Constrained App Data Caching Over Edge Server Graphs in Edge Computing Environment

计算机科学 边缘计算 GSM演进的增强数据速率 服务器 计算机网络 分布式计算 人工智能
作者
Xiaoyu Xia,Feifei Chen,John Grundy,Mohamed Abdelrazek,Hai Jin,Qiang He
出处
期刊:IEEE Transactions on Services Computing [Institute of Electrical and Electronics Engineers]
卷期号:15 (5): 2635-2647 被引量:40
标识
DOI:10.1109/tsc.2021.3062017
摘要

In recent years, edge computing, as an extension of cloud computing, has emerged as a promising paradigm for powering a variety of applications demanding low latency, e.g., virtual or augmented reality, interactive gaming, real-time navigation, etc. In the edge computing environment, edge servers are deployed at base stations to offer highly-accessible computing capacities to nearby end-users, e.g., CPU, RAM, storage, etc. From a service provider's perspective, caching app data on edge servers can ensure low latency in its users' data retrieval. Given constrained cache spaces on edge servers due to their physical sizes, the optimal data caching strategy must minimize overall user latency. In this article, we formulate this Constrained Edge Data Caching (CEDC) problem as a constrained optimization problem from the service provider's perspective and prove its $\mathcal {NP}$ -hardness. We propose an optimal approach named CEDC-IP to solve this CEDC problem with the Integer Programming technique. We also provide an approximation algorithm named CEDC-A for finding approximate solutions to large-scale CEDC problems efficiently and prove its approximation ratio. CEDC-IP and CEDC-A are evaluated on a real-world data set. The results demonstrate that they significantly outperform four representative approaches.

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