Bio-Inspired Models of Network, Information, and Computing Systems. 5th International ICST Conference, BIONETICS 2010, Boston, USA, December 1-3, 2010, Revised Selected Papers

Research Article

Software Service Selection by Multi-level Matching and Reinforcement Learning

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  • @INPROCEEDINGS{10.1007/978-3-642-32615-8_31,
        author={Rajeev Raje and Snehasis Mukhopadhyay and Sucheta Phatak and Rashmi Shastri and Lahiru Gallege},
        title={Software Service Selection by Multi-level Matching and Reinforcement Learning},
        proceedings={Bio-Inspired Models of Network, Information, and Computing Systems. 5th International ICST Conference, BIONETICS 2010, Boston, USA, December 1-3, 2010, Revised Selected Papers},
        proceedings_a={BIONETICS},
        year={2012},
        month={10},
        keywords={software services multi-level specifications discovery classification reinforcement learning acquaintances},
        doi={10.1007/978-3-642-32615-8_31}
    }
    
  • Rajeev Raje
    Snehasis Mukhopadhyay
    Sucheta Phatak
    Rashmi Shastri
    Lahiru Gallege
    Year: 2012
    Software Service Selection by Multi-level Matching and Reinforcement Learning
    BIONETICS
    Springer
    DOI: 10.1007/978-3-642-32615-8_31
Rajeev Raje1,*, Snehasis Mukhopadhyay1,*, Sucheta Phatak1, Rashmi Shastri1, Lahiru Gallege1
  • 1: Indiana University Purdue University Indianapolis
*Contact email: rraje@cs.iupui.edu, smukhopa@cs.iupui.edu

Abstract

The software realization of distributed systems is typically achieved as loose coalitions of independently created services. The selection of such services, to act as building blocks of a distributed system, is a critical task that requires discovery and matching activities. This selection task is generally based on simple matching techniques and without any notion of customization. This paper presents a method to achieve the service discovery process using the principles of multilevel matching based on multi-level specifications and customization based on reinforcement learning techniques. In this method, services are selected dynamically using an on-line performance-based reinforcement feedback. In contrast to methods which require the services to actually carry out a task before being selected, in the method proposed in this paper, service selection is carried out using only specification matching, thereby eliminating a large amount of redundant computation. Experimental results are presented in the context of a information classification system. These experiments demonstrate that a high degree of performance can be achieved at a much reduced computational cost using only multi-level specification-matching based reinforcement feedback signals.