Dynamic Virtual Machine Consolidation for Energy Efficiency in OpenStack-based Cloud
Shrinidhi Alur, Sagar Kanamadi, Surabhi Naik, Suyash Kamat, D. G. Narayan · 2023
The rapid growth of cloud computing demands efficient resource allocation to maximize performance while minimizing energy consumption. Scalability improves, and up-front expenditures for IT infrastructure are decreased due to cloud computing. As the number of virtual machines (VMs) in a cloud environment grows, guaranteeing appropriate resource allocation and utilization becomes a difficult issue. Inefficient resource allocation can result in underutilized resources, energy waste, and increased operational expenses. Our research aimed to contribute to this objective by proposing a dynamic consolidation approach. We evaluated existing consolidation methods and identified their shortcomings. Many earlier methods lacked the adaptability and flexibility necessary to optimize energy use. We presented a consolidation technique that takes the hosts’ and VMs’ computing factors into account to address these flaws. The modules in this technique generate diverse and realistic environments for hosts and VMs, allowing for comprehensive evaluation. The Energy-Efficient Consolidation module serves as the core component, utilizing algorithms to optimize migration decisions. The technique effectively migrated virtual machines (VMs) from underutilized hosts to more effective ones, resulting in energy savings of 27% and 35% in the two scenarios, respectively. These results show how our method may help create cloud computing environments that are more environmentally friendly and energy-efficient. The implementation of our strategy in a real-time Multi-Node OpenStack testbed adds practicality and relevance to our findings.