Minimizing Age of Information for Mobile Edge Computing Systems: A Nested Index Approach

TL;DR

This paper introduces a nested index approach to minimize Age-of-Information in mobile edge computing systems with heterogeneous devices, effectively balancing computation complexity and accuracy.

Contribution

It develops a hierarchical MDP and nested index policy for AoI minimization in MEC, achieving asymptotic optimality and reducing the optimality gap by up to 40%.

Findings

Reduces AoI effectively in heterogeneous MEC systems.

Achieves up to 40% reduction in optimality gap.

Asymptotically approaches the theoretical lower bound.

Abstract

Exploiting the computational heterogeneity of mobile devices and edge nodes, mobile edge computation (MEC) provides an efficient approach to achieving real-time applications that are sensitive to information freshness, by offloading tasks from mobile devices to edge nodes. We use the metric Age-of-Information (AoI) to evaluate information freshness. An efficient solution to minimize the AoI for the MEC system with multiple users is non-trivial to obtain due to the random computing time. In this paper, we consider multiple users offloading tasks to heterogeneous edge servers in a MEC system. We first reformulate the problem as a Restless Multi-Arm-Bandit (RMAB) problem and establish a hierarchical Markov Decision Process (MDP) to characterize the updating of AoI for the MEC system. Based on the hierarchical MDP, we propose a nested index framework and design a nested index policy with provably asymptotic optimality. Finally, the closed form of the nested index is obtained, which enables the performance tradeoffs between computation complexity and accuracy. Our algorithm leads to an optimality gap reduction of up to 40%, compared to benchmarks. Our algorithm asymptotically approximates the lower bound as the system scalar gets large enough.