Timely Offloading in Mobile Edge Cloud Systems

TL;DR

This paper investigates optimal scheduling policies for timely status updates in mobile edge cloud systems, balancing freshness of information with MEC usage costs using age of information metrics.

Contribution

It formulates the update scheduling as an MDP and proves the optimal policy has an age-threshold structure based on update service duration.

Findings

Optimal scheduling policy has an age-threshold structure.

The policy balances update timeliness and MEC usage.

The problem is modeled as an infinite horizon MDP.

Abstract

Future real-time applications like smart cities will use complex Machine Learning (ML) models for a variety of tasks. Timely status information is required for these applications to be reliable. Offloading computation to a mobile edge cloud (MEC) can reduce the completion time of these tasks. However, using the MEC may come at a cost such as related to use of a cloud service or privacy. In this paper, we consider a source that generates time-stamped status updates for delivery to a monitor after processing by the mobile device or MEC. We study how a scheduler must forward these updates to achieve timely updates at the monitor but also limit MEC usage. We measure timeliness at the monitor using the age of information (AoI) metric. We formulate this problem as an infinite horizon Markov decision process (MDP) with an average cost criterion. We prove that an optimal scheduling policy has an age-threshold structure that depends on how long an update has been in service.