Serverless Straggler Mitigation using Local Error-Correcting Codes
arXiv:2001.07490
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%.
References in corpus (8)
- Speeding Up Distributed Machine Learning Using Codes
- Serverless Computing: Current Trends and Open Problems
- OverSketch: Approximate Matrix Multiplication for the Cloud
- Occupy the Cloud: Distributed Computing for the 99%
- Learning a Code: Machine Learning for Approximate Non-Linear Coded Computation
- Coded Iterative Computing using Substitute Decoding
- A Sequential Approximation Framework for Coded Distributed Optimization
- Monte Carlo Methods and Path-Generation techniques for Pricing Multi-asset Path-dependent Options