<abstract xmlns="http://www.w3.org/1999/xhtml"><p>We present in this paper a novel distributed solution to a security-aware job scheduling problem in cloud computing infrastructures. We assume that the assignment of the available resources is governed exclusively by the specialized brokers assigned to individual users submitting their jobs to the system. The goal of this scheme is allocating a limited quantity of resources to a specific number of jobs minimizing their execution failure probability and total completion time. Our approach is based on the Pareto dominance relationship and implemented at an individual user level. To select the best scheduling strategies from the resulting Pareto frontiers and construct a global scheduling solution, we developed a decision-making mechanism based on the game-theoretic model of <italic>Spatial Prisoner’s Dilemma</italic>, realized by selfish agents operating in the two-dimensional cellular automata space. Their behavior is conditioned by the objectives of the various entities involved in the scheduling process and driven towards a Nash equilibrium solution by the employed social welfare criteria. The performance of the scheduler applied is verified by a number of numerical experiments. The related results show the effectiveness and scalability of the scheme in the presence of a large number of jobs and resources involved in the scheduling process.</p></abstract>

We present in this paper a novel distributed solution to a security-aware job scheduling problem in cloud computing infrastructures. We assume that the assignment of the available resources is governed exclusively by the specialized brokers assigned to individual users submitting their jobs to the system. The goal of this scheme is allocating a limited quantity of resources to a specific number of jobs minimizing their execution failure probability and total completion time. Our approach is based on the Pareto dominance relationship and implemented at an individual user level. To select the best scheduling strategies from the resulting Pareto frontiers and construct a global scheduling solution, we developed a decision-making mechanism based on the game-theoretic model of Spatial Prisoner’s Dilemma, realized by selfish agents operating in the two-dimensional cellular automata space. Their behavior is conditioned by the objectives of the various entities involved in the scheduling process and driven towards a Nash equilibrium solution by the employed social welfare criteria. The performance of the scheduler applied is verified by a number of numerical experiments. The related results show the effectiveness and scalability of the scheme in the presence of a large number of jobs and resources involved in the scheduling process.

Decentralized Job Scheduling in the Cloud Based on a Spatially Generalized <italic>Prisoner’s Dilemma</italic> Game

Department of Mathematics and Natural Sciences Cardinal Stefan Wyszyński University, Wóycickiego 1/3, 01-938 Warsaw, Poland

International Journal of Applied Mathematics and Computer Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Decentralized Job Scheduling in the Cloud Based on a Spatially Generalized  Game

We present in this paper a novel distributed solution to a security-aware job scheduling problem in cloud computing infrastructures. We assume that the...

Decentralized Job Scheduling in the Cloud Based on a Spatially Generalized Prisoner’s Dilemma Game

Online veröffentlicht: 30. Dez. 2015

Seitenbereich: 737 - 751

Eingereicht: 11. Apr. 2014

DOI: https://doi.org/10.1515/amcs-2015-0053

Schlüsselwörter
job scheduling, multiobjective optimization, genetic algorithm, cellular automata

© 2015 Jakub Gąsior et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Decentralized Job Scheduling in the Cloud Based on a Spatially Generalized Prisoner’s Dilemma Game

Online veröffentlicht: 30. Dez. 2015

Seitenbereich: 737 - 751

Eingereicht: 11. Apr. 2014

DOI: https://doi.org/10.1515/amcs-2015-0053

Schlüsselwörterjob scheduling, multiobjective optimization, genetic algorithm, cellular automata

© 2015 Jakub Gąsior et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
job scheduling, multiobjective optimization, genetic algorithm, cellular automata