• DocumentCode
    3599763
  • Title

    A Communication Latency Predict-Based Method for Deploying Applications on Cloud

  • Author

    Pei Fan

  • Author_Institution
    China HuaYi Broadcasting Corp., Fuzhou, China
  • fYear
    2014
  • Firstpage
    23
  • Lastpage
    29
  • Abstract
    Nowadays, more and more applications are moving to cloud computing. How to deploy these applications optimally is a great challenge. Many cloud applications, such as scientific applications, are large-scale distributed systems that are deployed on a lot of distributed cloud nodes, and there are a lot of communications between these nodes. Therefore, taking the communication latency between cloud nodes into consideration during the deployment will better the performance of the application. However, the communication latency between nodes cannot be obtained in some scenarios, in which the communication latency monitor daemon is crashed in some cloud nodes. In this paper, we propose a communication latency predict-based method for deploying applications on cloud to address this challenge. Our method predicts the missing value of communication latency via collaborative filtering. And then the clustering-based method is applied to deploy applications on cloud based on the complete communication latency information. Comprehensive experiments are conducted by employing a well-known MPI benchmark and comparing the performance of our method with those of other methods. The experimental results show the effectiveness of our method.
  • Keywords
    cloud computing; collaborative filtering; MPI benchmark; cloud computing; cloud nodes; clustering based method; collaborative filtering; communication latency; distributed cloud nodes; large-scale distributed systems; Accuracy; Benchmark testing; Cloud computing; Collaboration; Correlation; Mathematical model; Monitoring; collaborative filtering; communication latency; deployment; prediction;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Services Computing Conference (APSCC), 2014 Asia-Pacific
  • Type

    conf

  • DOI
    10.1109/APSCC.2014.14
  • Filename
    7175490