ReStorEdge: An edge computing system with reuse semantics

TL;DR

ReStorEdge introduces a reuse semantics approach in edge computing, leveraging similarity-based caching to reduce processing overhead and increase throughput by 25-33% in distributed edge systems.

Contribution

The paper presents a novel similarity-based caching system for edge computing that improves throughput and reduces computation by reusing previous results for similar queries.

Findings

Throughput increased by 25-33% with reuse semantics.

Similarity-based classification effectively reduces processing overhead.

Distributed orchestration strategies enhance system capacity.

Abstract

This paper investigates an edge computing system where requests are processed by a set of replicated edge servers. We investigate a class of applications where similar queries produce identical results. To reduce processing overhead on the edge servers we store the results of previous computations and return them when new queries are sufficiently similar to earlier ones that produced the results, avoiding the necessity of processing every new query. We implement a similarity-based data classification system, which we evaluate based on real-world datasets of images and voice queries. We evaluate a range of orchestration strategies to distribute queries and cached results between edge nodes and show that the throughput of queries over a system of distributed edge nodes can be increased by 25-33%, increasing its capacity for higher workloads.