This paper investigates the allocation of coded computing tasks in edge networks with uncertain conditions. Specifically, it focuses on scenarios where the computing speed-related parameters of edge devices are heterogeneous and unknown. The challenge lies in mitigating the impact of stragglers, which involves identifying fast workers and distributing a reasonable workload among them. In this paper, we put the fast worker identification into a multi-armed bandit (MAB) framework and propose a Thompson sampling (TS)-based approach to tackle it. Then, our approach leverages the heterogeneity of computing speeds by formulating the task allocation problem to minimize the expected computing delay. We derive a lower bound for the delay and prove that our proposed algorithm minimizes this bound. Importantly, the time complexity of our algorithm is independent of the number of tasks to be assigned, which is typically large in practical scenarios. When our scheme is applied to solve the linear regression problem, simulation results show that it reduces the computing delay of a state-of-the-art method by more than 15% in two different scenarios.
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