Ant Lion Optimization Algorithm (ALOA) Based Secure Vm Allocation Strategy for Protecting Against Co-Residence Attack in Energy Efficient Cloud

Abstract

Virtual machines (VMs) with fluctuating resource requirements are hosted on servers with various configurations, which constitute the bulk of cloud computing. Furthermore, in a networked environment, co-located VMs are susceptible to co-resident attacks (CRA). Degradation in security and performance may result from these two problems, which can also lead to assaults on the service and inconsistent utilization of resources on the server. In this research proposes a safe, workload-balanced, and energy-effective virtual machine allocation technique for preventing such co-residence attacks. To be more precise, formulate the issue as an optimization problem and evaluate and minimize three main aspects: (1) power consumption, (2) security threats, and (3) uneven workloads across several physical servers. Additionally, this research assumes a random number of VMs from various users coming at random times, which necessitates a continuously changing optimization approach in order to account for a real environmental setting. clustering VMs suggest in time windows initially, as the optimization issue is NP-hard. Then, utilize the Ant Lion Optimization Algorithm (ALOA) to determine the best allocation approach for each time window. Extensive findings derived from actual cloud trails confirm the efficacy of the suggested plan.