Energy consumption accounts for a large percentage of the operational expenses in data centers that are used as backend computing infrastructure for cloud computing. Existing solutions for energy efficiency and job scheduling are focusing on job distribution between servers based on the computational demands, while the communication demands are ignored. This work emphases the role of communication fabric and presents a scheduling solution, named e-STAB, which takes into account traffic requirements of cloud applications providing energy efficient job allocation and traffic load balancing in data center networks. Effective distribution of network traffic improves quality of service of running cloud applications by reducing the communication-related delays and congestion-related packet losses. The validation results, obtained from the GreenCloud simulator, underline benefits and efficiency of the proposed scheduling methodology.

e-STAB: Energy-Efficient Scheduling for Cloud Computing Applications with Traffic Load Balancing

Kliazovich, Dzmitry;S. T. Arzo;Granelli, Fabrizio;
2013

Abstract

Energy consumption accounts for a large percentage of the operational expenses in data centers that are used as backend computing infrastructure for cloud computing. Existing solutions for energy efficiency and job scheduling are focusing on job distribution between servers based on the computational demands, while the communication demands are ignored. This work emphases the role of communication fabric and presents a scheduling solution, named e-STAB, which takes into account traffic requirements of cloud applications providing energy efficient job allocation and traffic load balancing in data center networks. Effective distribution of network traffic improves quality of service of running cloud applications by reducing the communication-related delays and congestion-related packet losses. The validation results, obtained from the GreenCloud simulator, underline benefits and efficiency of the proposed scheduling methodology.
2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing
Piscataway, USA
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC,
9780769550466
Kliazovich, Dzmitry; Arzo, S. T.; Granelli, Fabrizio; Bouvry, P.; Ullah Khan, S.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11572/33595
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