged as a natural answer: in particular, the novel Coded Distributed Computing (CDC) paradigm exploits results from coding theory to divide large tasks into redundant sets of smaller subtasks to be processed across multiple workers, making the computation more robust to stragglers and malicious worker nodes. Optimizing the use of these distributed computing resources is critical, as excessive redundancy might impact on performance and energy consumption. This work considers a CDC system receiving periodic tasks, deriving the full distribution of the latency, reliability, and Peak Age of Information (PAoI) under worker diversity and random failures. The CDC system is modeled as a fork-join
$D/M/(K, N)/L$
queue, where only K of the coded N subtasks are necessary to solve the overall task, and workers can hold up to L subtasks in their queues. Our results are useful for resource optimization, showing the relationship between system load, redundancy, and latency, as well as the trade-off between latency, reliability, and age performance.
过去十年间,深度学习革命导致对计算能力和工作内存的需求不断增加,以支持越来越大的神经网络。由于这与摩尔定律的终结不谋而合,分布式解决方案自然应运而生:特别是,新颖的编码分布式计算(CDC)范例利用编码理论的结果,将大型任务划分为冗余的较小子任务集,由多个工作者处理,从而使计算对落伍者和恶意工作者节点更具鲁棒性。优化使用这些分布式计算资源至关重要,因为过多的冗余可能会影响性能和能耗。本研究考虑了一个接收周期性任务的 CDC 系统,推导出了工人多样性和随机故障下的延迟、可靠性和峰值信息年龄(PAoI)的完整分布。CDC 系统被建模为叉接 $D/M/(K, N)/L$ 队列,其中只有编码的 N 个子任务中的 K 个是解决整个任务所必需的,而工人的队列中最多可容纳 L 个子任务。我们的结果有助于资源优化,显示了系统负载、冗余和延迟之间的关系,以及延迟、可靠性和年龄性能之间的权衡。
期刊介绍:
The IEEE Open Journal of the Communications Society (OJ-COMS) is an open access, all-electronic journal that publishes original high-quality manuscripts on advances in the state of the art of telecommunications systems and networks. The papers in IEEE OJ-COMS are included in Scopus. Submissions reporting new theoretical findings (including novel methods, concepts, and studies) and practical contributions (including experiments and development of prototypes) are welcome. Additionally, survey and tutorial articles are considered. The IEEE OJCOMS received its debut impact factor of 7.9 according to the Journal Citation Reports (JCR) 2023.
The IEEE Open Journal of the Communications Society covers science, technology, applications and standards for information organization, collection and transfer using electronic, optical and wireless channels and networks. Some specific areas covered include:
Systems and network architecture, control and management
Protocols, software, and middleware
Quality of service, reliability, and security
Modulation, detection, coding, and signaling
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Mobile and portable communications
Terminals and other end-user devices
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Communications-based distributed resources control.