Energy-Aware Resource Allocation and Container Migration in Distributed Data Centers Under Variable Energy Pricing: A Genetic Programming Hyper-Heuristic Approach

Containers have emerged as a prevalent mechanism for deploying software applications within cloud data centers, thereby abstracting many operational details from developers and transferring the responsibility of resource management to cloud service providers. These providers are continually motivated to reduce operational costs by optimizing both the placement of containers and the Virtual Machines (VMs) that host them, as efficient placement directly contributes to reduced energy expenditures. Furthermore, given that energy prices vary both temporally and geographically due to fluctuating power production, demand, and the increasing integration of renewable en-ergy sources, explicit consideration of dynamic, location-specific energy pricing is essential for informed resource allocation and container migration decisions. In this work, we propose a novel container-based cloud resource allocation model that integrates variable energy prices across multiple locations. To address this problem, we introduce an innovative Genetic Programming Hyper-Heuristic (GPHH) algorithm that concurrently evolves three heuristics for container allocation, VM placement, and container migration. A key technical novelty of our approach is the incorporation of newly designed terminals within the GPHH framework that are specifically engineered to capture and utilize the complex dynamic power pricing information. Experimental results demonstrate that our GPHH algorithm offers significant improvements over several state-of-the-art methods, thereby enhancing both cost efficiency and energy optimization in cloud environments.