The Journal of Supercomputing

, Volume 71, Issue 3, pp 824–839 | Cite as

Resource and application-aware resource discovery in computing environments

  • Mohammad NorouziEmail author
  • Ali Jannesari


Efficient resource discovery plays a vital role in the effective management of resources and applications in heterogeneous computing environments. Therefore, the knowledge of applications’ behavior and resources’ usage pattern improves resource discovery decisions. This knowledge can be provided for the resource discovery mechanism by cooperating with the load balancing mechanism. In this paper, we formulate their cooperation by considering some parameters that represent applications’ behavior and resources’ usage patterns and extract the relation between them to introduce a formula using mathematical methods. Further, the resource discovery mechanism uses the formula to predict resources’ load before assigning them new processes and thus it prevents resource overloading which happens frequently in computing environments.


Computing environments Resource discovery Load balancing Application process behavior Resource capacity  Multivariate regression 


  1. 1.
    Tabbal A, Anderson M, Brodowicz M, Kaiser H, Sterling TL (2011) Preliminary design examination of the ParalleX System from a software and hardware perspective. In: ACM SIGMETRICS, San Jose, pp 81–87Google Scholar
  2. 2.
    Arab MN, Sharifi M (2014) A model for communication between resource discovery and load balancing units in computing environments. J Supercomput 68(3):1538–1555CrossRefGoogle Scholar
  3. 3.
    Nitzberg B, Schopf JM, Jones JP (2004) PBS Pro: grid computing and scheduling attributes. Kluwer Academic Publishers, NorwellGoogle Scholar
  4. 4.
    Meiri E, Barak A (2007) Parallel compression of correlated files. In: Proceedings of IEEE Cluster Computing, Austin, TexasGoogle Scholar
  5. 5.
    Trunfio P, Talia D, Papadakis H, Fragopoulou P, Mordacchini M, Pennanen M, Popove K, Vlassov V, Haridi S (2007) Peer-to-peer resource discovery in grids: models and systems. J Comput Syst 20(3):864–878Google Scholar
  6. 6.
    Ripeanu M, Foster I, Iamnitchi A (2002) Mapping the Gnutella Network: properties of large-scale peer-to-peer systems and implications for system design. J Internet Comput 6(1):50–57CrossRefGoogle Scholar
  7. 7.
    Talia D, Trun P, Zeng J (2007) Peer-to-peer models for resource discovery on grids. J Comput Syst 12(4):864–878Google Scholar
  8. 8.
    Arab MN, Mirtaheri SL, Khaneghah EM, Sharifi M, Mohammadkhani M (2011) Improving learning-based request forwarding in resource discovery through load-awareness. In: International Conference on Data Management in Grid and P2P Systems, Toulouse, pp 73–82Google Scholar
  9. 9.
    Dodonov E, Mello RFD (2010) Novel approach for distributed application scheduling based on prediction of communication events. Future Gener Comput Syst 26(5):740–752CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Iran University of Science and TechnologyTehranIran
  2. 2.German Research School for Simulation SciencesAachenGermany
  3. 3.RWTH Aachen UniversityAachenGermany

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