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Prediction of resource contention in cloud using second order Markov model

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The performance of applications running on the cloud entirely depends on two factors, namely, network availability and resource management. Resource contention occurs when request for resources to a host exceeds the availability of the resources and this leads to severe performance degradation of the application. Although virtualization has reduced the performance overhead, performance loss is still possible due to resource contention between collocated virtual machines (VMs). We propose a Second Order Markov Model based Prediction of Future State of Host algorithm for predicting resource contention in hosts in the cloud and to decide on the migration of VMs from one host to another. We also propose a Contention Mitigated Placement algorithm for placing the VMs that are migrated. The main objective of our work is to predict the hosts that will contend for resources and maximize the CPU utilization by reducing the number of VM migrations. Based on the predictions, the VMs from overloaded hosts are migrated to either under loaded or normally loaded hosts such that the destination host does not become overloaded after VM migration. As VM migration from one machine to another causes latency and decrease in CPU utilization due to migration overhead, we have used the number of VM migrations as a metric to measure the performance of our proposed work. Experimental results show that the proposed algorithms improve performance by reducing the number of VM migrations.

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Correspondence to V. Mary Anita Rajam.

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Surya, K., Rajam, V.M.A. Prediction of resource contention in cloud using second order Markov model. Computing 103, 2339–2360 (2021).

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