• DocumentCode
    701750
  • Title

    Energy efficient rescheduling algorithm for High Performance Computing

  • Author

    Chauhan, Manisha ; Parveen, Nazia ; Saurav, Sumit Kumar ; Ganga Prasad, G.L.

  • Author_Institution
    Opp.HAL Aeroengine Div., Centre for Dev. of Adv. Comput., Bangalore, India
  • fYear
    2015
  • fDate
    19-20 Feb. 2015
  • Firstpage
    1
  • Lastpage
    7
  • Abstract
    With the augmentation of High Performance Computing (HPC) system and power consumption pattern, energy optimization has become an important concern. Several surveys indicate that the energy utilized in computation and communication within a HPC system contributes considerably to their operational costs. The proposed energy efficient rescheduling algorithm aims to reduce energy consumption in HPC. This algorithm is based on energy efficient rescheduling of a job which considers the optimal operating point (OOP) i.e. voltage and frequency along with resource matching constraint to achieve the target performance. Performance -Energy-Time (PET) matrix for every application is built and stored in knowledge base (KB) in order to devise its OOP. The devised OOP gives minimal energy consumption at target execution environment. The algorithm is useful for energy optimization for cluster environment. Earlier works exploited process migration technique to address load balancing aspect. The proposed algorithm exploits process migration and reschedules the job dynamically with its OOP in context of energy reduction.
  • Keywords
    knowledge based systems; parallel processing; power aware computing; resource allocation; HPC system; KB; OOP; PET matrix; cluster environment; dynamic job rescheduling; energy consumption reduction; energy efficient job rescheduling algorithm; energy optimization; high-performance computing; knowledge base; load balancing; minimal energy consumption; operational costs; optimal operating point; performance-energy-time matrix; power consumption pattern; process migration technique; resource matching constraint; target execution environment; target performance; Algorithm design and analysis; Clustering algorithms; Energy consumption; Energy efficiency; Optimization; Positron emission tomography; Power demand; Energy Efficient Rescheduling Algorithm; Energy Optimization; High Performance Computing; Knowledge Base; Process Migration;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Parallel Computing Technologies (PARCOMPTECH), 2015 National Conference on
  • Conference_Location
    Bangalore
  • Print_ISBN
    978-1-4799-6916-6
  • Type

    conf

  • DOI
    10.1109/PARCOMPTECH.2015.7084521
  • Filename
    7084521