Graph Neural Networks and Reinforcement Learning for Proactive Application Image Placement

TL;DR

This paper introduces a proactive application image placement method for Edge computing using Graph Neural Networks and Reinforcement Learning, aiming to optimize placement decisions in dynamic, heterogeneous environments.

Contribution

It presents a novel combination of Graph Neural Networks and actor-critic Reinforcement Learning for proactive image placement in Edge computing systems.

Findings

The approach achieves superior placement outcomes compared to other solutions.

It may lead to longer execution times in some scenarios.

The method effectively manages resource heterogeneity and system dynamics.

Abstract

The shift from Cloud Computing to a Cloud-Edge continuum presents new opportunities and challenges for data-intensive and interactive applications. Edge computing has garnered a lot of attention from both industry and academia in recent years, emerging as a key enabler for meeting the increasingly strict demands of Next Generation applications. In Edge computing the computations are placed closer to the end-users, to facilitate low-latency and high-bandwidth applications and services. However, the distributed, dynamic, and heterogeneous nature of Edge computing, presents a significant challenge for service placement. A critical aspect of Edge computing involves managing the placement of applications within the network system to minimize each application's runtime, considering the resources available on system devices and the capabilities of the system's network. The placement of application images must be proactively planned to minimize image tranfer time, and meet the strict demands of the applications. In this regard, this paper proposes an approach for proactive image placement that combines Graph Neural Networks and actor-critic Reinforcement Learning, which is evaluated empirically and compared against various solutions. The findings indicate that although the proposed approach may result in longer execution times in certain scenarios, it consistently achieves superior outcomes in terms of application placement.