An Artificial Intelligence (AI) based Energy Efficient and Secured Virtual Machine Allocation Model in Cloud

Ensuring security in cloud systems is one of the most significant tasks for users facing security problems in load balancing. Many conventional works have developed secured load balancing models for allocating Virtual Machines (VMs) in cloud systems. Still, it limits by the problems of high migration overhead, increased processing time, storage complexity, high energy consumption, and lack of security. So, the proposed work objects to implement an energy-efficient and secured Artificial Intelligence (AI) based VM allocation model for cloud load balancing. The novel contribution of this work is to develop an Intelligent Mine Blow Optimization (IBMO) technique to securely allocate the VMs in the cloud. Also, it helps to mitigate the security risks created by the attacks of VMs from multiple users. Moreover, it maintains the workloads among all VMs in the cloud server. The primary advantages of this work are reduced energy consumption, resource consumption, and overhead. During analysis, the performance and results of the proposed IBMO-based VM allocation model are validated and compared using various evaluation indicators.