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EdgeKV: Decentralized, scalable, and consistent storage for the edge
Journal of Parallel and Distributed Computing ( IF 3.4 ) Pub Date : 2020-05-30 , DOI: 10.1016/j.jpdc.2020.05.009
Karim Sonbol , Öznur Özkasap , Ibrahim Al-Oqily , Moayad Aloqaily

Edge computing moves the computation closer to the data and the data closer to the user to overcome the high latency communication of cloud computing. Storage at the edge allows data access with high speeds that enable latency-sensitive applications in areas such as autonomous driving and smart grid. However, several distributed services are typically designed for the cloud and building an efficient edge-enabled storage system is challenging because of the distributed and heterogeneous nature of the edge and its limited resources. In this paper, we propose EdgeKV, a decentralized storage system designed for the network edge. EdgeKV offers fast and reliable storage, utilizing data replication with strong consistency guarantees. With a location-transparent and interface-based design, EdgeKV can scale with a heterogeneous system of edge nodes. We implement a prototype of the EdgeKV modules in Golang and evaluate it in both the edge and cloud settings on the Grid’5000 testbed. We utilize the Yahoo! Cloud Serving Benchmark (YCSB) to analyze the system’s performance under realistic workloads. Our evaluation results show that EdgeKV outperforms the cloud storage setting with both local and global data access with an average write response time and throughput improvements of 26% and 19% respectively under the same settings. Our evaluations also show that EdgeKV can scale with the number of clients, without sacrificing performance. Finally, we discuss the energy efficiency improvement when utilizing edge resources with EdgeKV instead of a centralized cloud.



中文翻译:

EdgeKV:分散,可扩展且一致的边缘存储

边缘计算使计算更靠近数据,数据更靠近用户,从而克服了云计算的高延迟通信。边缘存储允许高速访问数据,从而在诸如自动驾驶和智能电网等领域启用对延迟敏感的应用程序。但是,通常为云设计几种分布式服务,并且由于边缘的分布式和异构性质及其有限的资源,构建有效的支持边缘的存储系统是一项挑战。在本文中,我们提出EdgeKV,这是一种为网络边缘设计的分散式存储系统。EdgeKV利用具有强大一致性保证的数据复制,提供了快速可靠的存储。通过位置透明和基于接口的设计,EdgeKV可以与边缘节点的异构系统进行扩展。我们在Golang中实现EdgeKV模块的原型,并在Grid'5000测试床上的边缘和云设置中对其进行评估。我们利用Yahoo! 云服务基准(YCSB),用于分析实际工作负载下的系统性能。我们的评估结果表明,EdgeKV在本地和全局数据访问方面均优于云存储设置,在相同设置下,平均写入响应时间和吞吐量分别提高了26%和19%。我们的评估还显示,EdgeKV可以随客户数量扩展,而不会牺牲性能。最后,我们讨论了使用EdgeKV而不是集中式云利用边缘资源时的能效改进。我们利用Yahoo! 云服务基准(YCSB),用于分析实际工作负载下的系统性能。我们的评估结果表明,EdgeKV在本地和全局数据访问方面均优于云存储设置,在相同设置下,平均写入响应时间和吞吐量分别提高了26%和19%。我们的评估还显示,EdgeKV可以随客户数量扩展,而不会牺牲性能。最后,我们讨论了使用EdgeKV而不是集中式云利用边缘资源时的能效改进。我们利用Yahoo! 云服务基准(YCSB),用于分析实际工作负载下的系统性能。我们的评估结果表明,EdgeKV在本地和全局数据访问方面均优于云存储设置,在相同设置下,平均写入响应时间和吞吐量分别提高了26%和19%。我们的评估还显示,EdgeKV可以随客户数量扩展,而不会牺牲性能。最后,我们讨论了使用EdgeKV而不是集中式云利用边缘资源时的能效改进。我们的评估结果表明,EdgeKV在本地和全局数据访问方面均优于云存储设置,在相同设置下,平均写入响应时间和吞吐量分别提高了26%和19%。我们的评估还显示,EdgeKV可以随客户数量扩展,而不会牺牲性能。最后,我们讨论了使用EdgeKV而不是集中式云利用边缘资源时的能效改进。我们的评估结果表明,EdgeKV在本地和全局数据访问方面均优于云存储设置,在相同设置下,平均写入响应时间和吞吐量分别提高了26%和19%。我们的评估还显示,EdgeKV可以随客户数量扩展,而不会牺牲性能。最后,我们讨论了使用EdgeKV而不是集中式云利用边缘资源时的能效改进。

更新日期:2020-05-30
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