Online Container Scheduling for Low-Latency IoT Services in Edge Cluster Upgrade: A Reinforcement Learning Approach

TL;DR

This paper introduces a reinforcement learning-based method for scheduling containers during edge cluster upgrades in IoT services, significantly reducing task latency in mobile edge computing environments.

Contribution

It formulates the online container scheduling problem for edge cluster upgrades and proposes a novel RL algorithm tailored for MEC, improving latency performance.

Findings

Reduces total task latency by approximately 27%

Demonstrates effectiveness of RL in MEC container scheduling

Addresses unique MEC characteristics in scheduling policy

Abstract

In Mobile Edge Computing (MEC), Internet of Things (IoT) devices offload computationally-intensive tasks to edge nodes, where they are executed within containers, reducing the reliance on centralized cloud infrastructure. Frequent upgrades are essential to maintain the efficient and secure operation of edge clusters. However, traditional cloud cluster upgrade strategies are ill-suited for edge clusters due to their geographically distributed nature and resource limitations. Therefore, it is crucial to properly schedule containers and upgrade edge clusters to minimize the impact on running tasks. In this paper, we propose a low-latency container scheduling algorithm for edge cluster upgrades. Specifically: 1) We formulate the online container scheduling problem for edge cluster upgrade to minimize the total task latency. 2) We propose a policy gradient-based reinforcement learning algorithm to address this problem, considering the unique characteristics of MEC. 3) Experimental results demonstrate that our algorithm reduces total task latency by approximately 27\% compared to baseline algorithms.