TY - GEN
T1 - Towards energy-aware task scheduling (EATS) framework for divisible-load applications in cloud computing infrastructure
AU - Ismail, Leila
AU - Fardoun, Abbas A.
N1 - Funding Information:
The authors would like to thank the United Arab Emirates University (UAEU) for funding this work, Fund number: 31R056.
Publisher Copyright:
© 2017 IEEE.
PY - 2017/5/26
Y1 - 2017/5/26
N2 - Cloud computing is an emerging technology which is rapidly being adopted by industries, government and academia. However, the power consumption of the underlying data center has a critical impact for its known impact on the environment and the Cloud electricity bills. Therefore, there is a need for scheduling framework in the Cloud which takes into account the optimization of the power consumption of the Cloud. In this paper, we propose an Energy-Aware Task Scheduling (EATS) cloud computing framework which is responsible to schedule users' tasks considering the energy consumption when running those tasks. This paper describes our framework, and report on workload classifications of energy consumption. The results reveal that CPU-bound applications are the most consumer of energy, and therefore should be accounted for in any framework of energy-efficient scheduling, and that strategies based on shutdowns and startups should be avoided.
AB - Cloud computing is an emerging technology which is rapidly being adopted by industries, government and academia. However, the power consumption of the underlying data center has a critical impact for its known impact on the environment and the Cloud electricity bills. Therefore, there is a need for scheduling framework in the Cloud which takes into account the optimization of the power consumption of the Cloud. In this paper, we propose an Energy-Aware Task Scheduling (EATS) cloud computing framework which is responsible to schedule users' tasks considering the energy consumption when running those tasks. This paper describes our framework, and report on workload classifications of energy consumption. The results reveal that CPU-bound applications are the most consumer of energy, and therefore should be accounted for in any framework of energy-efficient scheduling, and that strategies based on shutdowns and startups should be avoided.
KW - Cloud Computing
KW - Cloud Computing Services and Middleware
KW - Data Center
KW - Energy Efficiency
KW - Green Computing
KW - Scheduling Algorithms
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U2 - 10.1109/SYSCON.2017.7934791
DO - 10.1109/SYSCON.2017.7934791
M3 - Conference contribution
AN - SCOPUS:85021392220
T3 - 11th Annual IEEE International Systems Conference, SysCon 2017 - Proceedings
BT - 11th Annual IEEE International Systems Conference, SysCon 2017 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 11th Annual IEEE International Systems Conference, SysCon 2017
Y2 - 24 April 2017 through 27 April 2017
ER -