Digital Library of the European Council for Modelling and Simulation

Title:	Stackelberg Game-Based Models In Energy-Aware Cloud Scheduling
Authors:	Damian Fernandez-Cerero, Alejandro Fernandez-Montes, Agnieszka Jakobik, Joanna Kolodziej
Published in:	(2018). ECMS 2018 Proceedings Edited by: Lars Nolle, Alexandra Burger, Christoph Tholen, Jens Werner, Jens Wellhausen European Council for Modeling and Simulation. doi: 10.7148/2018-0005 ISSN: 2522-2422 (ONLINE) ISSN: 2522-2414 (PRINT) ISSN: 2522-2430 (CD-ROM) 32nd European Conference on Modelling and Simulation, Wilhelmshaven, Germany, May 22^nd – May 26^5h, 2018
Citation format:	Damian Fernandez-Cerero, Alejandro Fernandez-Montes, Agnieszka Jakobik, Joanna Kolodziej (2018). Stackelberg Game-Based Models In Energy-Aware Cloud Scheduling, ECMS 2018 Proceedings Edited by: Lars Nolle, Alexandra Burger, Christoph Tholen, Jens Werner, Jens Wellhausen European Council for Modeling and Simulation. doi: 10.7148/2018-0460
DOI:	https://doi.org/10.7148/2018-0460
Abstract:	Energy-awareness remians the important problem in today’s cloud computing (CC). Optimization of the energy consumed in cloud data centers and comput-ing servers is usually related to the scheduling prob-lems. It is very difficult to define an optimal schedul-ing policy without negoative influence into the system performance and task completion time. In this work, we define a general cloud scheduling model based on a Stackelberg game with the workload scheduler and energy-efficiency agent as the main players. In this game, the aim of the scheduler is the minimization of the makespan of the workload, which is achieved by the employ of a genetic scheduling algorithm that maps the workload tasks into the computational nodes. The energy-efficiency agent selects the energy-optimization techniques based on the idea of switchin-off of the idle machines, in response to the scheduler decisions. The efficiency of the proposed model has been tested using a SCORE cloud simmulator. Obtained results show that the proposed model performs better than static energy-optimization strategies, achieving a fair balance between low energy consumption and short queue times and makespan.
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