Joint Task Allocation and Scheduling for Multi - Hop Distributed Computing
Ke Ma, Junfei Xie · 2024
The rise of edge computing has shifted computing resources closer to end-users, benefiting numerous delay-sensitive, computation-intensive applications. To speed up computation, distributed computing is a promising technique that allows parallel execution of computation tasks across multiple compute nodes. However, current research predominantly revolves around the master-worker paradigm, limiting resource sharing within one-hop neighborhoods. This limitation can render distributed computing ineffective in scenarios with limited nearby resources or constrained/dynamic connectivity. In this paper, we address this limitation by introducing a new distributed computing strategy that extends resource sharing beyond one-hop neighborhoods through exploring layered network structures and multi-hop routing. Our approach involves transforming the network graph into a sink tree and solving a joint optimization problem formulated based on the layered tree structure for task allocation and scheduling. Simulation results demonstrate a significant improvement over the traditional distributed computing and computation offloading strategies.