Service composition in stochastic settings

Ronen I. Brafman, Giuseppe De Giacomo, Massimo Mecella, Sebastian Sardina

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

With the growth of the Internet-of-Things and online Web services, more services with more capabilities are available to us. The ability to generate new, more useful services from existing ones has been the focus of much research for over a decade. The goal is, given a specification of the behavior of the target service, to build a controller, known as an orchestrator, that uses existing services to satisfy the requirements of the target service. The model of services and requirements used in most work is that of a finite state machine. This implies that the specification can either be satisfied or not, with no middle ground. This is a major drawback, since often an exact solution cannot be obtained. In this paper we study a simple stochastic model for service composition: we annotate the target service with probabilities describing the likelihood of requesting each action in a state, and rewards for being able to execute actions. We show how to solve the resulting problem by solving a certain Markov Decision Process (MDP) derived from the service and requirement specifications. The solution to this MDP induces an orchestrator that coincides with the exact solution if a composition exists. Otherwise it provides an approximate solution that maximizes the expected sum of values of user requests that can be serviced. The model studied although simple shades light on composition in stochastic settings and indeed we discuss several possible extensions.

Original languageEnglish
Title of host publicationAI*IA 2017 Advances in Artificial Intelligence - 16th International Conference of the Italian Association for Artificial Intelligence, Proceedings
EditorsFloriana Esposito, Stefano Ferilli, Francesca A. Lisi, Roberto Basili
PublisherSpringer Verlag
Pages159-171
Number of pages13
ISBN (Print)9783319701684
DOIs
StatePublished - 1 Jan 2017
Event16th International Conference on Italian Association for Artificial Intelligence, AI*IA 2017 - Bari, Italy
Duration: 14 Nov 201717 Nov 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10640 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference16th International Conference on Italian Association for Artificial Intelligence, AI*IA 2017
Country/TerritoryItaly
CityBari
Period14/11/1717/11/17

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