Near-Optimal Virtual Machine Packing Based on Resource Requirement of Service Demands Using Pattern Clustering

Upon the expansion of Cloud Computing and the positive outlook of organizations with regard to the movements towards using cloud computing and their expanding utilization of such valuable processing method, as well as the solutions provided by the cloud infrastructure providers with regard to the reduction of the costs of processing resources, the problem of organizing resources in a cloud environment gained a high importance. One of the major preoccupations of the minds of cloud infrastructure clients is their lack of knowledge on the quantity of their required processing resources in different periods of time. The managers and technicians are trying to make the most use of scalability and the flexibility of the resources in cloud computing. The main challenge is with calculating the amount of the required processing resources per moment with regard to the quantity of incoming requests of the service. Through deduction of the accurate amount of these items, one can have an accurate estimation of the requests per moment. This paper aims at introducing a model for automatic scaling of the cloud resources that would reduce the cost of renting the resources for the clients of cloud infrastructure. Thus, first we start with a thorough explanation of the proposal and the major components of the model. Then through calculating the incomings of the model through clustering and introducing the way that each of these components work in different phases, we mention the way that the required outcome which is the decision made for the number of proper virtual machines and the appropriate configuration and applying of services on virtual machines. Later, by testing through the produced data, we apply the model. Finally, we will analyze the results of testing and evaluating the model. The final quantity will be evaluated appropriately so that we could prove the accuracy of the application of the model. Keywords-Cloud computing; Resource management; VM allocation; Autoscaling; Dynamic scalability; Service selection;