IDEAS home Printed from https://ideas.repec.org/a/wsi/jikmxx/v15y2016i02ns0219649216500209.html
   My bibliography  Save this article

A Proximity and Semantic-Aware Optimisation Model for Sub-Domain-Based Decentralised Resource Discovery in Grid Computing

Author

Listed:
  • Abdul Khalique Shaikh

    (Artificial Intelligence Laboratory MIMOS Berhad National R&D, Centre in ICT, Bukit Jalil, Malaysia)

  • Saadat M. Alhashmi

    (College of Business, University of Sharjah, Sharjah, UAE)

  • Rajendran Parthiban

    (School of Engineering, Monash University, Sunway Campus, Bandar Sunway, Malaysia)

Abstract

One of the fundamental issues in Grid decentralised resource discovery services is high communication overheads that affect the Grid system’s performance significantly. The rationale is that Grid resources are geographically distributed across the world through a wide area network under various virtual organisations. To address the issue, a significant amount of effort has been made by proposing various decentralised overlay algorithms with semantic solutions. Current Grid literature reveals that when semantic features are added into discovery services, the probability of finding resources is enhanced and communication overheads could be better. However, most of the existing decentralised resource discovery models utilise a domain-based semantic ontology with First Come First Serve (FCFS) basis scheduling for allocating Grid resources that can cause job rejection at run time and can pick resources that are far from the user nodes. As a result, communication overheads of the models are affected as the proximity criterion is not being considered in the selection process. To overcome these issues and enhance the application performance, we propose a Unification of Proximity and Semantic similarity for Appropriate Resource Selection (UPSARS) algorithm in a decentralised resource discovery model by using a sub-domain ontology structure for Grid computing environments. The purpose of this unification is to get optimised resources for user jobs (Gridlets) so that Grid brokers could select optimum resources in terms of proximity with high semantic relevancy. The algorithm considers both semantic and proximity criteria and selects the nearby nodes resources and reduces the communication overheads in terms of proximity and latency. We design and implement the model using the GridSim and the FreePastry simulation and modelling toolkits. The experimental results provide promising outcomes to reduce communication overheads and enhance resource allocation performance.

Suggested Citation

  • Abdul Khalique Shaikh & Saadat M. Alhashmi & Rajendran Parthiban, 2016. "A Proximity and Semantic-Aware Optimisation Model for Sub-Domain-Based Decentralised Resource Discovery in Grid Computing," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 15(02), pages 1-25, June.
  • Handle: RePEc:wsi:jikmxx:v:15:y:2016:i:02:n:s0219649216500209
    DOI: 10.1142/S0219649216500209
    as

    Download full text from publisher

    File URL: http://www.worldscientific.com/doi/abs/10.1142/S0219649216500209
    Download Restriction: Access to full text is restricted to subscribers

    File URL: https://libkey.io/10.1142/S0219649216500209?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Shang, Ming-Sheng & Zhang, Zi-Ke & Zhou, Tao & Zhang, Yi-Cheng, 2010. "Collaborative filtering with diffusion-based similarity on tripartite graphs," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(6), pages 1259-1264.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Abdul Khalique Shaikh & Saadat M. Alhashmi & Rajendran Parthiban & Amril Nazir, 2018. "A Fuzzy Rule-Based Optimisation Model for Efficient Resource Utilisation in a Grid Environment Using Proximity Awareness and Semantic Technology," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 17(02), pages 1-21, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ramezani, Mohsen & Moradi, Parham & Akhlaghian, Fardin, 2014. "A pattern mining approach to enhance the accuracy of collaborative filtering in sparse data domains," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 408(C), pages 72-84.
    2. Li, Jianguo & Tang, Yong & Chen, Jiemin, 2017. "Leveraging tagging and rating for recommendation: RMF meets weighted diffusion on tripartite graphs," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 483(C), pages 398-411.
    3. Zhang, Yin & Zhang, Bin & Gao, Kening & Guo, Pengwei & Sun, Daming, 2012. "Combining content and relation analysis for recommendation in social tagging systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(22), pages 5759-5768.
    4. Zhang, Chu-Xu & Zhang, Zi-Ke & Yu, Lu & Liu, Chuang & Liu, Hao & Yan, Xiao-Yong, 2014. "Information filtering via collaborative user clustering modeling," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 396(C), pages 195-203.
    5. Yin, Chun-Xia & Peng, Qin-Ke & Chu, Tao, 2012. "Personal artist recommendation via a listening and trust preference network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(5), pages 1991-1999.
    6. Zhang, Jing & Peng, Qinke & Sun, Shiquan & Liu, Che, 2014. "Collaborative filtering recommendation algorithm based on user preference derived from item domain features," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 396(C), pages 66-76.
    7. Ramezani, Mohsen & Yaghmaee, Farzin, 2016. "A novel video recommendation system based on efficient retrieval of human actions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 457(C), pages 607-623.
    8. Moradi, Parham & Ahmadian, Sajad & Akhlaghian, Fardin, 2015. "An effective trust-based recommendation method using a novel graph clustering algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 462-481.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wsi:jikmxx:v:15:y:2016:i:02:n:s0219649216500209. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Tai Tone Lim (email available below). General contact details of provider: http://www.worldscinet.com/jikm/jikm.shtml .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.