Gray-Box Bayesian Optimization in One Dimension for Uncertain Coded Edge Computing

This letter studies online workload allocation for heterogeneous coded edge computing where iterative matrix multiplications are executed. Unlike conventional models assuming known random delay distributions, we consider a realistic scenario where the master only knows that each worker’s delay is an affine function of its workload, with random coefficients reflecting communication and computing delays. We formulate a stochastic problem, reduce the dimension to one via estimation, and solve it within a grey-box Bayesian optimization framework. Simulation results show that our approach effectively reduces delay relative to online benchmarks while incurring only a slightly higher delay than offline benchmarks.