Serverless Straggler Mitigation using Local Error-Correcting Codes

TL;DR

This paper introduces a novel error-correcting code-based approach for mitigating stragglers in serverless computing, improving latency and efficiency in distributed matrix multiplication tasks.

Contribution

It presents a fully distributed, masterless coding scheme for straggler mitigation in serverless systems, with theoretical optimality and practical performance improvements.

Findings

Outperforms existing schemes by at least 25% in experiments

Establishes asymptotic optimality in decoding time

Provides a lower bound on tolerated stragglers

Abstract

Inexpensive cloud services, such as serverless computing, are often vulnerable to straggling nodes that increase end-to-end latency for distributed computation. We propose and implement simple yet principled approaches for straggler mitigation in serverless systems for matrix multiplication and evaluate them on several common applications from machine learning and high-performance computing. The proposed schemes are inspired by error-correcting codes and employ parallel encoding and decoding over the data stored in the cloud using serverless workers. This creates a fully distributed computing framework without using a master node to conduct encoding or decoding, which removes the computation, communication and storage bottleneck at the master. On the theory side, we establish that our proposed scheme is asymptotically optimal in terms of decoding time and provide a lower bound on the number of stragglers it can tolerate with high probability. Through extensive experiments, we show that our scheme outperforms existing schemes such as speculative execution and other coding theoretic methods by at least 25%.