Optimizing Cloud Resource Allocation: A Green Approach with Enhanced Dragonfly Algorithm for VM Selection
Keywords:
Cloud Computing, Quality of Service, Physical Machines, Virtual Machines, MigrationAbstract
To provide dependable and scalable computational capacity, cloud computing is popular among businesses, academics, and the government. In cloud data centers, virtual and physical equipment are connected by high-speed networks. Virtualization helps cloud service providers manage resources more efficiently, however poorly optimized and inefficient services hurt the system's performance. Physical Machines (PMs), Virtual Machines (VMs), and the allocation and migration strategy of the VMs over the PMs are all components of the cloud computing scheduling architecture. The research offers a unique behavior of VM selection from overutilized PM using Swarm intelligence. The overutilized PMs receive a few migrations. With other state-of-the-art optimization algorithms from the same series, the suggested algorithm design is evaluated. In order to support flexibility, the evaluation was conducted based on Quality of Service (QoS) characteristics including Service Level Agreement (SLA) violation and energy usage. The results are described along with examples of how the suggested approach significantly outperformed previous strategies in terms of QoS.
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