Optimal Allocation of IaaS Cloud Resources through Enhanced Moth Flame Optimization (EMFO) Algorithm

Abstract

A new generation of computing resources is available to customers via IaaS, PaaS, and SaaS administrations, making cloud computing the most significant innovation in recent history for the general public. A virtual machine (VM) is configured, started, and maintained across numerous physical hosts using IaaS. In many cases, cloud providers (CPs) charge utility customers who have registered their premises with the utility registration authorities. Given the opposing aims of increas-ing customer demand fulfillment while decreasing costs and optimizing asset efficiency, efficient VM allocation is generally considered as one of the most difficult tasks for CPs to overcome. This paper proposes the Enhanced Moth Flame Optimization (EMFO) algorithm to provide a unique strategy for assigning virtual machines to suit customer requirements. The recommended approach is applied on Amazon’s EC2 after three distinct experiments are assumed. The utility of the proposed method is further shown by the use of well-known optimization techniques for effective VM allocation. The app was created using a Java-based programming language and then run on the Netbeans IDE 12.4 platform.