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Title: k-TIER AND SELECTIVE BACKFILLING APPROACH FOR PARALLEL WORKLOAD SCHEDULING IN CLOUD

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Title:
k-TIER AND SELECTIVE BACKFILLING APPROACH FOR PARALLEL WORKLOAD SCHEDULING IN CLOUD
Author Name:
Shahabanath K K, Sreekesh Namboodiri T
Abstract:
Cloud computing is known as a provider of very large scalable dynamic services and virtualized resources over the Internet. Job scheduling is most important task in cloud computing environment because user have to pay for used resources based upon time. The main goal of scheduling is distribute the load among processors and maximizing their utilization by minimizing the total task execution time and also maintaining the level of responsiveness of parallel jobs. Existing parallel scheduling mechanisms have some drawbacks such as context switching rate, large waiting times and large response time. There exists two-tier priority based consolidation methods : conservative migration and consolidation supported backfilling and aggressive migration and consolidation supported backfilling. In this method, partition the computing capacity of each node into two tiers, the foreground virtual machine (VM) tier (with high CPU priority) and the background VM tier (with low CPU priority). In this paper propose a new scheduling method which is better than both conservative and aggressive backfilling. This method provide reservation selectively, only to the jobs that have waited long enough in the queue. Also divide the computing capacity into k-tier. There will be one foreground VM and number of background VMs.
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