Combining High Throughput and Low Migration Latency for Consistent Data Storage on the Edge

Abstract

Today, many applications offload computation and storage to the cloud. Unfortunately, the high network latency between clients and datacenters can impair novel, latency-constrained, applications such as augmented reality. Edge computing has emerged as a potential solution to circumvent this problem. To unleash its full potential, the edge must cache data that is frequently used. However, building a storage service that is able to maintain many (partial) replicas while providing meaningful consistency guarantees to clients that migrate among multiple edge caches is an open challenge. In this paper, we present Gesto, a data storage architecture that enables scalable causal consistency for edge networks. Gesto integrates a novel causality tracking mechanism that relies on multi-part timestamps of constant size, independently on the number of edge caches. As our evaluation shows, this mechanism enables Gesto to simultaneously offer scalability, low read/write latency, high throughput, and, unlike previous work, fast client migrations.