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Gray-Box Bayesian Optimization in One Dimension for Uncertain Coded Edge Computing
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DOI：10.1109/LCOMM.2025.3571911.png)
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Abstract
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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 coordinator 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 gray-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.
Keywords:
Coded edge computing
gray-box Bayesian optimization
computation offloading
straggler mitigation
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