Experimental analysis of task-based energy consumption in cloud computing systems

  • Authors:

  • Feifei Chen;John Grundy;Yun Yang;Jean-Guy Schneider;Qiang He

  • Affiliations:

  • Swinburne University of Technology, Melbourne, Australia;Swinburne University of Technology, Melbourne, Australia;Swinburne University of Technology, Melbourne, Australia;Swinburne University of Technology, Melbourne, Australia;Swinburne University of Technology, Melbourne, Australia

  • Venue:
  • Proceedings of the 4th ACM/SPEC International Conference on Performance Engineering
  • Year:
  • 2013

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Abstract

Cloud computing delivers IT solutions as a utility to users. One consequence of this model is that large cloud data centres consume large amounts of energy and produce significant carbon footprints. A common objective of cloud providers is to develop resource provisioning and management solutions that minimise energy consumption while guaranteeing Service Level Agreements (SLAs). In order to achieve this objective, a thorough understanding of energy consumption patterns in complex cloud systems is imperative. We have developed an energy consumption model for cloud computing systems. To operationalise this model, we have conducted extensive experiments to profile the energy consumption in cloud computing systems based on three types of tasks: computation-intensive, data-intensive and communication-intensive tasks. We collected fine-grained energy consumption and performance data with varying system configurations and workloads. Our experimental results show the correlation coefficients of energy consumption, system configuration and workload, as well as system performance in cloud systems. These results can be used for designing energy consumption monitors, and static or dynamic system-level energy consumption optimisation strategies for green cloud computing systems.