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Energy-aware job scheduler for high-performance computing

  • Olli Mämmelä*
  • , Mikko Majanen
  • , Robert Basmadjian
  • , Herman De Meer
  • , Andre Giesler
  • , Willi Homberg
  • *Corresponding author for this work

    Research output: Contribution to journalArticleScientificpeer-review

    Abstract

    In recent years energy-aware computing has become a major topic, not only in wireless and mobile devices but also in devices using wired technology. The ICT industry is consuming an increasing amount of energy and a large part of the consumption is generated by large-scale data centers. In High-Performance Computing (HPC) data centers, higher performance equals higher energy consumption. This has created incentives on exploring several alternatives to reduce the energy consumption of the system, such as energy-efficient hardware or the Dynamic Voltage and Frequency Scaling (DVFS) technique. This work presents an energy-aware scheduler that can be applied to a HPC data center without any changes in hardware. The scheduler is evaluated with a simulation model and a real-world HPC testbed. Our experiments indicate that the scheduler is able to reduce the energy consumption by 6–16% depending on the job workload. More importantly, there is no significant slowdown in the turnaround time or increase in the wait time of the job. The results hereby evidence that our approach can be beneficial for HPC data center operators without a large penalty on service level agreements.
    Original languageEnglish
    Pages (from-to)265-275
    JournalComputer Science: Research and Development
    Volume27
    Issue number4
    DOIs
    Publication statusPublished - 2011
    MoE publication typeA1 Journal article-refereed

    UN SDGs

    This output contributes to the following UN Sustainable Development Goals (SDGs)

    1. SDG 7 - Affordable and Clean Energy
      SDG 7 Affordable and Clean Energy
    2. SDG 9 - Industry, Innovation, and Infrastructure
      SDG 9 Industry, Innovation, and Infrastructure

    Keywords

    • Energy-efficiency
    • HPC
    • power consumption
    • scheduling
    • simulation
    • testbed

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