Exponentially‐spider monkey optimization based allocation of resource in cloud

Abstract

The increasing desire for distributed computing systems has attracted huge interest in memory and computing resources. The cloud provides on-demand access to provide a flexible allocation of resources for reliable services. Therefore, there should be a provision in which resources are accessible to request users to satisfy user needs. In classical techniques, the allocation of resources by satisfying power and Quality-of-Services is a challenging aspect. This paper devises a novel technique for optimal resource allocation, namely, Exponentially Spider Monkey Optimization (E-SMO). Here, the proposed E-SMO is devised by combining Exponential Weighted Moving Average and Spider Monkey Optimization (SMO). Besides, the fitness function is newly devised considering resource utilization and resource cost. After that, the cloud resources employ a switching strategy to reduce power consumption to prevent the switching of redundant servers. For optimal switching, the proposed E-SMO is utilized with other fitness factors that compute the number of applications assigned in the physical machine using the switching state is in OFF condition. Thus, the server switching model is incorporated to activate or deactivate the server when not in use for effective resources utilization. The proposed E-SMO algorithm outperformed other methods with the maximal resource utilization of 0.874, the minimal resource cost of 0.302, and the minimal power usage of 0.275.