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Cloud computing is a computing technology that is expeditiously evolving. Cloud is a type of distributed computing system that provides a scalable computational resource on demand including storage, processing power and applications as a service via Internet. Cloud computing, with the assistance of virtualization, allows for transparent data and service sharing across cloud users, as well as access to thousands of machines in a single event. Virtual machine (VM) allocation is a difficult job in virtualization that is governed as an important aspect of VM migration. This process is performed to discover the optimum way to place VMs on physical machines (PMs) since it has clear implications for resource usage, energy efficiency, and performance of several applications, among other things. Hence an efficient VM placement problem is required. This paper presents a VM allocation technique based on the elephant herd optimization scheme. The proposed method is evaluated using real-time workload traces and the empirical results show that the proposed method reduces energy consumption, and maximizes resource utilization when compared to the existing methods. [ABSTRACT FROM AUTHOR]

Cloud is a pay-per use infra-structed which has invited huge clients to cloud, in order to get reliable services without extra maintenance or infrastructure cost. Growing cloud services and migration of small business to cloud have led to high load on cloud service providers, which leads to the need of better optimization algorithm in order to manage the machine better performance and meet better quality of services to the client. Cloud broker or agent plays an important role to achieve this using intelligent task scheduling algorithm to manage the task in such a way to optimize the performance of the cloud services and data center. Currently various optimization algorithms are proposed but most of them take execution time into consideration but not the network delay between the client and the data center. Hence, to overcome this, an optimization algorithm is proposed in this work using execution time and network delay as the optimization parameters. The nature inspired grasshopper optimization is proposed which is compared with the exiting PSO and ACO models to study the performance. The results show that the proposed algorithm out performs the existing models with execution time, total time and network delay as performance metrics. It demonstrates how the suggested, naturally inspired GOA algorithm beats the existing ACO and PSO algorithms for task scheduling in the cloud with scaling loads requiring 5 virtual machines and 2 data centers. More objective functions, such as power and cost-effective algorithms, can be added to the work to further expand it. This study compares the efficacy of several algorithms based on the predetermined criteria while also examining related algorithms. To determine the best algorithm possible, it is intended to offer each approach individually, analyze the results, and plot the resulting graphs. [ABSTRACT FROM AUTHOR]