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Digital Library

of the European Council for Modelling and Simulation

 

Title:

Security Supportive Energy Aware Scheduling and Scaling for Cloud Environments

Authors:

Agnieszka Jakobik, Daniel Grzonka, Joanna Kolodziej

Published in:

 

 

 

(2017).ECMS 2017 Proceedings Edited by: Zita Zoltay Paprika, Péter Horák, Kata Váradi, Péter Tamás Zwierczyk, Ágnes Vidovics-Dancs, János Péter Rádics

European Council for Modeling and Simulation. doi:10.7148/2017

 

 

ISBN: 978-0-9932440-4-9/

ISBN: 978-0-9932440-5-6 (CD)

 

 

31st European Conference on Modelling and Simulation,

Budapest, Hungary, May 23rd – May 26th, 2017

 

Citation format:

Agnieszka Jakobik, Daniel Grzonka, Joanna Kolodziej (2017). Security Supportive Energy Aware Scheduling and Scaling for Cloud Environments, ECMS 2017 Proceedings Edited by: Zita Zoltay Paprika, Péter Horák, Kata Váradi, Péter Tamás Zwierczyk, Ágnes Vidovics-Dancs, János Péter Rádics European Council for Modeling and Simulation. doi: 10.7148/2017-0583

 

DOI:

https://doi.org/10.7148/2017-0583

Abstract:

Energy consumption is one of the most important problems in the era of Computational Clouds (CC). CC infrastructures must be elastic and scalable for being accessible by huge population of users in different geographical locations. It means also that CC energy utilization systems must be modern and dynamic in order to reduce the cost of using the cloud services and resources.

In this paper, we develop the novel energy saving strategies for resource allocation and task scheduling in computational clouds. We present the new energy-aware scheduling policies and methods of scaling the virtual resources. The idea of the proposed models is based on Dynamic Voltage and Frequency Scaling (DVFS) techniques of modulation of the power of microprocessors. Additionally, the proposed model enables the monitoring of the energy consumption, which is necessary for providing the scheduling under the security criterion. The efficiency of the proposed models has been justified in the simple empirical analysis. The obtained results show the need to maintain a balance between energy consumption and task schedule execution.

 

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