Energy-Aware Dynamic Resource Allocation and Container Migration in Cloud Servers: A Co-evolution GPHH Approach
Authors: 
Mathew Falloon, Hui Ma, Aaron ChenAuthors Info & Claims
Mathew Falloon, Hui Ma, Aaron ChenAuthors Info & ClaimsPages 1219 - 1227
Abstract
Containers are a popular way of deploying software in cloud data centers. Containers are allocated to Virtual machines (VMs) which are allocated to Physical machines (PMs) within the data center. Since the resources required by containers often do not match those of VMs, where to allocate them must be decided. A poor solution can result in high energy costs. Many existing methods to solve this problem use heuristics which do not consider containers leaving the data center after being allocated. Some do consider migrating containers between VMs but few do for energy efficiency reasons. These overlooked aspects may lead to increased energy usage, particularly since studies have demonstrated that many containers run for only a brief duration. In this paper, we develop a model of the container-based cloud resource allocation problem that considers the energy impact of leaving and migrating containers. We then design a new Genetic Programming Hyper-Heuristic (GPHH) algorithm to jointly evolve three heuristics for container placement, VM placement and container migration control. We utilize newly designed terminals to ensure the effectiveness of our GPHH algorithm. Experiments have been conducted with results indicating that the heuristics evolved by our GPHH algorithm can achieve better performance compared to several state-of-the-art techniques.
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Index Terms
- Energy-Aware Dynamic Resource Allocation and Container Migration in Cloud Servers: A Co-evolution GPHH Approach
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Published: 14 July 2024
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July 14 - 18, 2024
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