Aligning a Service Provisioning Model of a Service-Oriented System with the ITIL v.3 Life Cycle

Bringing together the ICT and the business layer of a service-oriented system (SoS) remains a great challenge. Few papers tackle the management of SoS from the business and organizational point of view. One solution is to use the well-known ITIL v.3 framework. The latter enables to transform the organization into a service-oriented organizational which focuses on the value provided to the service customers. In this paper, we align the steps of the service provisioning model with the ITIL v.3 processes. The alignment proposed should help organizations and IT teams to integrate their ICT layer, represented by the SoS, and their business layer, represented by ITIL v.3. One main advantage of this combined use of ITIL and a SoS is the full service orientation of the company.Comment: This document is the technical work of a conference paper submitted to the International Conference on Exploring Service Science 1.5 (IESS 2015

Continually Learning Optimal Web Service Compositions

Open service-oriented systems which autonomously and continually satisfy users' service requests to optimal levels are an appropriate response to the need for increased automation of information systems. Given a service request, an open service-oriented system interprets the functional and nonfunctional requirements laid out in the request and identifies the optimal selection of services that is, identifies services. These services' coordinated execution optimally satisfies the requirements in the request. When selecting services, it is relevant to: (1) revise selections as new services appear and others become unavailable; (2) use multiple criteria, including nonfunctional ones to choose among competing services; (3) base the comparisons of services on observed, instead of advertised performance; and (4) allow for uncertainty in the outcome of service executions. To address issues (1)(4), we propose the Multi-Criteria Randomized Reinforcement Learning (MCRRL) service selection approach. MCRRL learns and revises service selections using a novel multicriteria-driven (including quality of service parameters, deadline, reputation, cost, and preferences) reinforcement learning algorithm, which integrates the exploitation of data about individual services' past performance with optimal, undirected, continual exploration of new selections that involve services whose behavior has not been observed. The experiments indicate the algorithm behaves as expected and outperforms two standard approaches