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.
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Raje, R.R., Mukhopadhyay, S., Phatak, S., Shastri, R., Gallege, L.S. (2012). Software Service Selection by Multi-level Matching and Reinforcement Learning. In: Suzuki, J., Nakano, T. (eds) Bio-Inspired Models of Network, Information, and Computing Systems. BIONETICS 2010. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 87. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32615-8_31
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DOI: https://doi.org/10.1007/978-3-642-32615-8_31
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