Sprout: A functional caching approach to minimize service latency in erasure-coded storage

TL;DR

Sprout introduces a novel functional caching framework using erasure-coded chunks to significantly reduce service latency in distributed storage systems, optimizing cache placement and request routing.

Contribution

It proposes a new functional caching approach with an optimal placement algorithm that improves latency in erasure-coded storage systems.

Findings

Significant latency reduction demonstrated in simulations.

Prototyped solution shows practical benefits in cloud storage.

Optimal caching placement improves overall system performance.

Abstract

Modern distributed storage systems often use erasure codes to protect against disk and node failures to increase reliability, while trying to meet the latency requirements of the applications and clients. Storage systems may have caches at the proxy or client ends in order to reduce the latency. In this paper, we consider a novel caching framework with erasure code called functional caching. Functional Caching involves using erasure-coded chunks in the cache such that the code formed by the chunks in storage nodes and cache combined are maximal-distance-separable (MDS) erasure codes. Based on the arrival rates of different files, placement of file chunks on the servers, and service time distribution of storage servers, an optimal functional caching placement and the access probabilities of the file request from different disks are considered. The proposed algorithm gives significant latency improvement in both simulations and a prototyped solution in an open-source, cloud storage deployment.