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Cloud computing is a trending topic in the field of science and technology since the internet dependent services have been growing rapidly. In this environment, there are a lot of immense infrastructures and resources to satisfy the internet users. When a large number of service requests reach at a particular time, load balancing becomes a necessity. Load balancing involves the effective migration of the resources from the loaded physical machine to the other physical machine. For the effective migration, a method named Modified Exponential Gravitational Search Algorithm based on Virtual Machine Migration strategy (MEGSA-VMM) has been proposed that uses the gravitational concepts for performing the frequency-based velocity computations. MEGSA algorithm is the integration of the gravitational search algorithm and exponential weighted moving average theory. Also, the qualityof-Service (QoS) constraints considered for VM migration are migration cost, migration time, resource usage and energy. Simulation of the proposed method and the comparison of the results obtained, with the traditional methods like Ant-Colony Optimization (ACO), Gravitational Search Algorithm (GSA) and Exponential Gravitational Search Algorithm (EGSA) is performed. The proposed method is found to achieve an optimum migration with a minimum energy at a rate of 0.26 and minimum migration cost at a rate of 0.015.

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

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