A Survey of Coded Distributed Computing
arXiv:2008.09048
Abstract
Distributed computing has become a common approach for large-scale computation of tasks due to benefits such as high reliability, scalability, computation speed, and costeffectiveness. However, distributed computing faces critical issues related to communication load and straggler effects. In particular, computing nodes need to exchange intermediate results with each other in order to calculate the final result, and this significantly increases communication overheads. Furthermore, a distributed computing network may include straggling nodes that run intermittently slower. This results in a longer overall time needed to execute the computation tasks, thereby limiting the performance of distributed computing. To address these issues, coded distributed computing (CDC), i.e., a combination of coding theoretic techniques and distributed computing, has been recently proposed as a promising solution. Coding theoretic techniques have proved effective in WiFi and cellular systems to deal with channel noise. Therefore, CDC may significantly reduce communication load, alleviate the effects of stragglers, provide fault-tolerance, privacy and security. In this survey, we first introduce the fundamentals of CDC, followed by basic CDC schemes. Then, we review and analyze a number of CDC approaches proposed to reduce the communication costs, mitigate the straggler effects, and guarantee privacy and security. Furthermore, we present and discuss applications of CDC in modern computer networks. Finally, we highlight important challenges and promising research directions related to CDC
References in corpus (9)
- Very Deep Convolutional Networks for Large-Scale Image Recognition
- Approximate Gradient Coding via Sparse Random Graphs
- ErasureHead: Distributed Gradient Descent without Delays Using Approximate Gradient Coding
- Incentive Mechanism Design for Resource Sharing in Collaborative Edge Learning
- Towards Federated Learning in UAV-Enabled Internet of Vehicles: A Multi-Dimensional Contract-Matching Approach
- Coded Network Function Virtualization: Fault Tolerance via In-Network Coding
- Combating Computational Heterogeneity in Large-Scale Distributed Computing via Work Exchange
- Coded Distributed Computing with Heterogeneous Function Assignments
- Timely-Throughput Optimal Coded Computing over Cloud Networks