Coded Elastic Computing

TL;DR

This paper introduces coded elastic computing, a method that efficiently manages elastic, preemptible cloud resources for distributed tasks, maintaining performance while reducing computation time.

Contribution

It presents a novel coding-based approach that adapts workload dynamically, enabling elastic resource utilization without sacrificing algorithm accuracy.

Findings

Achieves same numerical results as noiseless methods.

Reduces computation time by 46% compared to non-adaptive schemes.

Handles preemption and resource joining seamlessly.

Abstract

Cloud providers have recently introduced new offerings whereby spare computing resources are accessible at discounts compared to on-demand computing. Exploiting such opportunity is challenging inasmuch as such resources are accessed with low-priority and therefore can elastically leave (through preemption) and join the computation at any time. In this paper, we design a new technique called coded elastic computing, enabling distributed computations over elastic resources. The proposed technique allows machines to leave the computation without sacrificing the algorithm-level performance, and, at the same time, adaptively reduce the workload at existing machines when new ones join the computation. Leveraging coded redundancy, our approach can achieve similar computational cost as the original (noiseless) method when all machines are present; the cost gracefully increases when machines are preempted and reduces when machines join. The performance of the proposed technique is evaluated on matrix-vector multiplication and linear regression tasks. In experimental validations, it can achieve exactly the same numerical result as the noiseless computation, while reducing the computation time by 46% when compared to non-adaptive coding schemes.