Advance Resource Reservation based on Context Aware Workload

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Parimal Gajre
Prof. Vivek Prasad
Dr. Madhuri Bhavsar

Abstract

Cloud computing gives the facility of provisioning resources on rent in pay as-you-go fashion, due to which resource demand changes dynamically over time. Such type of dynamic resource demand leads to mainly two types of problems such as Over-Provisioning and Under-Provisioning. The former leads to violations of Service Level Objectives (SLOs) whereas latter leads to wastage of resources, as the system is not being used to full capacity all the time. Also some cloud having limited number of resources cannot satisfy all the requests at a time. To handle such scenario advance reservation techniques are used, so that the resources available could be used efficiently with minimum possible provisioning cost and at the same time satisfying service level objectives. In the proposed technique, history of resource usage profile of tasks is maintained. For each task submitted to cloud, pattern finding technique is used a task with similar resource usage requirement from history of resource usage profile. After that check-pointing mechanism is used to monitor completion of new task based on resource usage profile of task found in history. Such type of monitoring helps in order to estimate the amount resources that will be released in short duration and based on that resources can be reserved in advance as per user’s request. Hence problem of under as well as over provisioning could be solved up to great extent at the same time meeting the Service Level Objectives. Keywords: Resource Provisioning, Check-pointing, Advance Reservation, Profiling, K-means clustering.

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