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Robust Optimization Model for Backup Resource Allocation in Cloud Provider | IEEE Conference Publication | IEEE Xplore

Robust Optimization Model for Backup Resource Allocation in Cloud Provider


Abstract:

This paper proposes a backup resource allocation model that provides a probabilistic protection for primary physical machines in a cloud provider to minimize the required...Show More

Abstract:

This paper proposes a backup resource allocation model that provides a probabilistic protection for primary physical machines in a cloud provider to minimize the required total capacity. When any random failure occurs, workloads are transferred to preplanned and dedicated backup physical machines for prompt recovery. In the proposed model, a probabilistic protection guarantee is introduced to prevent the cloud provider from capacity overbooking. We apply robust optimization in our model to formulate the backup resource allocation problem as an integer linear programming problem. A simulated annealing heuristic is adopted to solve the same optimization problem when the cloud provider is large. Finally, the results reveal that the required backup capacity depends on the reliability of primary physical machines. Specifically, the more the resources in primary physical machines share backup capacity when the failure probabilities of primary physical machines are sufficiently small, the less capacity is required for backup resource allocation.
Date of Conference: 20-24 May 2018
Date Added to IEEE Xplore: 30 July 2018
ISBN Information:
Electronic ISSN: 1938-1883
Conference Location: Kansas City, MO, USA

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