Scheduling for resource optimisation in cloud environment: Using genetic algorithm through ahp approach

Genetic algorithms (GAs) are stochastic search algorithms inspired by the basic principles of biological evolution and natural selection. GAs simulates the evolution of living organisms, where the fittest individuals dominate over the weaker ones, by mimicking the biological mechanisms of evolution, such as selection, crossover and mutation. GAs have been successfully applied to solve optimization problems, both for continuous (whether differentiable or not) and discrete functions. The Analytic Hierarchy Process (AHP) is a theory of measurement through pair wise comparisons and relies on the judgments of experts to derive priority scales. The judgments may be inconsistent, and how to measure inconsistency and improve the judgments, when possible to obtain better consistency is a concern of the AHP. By reducing complex decisions to a series of pairwise comparisons, and then synthesizing the results, the AHP helps to capture both subjective and objective aspects of a decision. In recent years it has been observed that cloud computing has laid strong market. Recently, cloud computing emerged as the leading technology for delivering reliable, secure, fault-tolerant, sustainable, and scalable computational services, which are presented as Software, Infrastructure, or Platform as services (SaaS, IaaS, PaaS). Moreover, these services may be offered in private data centers (private clouds), may be commercially offered for clients (public clouds), or yet it is possible that both public and private clouds are combined in hybrid clouds. The provisioning of cloud resources, as to execute or carry out various tasks in critical tasks, as the number of users increases. Generally the parameters that are being taken into consideration are not always same, i.e. they may vary. Moreover, there is no specific immigration method which could be applied to the tasks which are taken into consideration. As the number of task also increases the provisioning of the virtual machines should be such that the throughput will be maximum .In order to achieve the above situation genetic algorithm and AHP are taken into consideration and applied in this paper. The paper is divided into five parts. Part I introduces various concepts that have been used in this paper. Part II consists of background knowledge and all the assumptions that were considered while applying genetic algorithm. Part III consists all the steps of genetic algorithm applied and how it is implemented in the process of executing the cloudlet. Part IV consists the final conclusion of the research where the default allocation of virtual machines is compared with genetic algorithm applied. Part V consists of various reference works used.