Uncoded Download in Lagrange-Coded Elastic Computing with Straggler Tolerance

TL;DR

This paper introduces Lagrange-coded elastic computing schemes with uncoded download (LCSUD) that effectively handle elasticity and stragglers, while reducing storage, encoding complexity, and upload costs compared to previous methods.

Contribution

It proposes a novel class of elastic computing schemes using Lagrange-coded storage with uncoded download to improve efficiency and straggler tolerance.

Findings

Lower storage size compared to existing methods

Reduced encoding complexity and upload cost

Effective straggler and elasticity handling

Abstract

Coded elastic computing, introduced by Yang et al. in 2018, is a technique designed to mitigate the impact of elasticity in cloud computing systems, where machines can be preempted or be added during computing rounds. This approach utilizes maximum distance separable (MDS) coding for both storage and download in matrix-matrix multiplications. The proposed scheme is unable to tolerate stragglers and has high encoding complexity and upload cost. In 2023, we addressed these limitations by employing uncoded storage and Lagrange-coded download. However, it results in a large storage size. To address the challenges of storage size and upload cost, in this paper, we focus on Lagrange-coded elastic computing based on uncoded download. We propose a new class of elastic computing schemes, using Lagrange-coded storage with uncoded download (LCSUD). Our proposed schemes address both elasticity and straggler challenges while achieving lower storage size, reduced encoding complexity, and upload cost compared to existing methods.