Energy Aware Optimal Virtual Machine Scheduling in Cloud Environment Using Hybridized Egret Swarm with Sea Lion Optimization Algorithm

Energy Aware Optimal Virtual Machine Scheduling in Cloud Environment Using Hybridized Egret Swarm with Sea Lion Optimization Algorithm

연구 분야: Cryptography

학회: Neural Processing Letters

With emergence of advanced technologies, the internet plays as the essential role to share the information across the world. With emergence of large number of users, the data quality gets affected thus; it leads to cause burden in scheduling where it provides fewer services to the users. Due to this existence, the cloud computing technology emerges to offer better services. Certainly, the optimal scheduling process of a Virtual Machine (VM) becomes challenging in the larger size of data in cloud environments. Modern systems and data centers place a high priority on energy consumption. Thus, it provides poor performance regarding energy usage and throughput analysis marked in the traditional techniques. In order to consider aforementioned issues, a novel VM scheduling mechanism is introduced for allocating and managing resources in cloud. The major scope of this developed VM scheduling process tends to minimize energy consumption. The developed Hybrid Egret Swarm-based Sea Lion Optimization (HES-SLO) algorithm is implemented to allocate resources based on several objective functions like execution time, energy or power consumption, cost, and resource utilization. Thus, the overall performance analysis takes place, where the developed VM scheduling model is compared with the traditional VM allocation mechanisms to verify the efficacy. In this validation, the HES-SLO attains 21.16%, 4.903%, 0.17%, and 7.86% for energy consumption, resource utilization, execution time, and throughput analysis. Based on the entire validation, it shows greater performance by compared with existing approaches.