Service selection in web service compositions optimizing energy consumption and service response time

A challenging task in Web service composition is the runtime binding of a set of interconnected abstract services to concrete ones. This question, formulated as the service selection problem, has been studied in the area of service compositions implementing business processes. Despite the abundance of work on this topic, few of them match some practical needs that we are interested in. Indeed, while considering the business process implemented by service compositions, we can distinguish between two classes: compositions that correspond to single business process and those implementing multiple communicating processes. While most of the prior work focuses only on the first case, it is the latter that interests us in this paper. This paper contributes to the service selection by proposing a new algorithm that, in polynomial time, generates a mixed linear integer program for optimizing service compositions based on the service response time and the energy consumption. The novelty in this work is our focus on multi-process composition and energy consumption. The paper also proposes a new analysis of the service selection and an evaluation of the proposed algorithm.

[1]  Marco Aurélio Gerosa,et al.  Service-oriented middleware for the Future Internet: state of the art and research directions , 2011, Journal of Internet Services and Applications.

[2]  Jerry R. Hobbs,et al.  DAML-S: Web Service Description for the Semantic Web , 2002, SEMWEB.

[3]  Amit P. Sheth,et al.  Modeling Quality of Service for Workflows and Web Service Processes , 2002 .

[4]  Danilo Ardagna,et al.  Adaptive Service Composition in Flexible Processes , 2007, IEEE Transactions on Software Engineering.

[5]  Maria Luisa Villani,et al.  An approach for QoS-aware service composition based on genetic algorithms , 2005, GECCO '05.

[6]  Gero Mühl,et al.  QoS aggregation for Web service composition using workflow patterns , 2004 .

[7]  Dhabaleswar K. Panda,et al.  Communication modeling of heterogeneous networks of workstations for performance characterization of collective operations , 1999, Proceedings. Eighth Heterogeneous Computing Workshop (HCW'99).

[8]  Tao Yu,et al.  Efficient algorithms for Web services selection with end-to-end QoS constraints , 2007, TWEB.

[9]  Java Binding,et al.  GNU Linear Programming Kit , 2011 .

[10]  Mathias Weske,et al.  Business Process Management: Concepts, Languages, Architectures , 2007 .

[11]  Peter Dolog,et al.  A Scalable Approach for QoS-Based Web Service Selection , 2008, ICSOC Workshops.

[12]  Rodney S. Tucker,et al.  Green Cloud Computing: Balancing Energy in Processing, Storage, and Transport , 2011, Proceedings of the IEEE.

[13]  Alfredo Goldman,et al.  On Graph Reduction for QoS Prediction of Very Large Web Service Compositions , 2012, 2012 IEEE Ninth International Conference on Services Computing.

[14]  Dejan S. Milojicic,et al.  An analytical approach for predicting QoS of web services choreographies , 2012, MGC '12.

[15]  M. Brian Blake,et al.  Green Web Services: Modeling and Estimating Power Consumption of Web Services , 2012, 2012 IEEE 19th International Conference on Web Services.

[16]  Lei Cao,et al.  Using genetic algorithm to implement cost-driven web service selection , 2007, Multiagent Grid Syst..

[17]  Juhnyoung Lee,et al.  Matching Algorithms for Composing Business Process Solutions with Web Services , 2003, EC-Web.

[18]  Valérie Issarny,et al.  Efficient Semantic Service Discovery in Pervasive Computing Environments , 2006, Middleware.

[19]  James Lockerbie,et al.  CHOReOS Requirements and scenarios for the "Passenger-friendly airport" (D6.1) , 2011 .

[20]  Anne H. H. Ngu,et al.  QoS-aware middleware for Web services composition , 2004, IEEE Transactions on Software Engineering.