论文标题

EdgekV:边缘的分散,可扩展和一致的存储

EdgeKV: Decentralized, scalable, and consistent storage for the edge

论文作者

Sonbol, Karim, Özkasap, Öznur, Al-Oqily, Ibrahim, Aloqaily, Moayad

论文摘要

边缘计算将计算更接近数据和数据靠近用户,以克服云计算的高潜伏期通信。边缘存储允许高速访问数据,从而可以在自动驾驶和智能电网等领域中对潜伏期敏感的应用程序进行访问。但是,由于边缘的分布式和异质性质及其有限的资源,几种分布式服务通常是为云设计的,并且构建有效的边缘存储系统是具有挑战性的。在本文中,我们提出了EdgeKV,这是一个为网络边缘设计的分散存储系统。 EdgeKV提供快速可靠的存储空间,并利用具有强大一致性保证的数据复制。借助位置透明和基于接口的设计,EdgeKV可以通过边缘节点的异质系统进行扩展。我们在Golang中实现了EdgeKV模块的原型,并在Grid'5000测试台上的边缘和云设置中对其进行了评估。我们利用Yahoo!云提供基准(YCSB),以分析在现实的工作负载下系统的性能。我们的评估结果表明,在相同的设置下,EdgeKV的表现优于本地和全局数据访问的云存储设置,平均写入响应时间和吞吐量改进分别为26%和19%。我们的评估还表明,EdgeKV可以随着客户量的数量扩展,而无需牺牲性能。最后,我们讨论了使用EdgeKV而不是集中式云的Edge资源时的能源效率提高。

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.

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