Robust Optimization Model for Probabilistic Protection With Multiple Types of Resources

• Published in: IEEE eTransactions on network and service management Vol. 18; no. 4; pp. 4711 - 4729
• Main Authors: Ito, Mitsuki, He, Fujun, Oki, Eiji
• Format: Journal Article
• Language: English
• Published: New York IEEE 01-12-2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Backup capacity allocation; Capacity planning; Central processing units; Cloud computing; cloud provider; CPUs; Failure; Genetic algorithms; Integer programming; Linear programming; Mathematical model; Mathematical models; Memory management; Mixed integer; multi-objective optimization; Multiple objective analysis; Optimization; Optimization models; Optimization techniques; Parameters; Pareto optimization; Probabilistic logic; Resource management; robust optimization; Robustness; Simulated annealing; Sorting algorithms; Statistical analysis; Survivability; Virtual environments; virtual machine
• ISSN: 1932-4537; 1932-4537
• DOI: 10.1109/TNSM.2021.3093066

This paper proposes a robust optimization model for probabilistic protection with multiple types of resources to minimize the required backup capacity for each type of resource against multiple random failures of physical machines in a cloud provider. If random failures occur, the required capacities for virtual machines are allocated to the preplanned backup physical machines, which are determined in advance. Probabilistic protection restricts the probability that the workload caused by failures exceeds the backup capacity by a given survivability parameter. We introduce three survivability parameters for central processing unit (CPU), memory, and the entire cloud provider considering both CPU and memory. By using the relationship between the three survivability parameters, the proposed model guarantees probabilistic protection for each resource, CPU and memory, and the entire cloud provider. By adopting the robust optimization technique, we formulate the proposed model as a multi-objective mixed integer linear programming problem. To deal with the multi-objective optimization problem, we apply the lexicographic weighted Tchebycheff method with which a Pareto optimal solution is obtained. We show that our proposed model reduces the average value between the backup capacity ratios of CPU and memory compared with the conventional model. A multi-objective simulated annealing (MOSA) and nondominated sorting genetic algorithm II (NSGA-II) are adopted to solve larger size problems. By using them, approximate solutions are obtained for larger size problems. In addition, we find that NSGA-II searches for solutions more effectively than MOSA, in our backup capacity allocation problem.