Optimization of Resource Deployment and Configuration in Hierarchical Edge Topologies
Georgios Kontos, Polyzois Soumplis, Emmanouel Varvarigos · 2024
Edge computing has consolidated as an essential technology for addressing the stringent requirements of modern applications, by distributing computing resources closer to data sources. Nonetheless, this innovation introduces significant challenges for the infrastructure designers and operators, given the high number of edge locations, the heterogeneity of edge resources and the varying requirements of today’s applications. Effective edge-network design is critical to harnessing its full potential, ensuring optimal performance, resource availability and cost efficiency during the applications’ execution. In this work, we propose mechanisms that address the challenge of joint optimal edge deployment location and capacity and device configuration, with respect to workload constraints. We formulate the respective problem as a multi-objective optimization that simultaneously considers the activation and resource costs, energy efficiency, and the workload’s experienced latency. Initially, we present the Mixed Integer Linear Programming (MILP) formulation that yields the optimal solution. To tackle its increased computational complexity, we also propose a rollout mechanism. It iteratively leverages a best-fit heuristic to perform the resource allocation and thus evaluates the impact of different deployment schemes on the overall system performance in a reinforcement learning manner. Our simulation experiments demonstrate the effectiveness of the developed mechanisms in enhancing responsiveness, reducing energy consumption and optimizing the Return On Investment (ROI) of the infrastructure across various deployment scenarios.