Edge Computing in the Dark: Leveraging Contextual-Combinatorial Bandit and Coded Computing

With recent advancements in edge computing capabilities, there has been a significant increase in utilizing the edge cloud for event-driven and time-sensitive computations. However, large-scale edge computing networks can suffer substantially from unpredictable and unreliable computing resources which can result in high variability of service quality. We consider the problem of computation offloading over unknown edge cloud networks with a sequence of timely computation jobs. Motivated by the MapReduce computation paradigm, we assume that each computation job can be partitioned to smaller Map functions which are processed at the edge, and the Reduce function is computed at the user after the Map results are collected from the edge nodes. We model the service quality of each edge device as function of context. The user decides the computations to offload to each device with the goal of receiving a recoverable set of computation results in the given deadline. By leveraging the coded computing framework in order to tackle failures or stragglers in computation, we formulate this problem using contextual-combinatorial multi-armed bandits (CC-MAB), and aim to maximize the cumulative expected reward. We propose an online learning policy called online coded edge computing policy, which provably achieves asymptotically-optimal performance in terms of regret loss compared with the optimal offline policy for the proposed CC-MAB problem. In terms of the cumulative reward, it is shown that the online coded edge computing policy significantly outperforms other benchmarks via numerical studies.

Automated summary

This paper investigates computation offloading over unknown edge cloud networks, proposing an online coded edge computing policy to maximize cumulative expected reward in an asymptotically-optimal manner. Numerical studies show that this policy significantly outperforms other benchmarks in terms of cumulative reward.