Efficient Auto-Scaling Mechanism in the Cloud Environment Using Proactive Approach

Cloud computing

Cloud computing is latest emerging technology for large scale distributed computing and parallel computing. Cloud computing gives large pool of shared resources, information, packets at any instances of time. Auto-scaling is the strategy that has ability to adjust the available resources to meet the user demands. To facilitate users with availability of resources seamlessly. Auto scaling is cloud computing feature that allows users to automatically scale cloud services, like virtual machine and server capacities, up or down, depending on user on-demand. Proactive auto-scaling mechanisms predict the workload ahead such that the auto-scaler can make decision based on the expected workload instead of waiting for trigger. Proactive auto-scaling mechanism is efficient then reactive auto-scaling because in reactive auto-scaling approach, the auto-scaling decision would be triggered by a predefined set of events. In current cloud computing environment, management of data reliability has become a challenge. For data-intensive scientific applications, storing data in the cloud with the typical 3-replica replication strategy for managing the data reliability would incur huge storage cost. In this paper we work on machine learning base effective approach for auto-scaling mechanism in these systems we work on design effective approach for automatically scale the capacity of cloud. We also consider machine learning concept for selecting appropriate node and scale according. Work on parameters like QOS, Accuracy, efficiency etc.

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