Stackelberg game-based models in energy-aware cloud scheduling / Damián Fernández-Cerero, Alejandro Fernández-Montes, Agnieszka Jakóbik, Joanna Kołodziej // W: 32nd European Conference on Modelling ... więcej

Stackelberg game-based models in energy-aware cloud scheduling / Damián Fernández-Cerero, Alejandro Fernández-Montes, Agnieszka Jakóbik, Joanna Kołodziej // W: 32nd European Conference on Modelling and Simulation ECMS 2018, May 22nd - May 25th, 2018, Wilhelmshaven, Germany : proceedings [Dokument elektroniczny] / ed. by Lars Nolle [et al.] ; org. by ECMS - European Council for Modelling and Simulation. – Sbr.-Dudweiler : European Council for Modelling and Simulation, 2018. – (European Conference for Modelling and Simulation, ISSN 2522-2422). – S. 460-467 : schem., tab. – Tryb dostępu: http://www.scs-europe.net/dlib/2018/2018-0460.htm. – Bibliogr. 24 poz., Abstr. – doi: 10.7148/2018-0460. – ISBN 978-0-9932440-6-3 (print). – ISBN 978-0-9932440-7-0 (CD)

Energy-awareness remians the important problem in today’s cloud computing (CC). Optimization of the energy consumed in cloud data centers and computing servers is usually related to the scheduling problems. ... więcej

Energy-awareness remians the important problem in today’s cloud computing (CC). Optimization of the energy consumed in cloud data centers and computing servers is usually related to the scheduling problems. It is very difficult to define an optimal scheduling 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.