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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 22nd – May 265h, 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 dicult 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-eciency 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-eciency agent selects the energy-optimization techniques based on the idea of switchin-o of the idle machines, in response to the scheduler decisions. The eciency 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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