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      • HARVEST
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      Investigation of service selection algorithms for grid services

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      Tapashree_M.Sc._Thesis_Sept09.pdf (3.915Mb)
      Date
      2009-08
      Author
      Guha, Tapashree
      Type
      Thesis
      Degree Level
      Masters
      Metadata
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      Abstract
      Grid computing has emerged as a global platform to support organizations for coordinated sharing of distributed data, applications, and processes. Additionally, Grid computing has also leveraged web services to define standard interfaces for Grid services adopting the service-oriented view. Consequently, there have been significant efforts to enable applications capable of tackling computationally intensive problems as services on the Grid. In order to ensure that the available services are assigned to the high volume of incoming requests efficiently, it is important to have a robust service selection algorithm. The selection algorithm should not only increase access to the distributed services, promoting operational flexibility and collaboration, but should also allow service providers to scale efficiently to meet a variety of demands while adhering to certain current Quality of Service (QoS) standards. In this research, two service selection algorithms, namely the Particle Swarm Intelligence based Service Selection Algorithm (PSI Selection Algorithm) based on the Multiple Objective Particle Swarm Optimization algorithm using Crowding Distance technique, and the Constraint Satisfaction based Selection (CSS) algorithm, are proposed. The proposed selection algorithms are designed to achieve the following goals: handling large number of incoming requests simultaneously; achieving high match scores in the case of competitive matching of similar types of incoming requests; assigning each services efficiently to all the incoming requests; providing the service requesters the flexibility to provide multiple service selection criteria based on a QoS metric; selecting the appropriate services for the incoming requests within a reasonable time. Next, the two algorithms are verified by a standard assignment problem algorithm called the Munkres algorithm. The feasibility and the accuracy of the proposed algorithms are then tested using various evaluation methods. These evaluations are based on various real world scenarios to check the accuracy of the algorithm, which is primarily based on how closely the requests are being matched to the available services based on the QoS parameters provided by the requesters.
      Degree
      Master of Science (M.Sc.)
      Department
      Computer Science
      Program
      Computer Science
      Supervisor
      Ludwig, Simone
      Committee
      Wu, Fang Xiang; McQuillan, Ian; Keil, Mark
      Copyright Date
      August 2009
      URI
      http://hdl.handle.net/10388/etd-09112009-201250
      Subject
      Evolutionary Algorithms
      Grid Computing
      Service Selection
      Swarm Intelligence
      Artificial Intelligence
      Assignment Problem
      Collections
      • Electronic Theses and Dissertations
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