• DocumentCode
    3706541
  • Title

    Sheriff: A Regional Pre-alert Management Scheme in Data Center Networks

  • Author

    Xiaofeng Gao;Wen Xu;Fan Wu;Guihai Chen

  • Author_Institution
    Dept. of Comput. Sci. &
  • fYear
    2015
  • Firstpage
    669
  • Lastpage
    678
  • Abstract
    As the base infrastructure to support various cloud services, data center draws more and more attractions from both academia and industry. A stable, effective, and robust data center network (DCN) management system is urgently required from institutions and corporations. However, existing management schemes have several problems, including the difficulty to manage the entire network with heterogeneous network components by a centralized controller, and the short-sighted mechanism to deal with resource allocation, congestion control, and VM migration. In this paper, we design Sheriff: a distributed pre-alert and management scheme for DCN management. Sheriff is a regional self-automatic control scheme at end host side to balance network traffic and workload. It includes two phases: prediction and management. Each end-host predicts possible overload and congestion by prediction strategy based on ARIMA and Neural Network methodology, and perform an Alert message. Delegated local controllers then monitor their dominating region and activate localized protocols VmMigration to manage the network. We illustrate the predication accuracy by network traces from a local data center service provider, examine the management efficiency by simulations on both Fat-Tree topology and Bcube topology, and prove that VmMigration is an approximation with ratio 3+2/p where p is a constant predefined in local search algorithm. Both numerical simulations and theoretical analysis validate the efficiency of our design. In all, Sheriff is a fast and effective scheme to better improve the performance of DCN.
  • Keywords
    "Conferences","Parallel processing"
  • Publisher
    ieee
  • Conference_Titel
    Parallel Processing (ICPP), 2015 44th International Conference on
  • ISSN
    0190-3918
  • Type

    conf

  • DOI
    10.1109/ICPP.2015.76
  • Filename
    7349622