Optimal Task Offloading with Firm Deadlines for Mobile Edge Computing Systems

TL;DR

This paper develops an optimal task offloading strategy for mobile edge computing using autonomous mobile agents, balancing resource costs and task expiration penalties, and proposes methods to reduce computational complexity.

Contribution

It introduces a dynamic programming framework for optimal offloading with deadlines and identifies properties that simplify computation in large state spaces.

Findings

Optimal policies can be derived from a reduced state space.

The proposed method significantly decreases computational complexity.

Simulations confirm the effectiveness of the optimal offloading strategy.

Abstract

Under a dramatic increase in mobile data traffic, a promising solution for edge computing systems to maintain their local service is the task migration that may be implemented by means of Autonomous mobile agents (AMA). In designing an optimal scheme for task offloading to AMA, we define a system cost as a minimization objective function that comprises two parts. First, an offloading cost which can be interpreted as the cost of using computational resources from the AMA. Second, a penalty cost due to potential task expiration. To minimize the expected (timeaverage) cost over a given time horizon, we formulate a Dynamic programming (DP). However, the DP Equation suffers from the well-known curse of dimensionality, which makes computations intractable, especially for infinite system state space. To reduce the computational burden, we identify three important properties of the optimal policy and show that it suffices to evaluate the DP Equation on a finite subset of the state space only. We then prove that the optimal task offloading decision at a state can be inferred from that at its adjacent states, further reducing the computational load. We present simulations to verify the theoretical results and to provide insights into the considered system.