Graph Neural Networks and Reinforcement Learning for Proactive Application Image Placement
arxiv(2024)
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.
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