Journal
IEEE TRANSACTIONS ON MOBILE COMPUTING
Volume 22, Issue 10, Pages 5846-5857Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TMC.2022.3190046
Keywords
Processor scheduling; Wireless communication; Performance evaluation; Image edge detection; Resource management; Task analysis; Scheduling; Edge computation offloading; distributed and parallel computing; communication scheduling
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This article explores the correlation between computation workloads and communication workloads in distributed edge computation offloading, aiming to minimize the total completion time. The authors tackle challenges such as precedence constraints, interference constraints, and workload correlation in order to devise efficient policies that approach the optimal solution. Their results provide valuable insights for the computation-communication co-design of distributed edge computation offloading.
Distributed edge computation offloading makes use of distributed wireless edge devices to perform offloaded computation in parallel, which can substantially reduce the computation time. In this article, we explore distributed edge computation offloading where the computation workloads of edge devices are correlated with their communication workloads. In particular, we study the fundamental problem of computation workload allocation and communication scheduling for minimizing the total completion time of the computation offloading. To solve this problem, we need to tackle several challenges due to the precedence constraints of computations and communications, the interference constraints of wireless edge devices, and the correlation between computation and communication workloads. We consider preemptive, half-preemptive, and non-preemptive networks for the formulated problem, respectively. For each setting, we first develop a simplified problem of computation allocation, based on which we then devise an efficient and feasible policy that can arbitrarily approach the optimal policy. For half-preemptive and non-preemptive networks, we also characterize the optimal communication orders. Our results provides useful insights for the computation-communication co-design of distributed edge computation offloading. We evaluate the proposed algorithms using simulation results, which corroborate the advantages of the algorithms.
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