Cloud computing allows users to devise cost-effectivesolutions for deploying their applications. Nevertheless, the deci-sions about resource provisioning are very challenging becauseworkloads are seriously affected by the uncertainty of cloudperformance and their characteristics vary. In this paper weaddress these issues by explicitly modeling workload and clouduncertainty in the decision process. For this purpose, we adopt aprobabilistic formulation of the optimization problem aimed atminimizing the expected cost for deploying a parallel applicationunder a deadline constraint. To find a sub-optimal solutionof the problem we apply a Genetic Algorithm. By tuning itsparameters we are able to assess their role and their impact onthe effectiveness and efficiency of the algorithm for provisioningand scheduling in uncertain cloud environments.

Carla Calzarossa, M., Massari, L., Della Vedova, M. L., Nebbione, G., Tessera, D., Tuning Genetic Algorithms for resource provisioning and scheduling in uncertain cloud environments: Challenges and findings, in Proc. 27th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing - PDP, (Italia, 13-February 15-March 2019), IEEE, NEW YORK -- USA 2019:2019 174-180. [10.1109/EMPDP.2019.8671564] [http://hdl.handle.net/10807/132271]

Tuning Genetic Algorithms for resource provisioning and scheduling in uncertain cloud environments: Challenges and findings

Massari, Luisa
Membro del Collaboration Group
;
Della Vedova, Marco Luigi
Membro del Collaboration Group
;
Tessera, Daniele
Membro del Collaboration Group
2019

Abstract

Cloud computing allows users to devise cost-effectivesolutions for deploying their applications. Nevertheless, the deci-sions about resource provisioning are very challenging becauseworkloads are seriously affected by the uncertainty of cloudperformance and their characteristics vary. In this paper weaddress these issues by explicitly modeling workload and clouduncertainty in the decision process. For this purpose, we adopt aprobabilistic formulation of the optimization problem aimed atminimizing the expected cost for deploying a parallel applicationunder a deadline constraint. To find a sub-optimal solutionof the problem we apply a Genetic Algorithm. By tuning itsparameters we are able to assess their role and their impact onthe effectiveness and efficiency of the algorithm for provisioningand scheduling in uncertain cloud environments.
2019
Inglese
Proc. 27th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing - PDP
2019 27th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP)
Italia
13-feb-2019
15-mar-2019
978-1-7281-1644-0
IEEE
Carla Calzarossa, M., Massari, L., Della Vedova, M. L., Nebbione, G., Tessera, D., Tuning Genetic Algorithms for resource provisioning and scheduling in uncertain cloud environments: Challenges and findings, in Proc. 27th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing - PDP, (Italia, 13-February 15-March 2019), IEEE, NEW YORK -- USA 2019:2019 174-180. [10.1109/EMPDP.2019.8671564] [http://hdl.handle.net/10807/132271]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10807/132271
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