Wukong: A Scalable and Locality-Enhanced Framework for Serverless Parallel Computing

TL;DR

Wukong is a scalable, decentralized serverless framework for parallel DAG computations that significantly improves performance, data locality, and cost efficiency on AWS Lambda.

Contribution

The paper introduces Wukong, a novel decentralized scheduling framework for serverless parallel computing that enhances scalability, data locality, and cost savings.

Findings

Achieves near-ideal scalability for parallel jobs.

Executes jobs up to 68.17x faster.

Reduces network I/O by orders of magnitude.

Abstract

Serverless computing is increasingly being used for parallel computing, which have traditionally been implemented as stateful applications. Executing complex, burst-parallel, directed acyclic graph (DAG) jobs poses a major challenge for serverless execution frameworks, which will need to rapidly scale and schedule tasks at high throughput, while minimizing data movement across tasks. We demonstrate that, for serverless parallel computations, decentralized scheduling enables scheduling to be distributed across Lambda executors that can schedule tasks in parallel, and brings multiple benefits, including enhanced data locality, reduced network I/Os, automatic resource elasticity, and improved cost effectiveness. We describe the implementation and deployment of our new serverless parallel framework, called Wukong, on AWS Lambda. We show that Wukong achieves near-ideal scalability, executes parallel computation jobs up to 68.17x faster, reduces network I/O by multiple orders of magnitude, and achieves 92.96% tenant-side cost savings compared to numpywren.