Bayesian Inference and Greedy Task Allocation for Edge Computing Systems with Uncertainty

A computing task can be distributed in an edge network and offloaded to multiple edge devices, called workers, to expedite the processing. The computing speeds of the workers, however, are usually unknown or time-varying. To identify the fast workers, a Bayesian approach based on Thompson sampling is used. The estimation of the computing speeds of the workers is formulated as a multi-armed bandit problem. While existing schemes allocate the same amount of computation work to each selected worker, this paper exploits the heterogeneous computing speeds of the workers and formulates the task allocation problem with the objective of minimizing the overall computing delay. A lower bound for the delay is obtained and is proved to be minimized by a greedy algorithm. Simulation results show that our scheme outperforms other benchmarks.