Dynamic Resource Allocation in Edge Computing for AI/ML Applications: Architectural Framework and Optimization Techniques

Abstract

This research article proposes a comprehensive architectural framework and optimization techniques for dynamic resource allocation in edge computing environments specifically tailored for AI/ML applications. Edge computing has emerged as a promising paradigm for handling the computational demands of AI/ML tasks by leveraging resources closer to data sources. However, effective resource allocation poses significant challenges due to the heterogeneity and dynamic nature of edge environments. In response, this paper presents a novel framework that integrates dynamic resource allocation strategies with AI/ML application requirements. The proposed framework encompasses various optimization techniques tailored to efficiently allocate resources, considering factors such as workload characteristics, resource availability, and latency constraints. Through extensive simulations and evaluations, we demonstrate the efficacy of the proposed approach in improving resource utilization, minimizing latency, and enhancing overall performance for AI/ML workloads in edge computing scenarios.