• DocumentCode
    61512
  • Title

    NFRA: Generalized Network Flow-Based Resource Allocation for Hosting Centers

  • Author

    Patel, K. ; Annavaram, Murali ; Pedram, Massoud

  • Author_Institution
    Nvidia Inc., Santa Clara, CA, USA
  • Volume
    62
  • Issue
    9
  • fYear
    2013
  • fDate
    Sept. 2013
  • Firstpage
    1772
  • Lastpage
    1785
  • Abstract
    Due to prohibitive cost of data center setup and maintenance, many small-scale businesses rely on hosting centers to provide the cloud infrastructure to run their workloads. Hosting centers host services of the clients on their behalf and guarantee quality of service as defined by service level agreements (SLAs.) To reduce energy consumption and to maximize profit it is critical to optimally allocate resources to meet client SLAs. Optimal allocation is a nontrivial task due to 1) resource heterogeneity where energy consumption of a client task varies depending on the allocated resources 2) lack of energy proportionality where energy cost for a task varies based on server utilization. In this paper, we introduce a generalized Network Flow-based Resource Allocation framework, called NFRA, for energy minimization and profit maximization. NFRA provides a unified framework to model profit maximization under a wide range of SLAs. We will demonstrate the simplicity of this unified framework by deriving optimal resource allocations for three different SLAs. We derive workload demands and server energy consumption data from SPECWeb2009 benchmark results to demonstrate the efficiency of NFRA framework.
  • Keywords
    cloud computing; computer centres; contracts; energy consumption; quality of service; resource allocation; NFRA framework; SLA; SPECWeb2009 benchmark; cloud infrastructure; data center maintenance; data center setup; energy consumption reduction; energy cost; energy minimization; energy proportionality lackness; generalized network flow-based resource allocation framework; hosting centers; profit maximization; quality of service; resource allocation; resource heterogeneity; server energy consumption data; server utilization; service level agreements; small-scale business; workload demands; Energy consumption; Minimization; Resource management; Servers; Throughput; Time factors; Resource allocation; clouds; data center; energy proportionality; hosting center; network flow;
  • fLanguage
    English
  • Journal_Title
    Computers, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0018-9340
  • Type

    jour

  • DOI
    10.1109/TC.2012.253
  • Filename
    6338922