EdgeCut: Fast and Low-overhead Access of User-associated Contents from Edge Servers
Yi Liu, Minmei Wang, Shouqian Shi, Yang Wang, Chen Qian · 2023
User-associated contents play an increasingly important role in modern network applications. With growing deployments of edge servers, the capacity of content storage in edge clusters significantly increases, which provides great potential to satisfy content requests with much shorter latency. However, the large number of contents also causes the difficulty of searching contents on edge servers in different locations because indexing contents costs huge DRAM on each edge server. In this work, we explore the opportunity of efficiently indexing user-associated contents and propose a scalable content-sharing mechanism for edge servers, called EdgeCut, that significantly reduces content access latency by allowing many edge servers to share their cached contents. We design a compact and dynamic data structure called Ludo Locator that returns the IP address of the edge server that stores the requested user-associated content. We have implemented a prototype of EdgeCut in a real network environment running in a public geo-distributed cloud. The experiment results show that EdgeCut reduces content access latency by up to 50% and reduces cloud traffic by up to 50% compared to existing solutions. The memory cost is less than 50MB for 10 million mobile users. The simulations using real network latency data show EdgeCut's advantages over existing solutions on a large scale.