2013 · 88 citations · 20 references
ComputingCloud ComputingComputer ScienceCloud ArchitectureCloud InfrastructureCloud Performance OptimizationCloud Resource ManagementCloud Traffic EngineeringCompute Resource ManagementEnergy-efficient Cloud ComputingJob SchedulingResource SchedulingCloud Computing ResourcesCloud Load BalancingCloud SchedulingEnergy-efficient Load BalancingEnergy-efficient Resource AllocationGreen Cloud ComputingNetwork Traffic ModelingTraffic Load Balancing
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 Green Cloud simulator, underline benefits and efficiency of the proposed scheduling methodology.
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