用于分散协作边缘计算的功能即服务中间件

IF 6.2 2区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS
Catarina Gonçalves , José Simão , Luís Veiga
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引用次数: 0

摘要

功能即服务(FaaS)是一种复杂的云计算范式,非常适合处理由物联网(IoT)基础设施生成的指数级增长的数据量。在数据生成源附近部署计算模型可以解决边缘分布式应用程序中固有的关键延迟和带宽限制。边缘计算环境呈现出复杂的架构挑战,其特征是大规模分散的基础设施和资源受限的设备,这极大地阻碍了当代功能即服务实现策略。本研究介绍了FaaS@Edge,这是一个利用通过星际文件系统(IPFS)网络发现并通过Apache OpenWhisk部署的自愿边缘节点资源的新框架。提出的系统通过支持跨异构边缘基础设施的FaaS运行时部署来解决计算资源分布的挑战。综合实验评估表明,FaaS@Edge在功能提交期间引入了边际延迟,同时保持了与本地OpenWhisk实现相当的性能。经验结果表明,函数提交和调用过程的请求成功率约为98%。这些发现表明FaaS@Edge作为边缘计算环境的高效计算模型的潜力,其特点是低延迟性能和优化的资源分配。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A function-as-a-service middleware for decentralized collaborative edge computing
Function-as-a-Service (FaaS) emerges as a sophisticated cloud computing paradigm critically suited to processing the exponentially increasing data volumes generated by Internet of Things (IoT) infrastructures. Deploying computational models proximal to data generation sources addresses critical latency and bandwidth constraints inherent in edge-distributed applications. Edge computing environments present complex architectural challenges characterized by large-scale decentralized infrastructures and resource-constrained devices, which substantially impede contemporary Function-as-a-Service implementation strategies. This research introduces FaaS@Edge, a novel framework that leverages volunteered edge node resources discovered through the InterPlanetary File System (IPFS) network and deployed via Apache OpenWhisk. The proposed system addresses computational resource distribution challenges by enabling FaaS runtime deployments across heterogeneous edge infrastructure. Comprehensive experimental evaluation shows that FaaS@Edge introduces marginal latency during function submission while maintaining performance comparable to local OpenWhisk implementations. Empirical results demonstrate request success rates that approximate 98 % for function submission and invocation processes. These findings shows FaaS@Edge’s potential as an efficient computational model for edge computing environments, characterized by low-latency performance and optimized resource allocation.
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来源期刊
CiteScore
19.90
自引率
2.70%
发文量
376
审稿时长
10.6 months
期刊介绍: Computing infrastructures and systems are constantly evolving, resulting in increasingly complex and collaborative scientific applications. To cope with these advancements, there is a growing need for collaborative tools that can effectively map, control, and execute these applications. Furthermore, with the explosion of Big Data, there is a requirement for innovative methods and infrastructures to collect, analyze, and derive meaningful insights from the vast amount of data generated. This necessitates the integration of computational and storage capabilities, databases, sensors, and human collaboration. Future Generation Computer Systems aims to pioneer advancements in distributed systems, collaborative environments, high-performance computing, and Big Data analytics. It strives to stay at the forefront of developments in grids, clouds, and the Internet of Things (IoT) to effectively address the challenges posed by these wide-area, fully distributed sensing and computing systems.
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