Delay-Optimal Computation Task Scheduling for Mobile-Edge Computing Systems

TL;DR

This paper proposes an optimal stochastic task scheduling policy for mobile-edge computing systems that minimizes average delay under power constraints, using a Markov decision process approach and a one-dimensional search algorithm.

Contribution

It introduces a novel two-timescale stochastic optimization framework for MEC task scheduling, optimizing delay and power consumption simultaneously.

Findings

Achieves shorter average execution delay compared to baseline policies.

Effectively balances delay minimization and power constraints.

Demonstrates the efficiency of the proposed scheduling policy through simulations.

Abstract

Mobile-edge computing (MEC) emerges as a promising paradigm to improve the quality of computation experience for mobile devices. Nevertheless, the design of computation task scheduling policies for MEC systems inevitably encounters a challenging two-timescale stochastic optimization problem. Specifically, in the larger timescale, whether to execute a task locally at the mobile device or to offload a task to the MEC server for cloud computing should be decided, while in the smaller timescale, the transmission policy for the task input data should adapt to the channel side information. In this paper, we adopt a Markov decision process approach to handle this problem, where the computation tasks are scheduled based on the queueing state of the task buffer, the execution state of the local processing unit, as well as the state of the transmission unit. By analyzing the average delay of each task and the average power consumption at the mobile device, we formulate a power-constrained delay minimization problem, and propose an efficient one-dimensional search algorithm to find the optimal task scheduling policy. Simulation results are provided to demonstrate the capability of the proposed optimal stochastic task scheduling policy in achieving a shorter average execution delay compared to the baseline policies.