Dynamic resource allocation in cloud infrastructure using ant lion‐based auto‐regression model

Abstract

SummaryCloud computing provides a quick, simple, and gainful means for configuring and assigning the resources for a web‐based appliance, like medical records systems, smart grid applications, and security management infrastructures. The optimization of resource allocation in the cloud is a major task for meeting customer demands and maximizing profit. This paper devises an optimization method for allocating resources in cloud infrastructures. The main contribution is to provide a framework for allocating the available resource to the deserving tasks. Here, the dynamic resource allocation is performed to allocate the resources dynamically as requested by users without affecting the system performance. Here, the ant lion‐based auto‐regressive optimization (ALAO) strategy is employed to allocate the cloud resources. ALAO is designed by integrating Ant Lion Optimizer (ALO) in auto‐regression model for further enhancing the allocation of resources, and a new fitness function is adapted, which considers certain parameters, such as cost, speed, and load. The results proved that the proposed ALAO algorithm attained enhanced performance with a maximal profit of 0.153, a minimum load of 0.028, and a minimal task assignment cost of 0.094.