Efficient QoS-aware Service Composition (original) (raw)

Combining global optimization with local selection for efficient QoS-aware service composition

Proceedings of the 18th international conference on World wide web - WWW '09, 2009

The run-time binding of web services has been recently put forward in order to support rapid and dynamic web service compositions. With the growing number of alternative web services that provide the same functionality but differ in quality parameters, the service composition becomes a decision problem on which component services should be selected such that user's end-to-end QoS requirements (e.g. availability, response time) and preferences (e.g. price) are satisfied. Although very efficient, local selection strategy fails short in handling global QoS requirements. Solutions based on global optimization, on the other hand, can handle global constraints, but their poor performance renders them inappropriate for applications with dynamic and realtime requirements. In this paper we address this problem and propose a solution that combines global optimization with local selection techniques to benefit from the advantages of both worlds. The proposed solution consists of two steps: first, we use mixed integer programming (MIP) to find the optimal decomposition of global QoS constraints into local constraints. Second, we use distributed local selection to find the best web services that satisfy these local constraints. The results of experimental evaluation indicate that our approach significantly outperforms existing solutions in terms of computation time while achieving close-tooptimal results.

Efficient anytime algorithm for large-scale QoS-aware web service composition

International Journal of Web and Grid Services, 2013

The QoS-aware web service composition (WSC) problem aims at the fully automatic construction of a composite web service with the optimal accumulated QoS value. It is, however, intractable to solve the QoS-aware WSC problem for large scale instances since the problem corresponds to a global optimisation problem. That is, in the real world, traditional algorithms can require significant amount of time to finally find the optimal solution, and such an unexpected long delay is unfavourable to users. In this paper, we propose a novel anytime algorithm using dynamic beam widths for the QoSaware WSC problem. Our algorithm generates early sub-optimal solutions and keeps improving the quality of the solution along with the execution time, up to the optimal solution if a client allows enough time. We empirically validate that our algorithm can identify composite web services with high quality much earlier than an optimal algorithm and the beam stack search.

A Survey of QoS-aware Web Service Composition Techniques

Web service composition can be briefly described as the process of aggregating services with disparate functionalities into a new composite service in order to meet increasingly complex needs of users. Service composition process has been accurate on dealing with services having disparate functionalities, however, over the years the number of web services in particular that exhibit similar functionalities and varying Quality of Service (QoS) has significantly increased. As such, the problem becomes how to select appropriate web services such that the QoS of the resulting composite service is maximized or, in some cases, minimized. This constitutes an NP-hard problem as it is complicated and difficult to solve. In this paper, a discussion of concepts of web service composition and a holistic review of current service composition techniques proposed in literature is presented. Our review spans several publications in the field that can serve as a road map for future research.

A scalable approach for QoS-based web service selection

2009

QoS-based service selection aims at finding the best component services that satisfy the end-to-end quality requirements. The problem can be modeled as a multi-dimension multi-choice 0-1 knapsack problem, which is known as NP-hard. Recently published solutions propose using linear programming techniques to solve the problem. However, the poor scalability of linear program solving methods restricts their applicability to small-size problems and renders them inappropriate for dynamic applications with run-time requirements. In this paper, we address this problem and propose a scalable QoS computation approach based on a heuristic algorithm, which decomposes the optimization problem into small sub-problems that can be solved more efficiently than the original problem. Experimental evaluations show that near-to-optimal solutions can be found using our algorithm much faster than using linear programming methods.

A Partial Selection Methodology for Efficient QoS-Aware Service Composition

—As web service has become a popular way for engineering software on the Internet, quality of service (QoS) which describes non-functional characteristics of web services is often employed in service composition. Since QoS is an aggregated concept consisting of several attributes, service composition on enormous candidate sets is a challenging multi-objective optimization problem. In this paper, we study the problem from a general Pareto optimal angle, seeking to reduce search space in service composition. Pareto set model for QoS-aware service composition is introduced, and its relationship with the widely used utility function model is theoretically studied, which proves the applicability of our model. QoS attributes are systematically studied according to their different types of aggregation patterns in service composition, and QoS-based dominance relationships between candidates and between workflows are defined. Taking advantage of pruning candidates by dominance relationships and constraint validations at candidate level, a service composition algorithm using partial selection techniques is proposed. Furthermore, a parallel approach is designed, which is able to significantly reduce search space and achieve great performance gains. A careful analysis of the optimality of our approach is provided, and its efficacy is further validated by both simulation experiments and real-world data based evaluations.

An Approach of Composite QoS Parameter Based Web Service Selection

Service selection using the quality of the service (QOS) is a strategy for implementing the most suitable services that accommodate the quality considerations in each aspect. A multi-dimension, multi-choice 0-1 knapsack concern, it is difficult as well as a complex concern to solve. The research recently centered on linear programming techniques to solve the concern because of their inadequate scalability features that make them appropriate only to simple issues and inapplicable for dynamic programs having real time specifications. In this document, we evaluate a scalable QoS service selection strategy according to a heuristic algorithm, which divides the concern into compact sub-problems for a near-to-optimal solution significantly faster than linear programming strategies.

QoS-Based web service composition based on genetic algorithm

Quality of service (QoS) is an important issue in the design and management of web service composition. QoS in web services consists of various non-functional factors, such as execution cost, execution time, availability, successful execution rate, and security. In recent years, the number of available web services has proliferated, and then offered the same services increasingly. The same web services are distinguished based on their quality parameters. Also, clients usually demand more value added services rather than those offered by single, isolated web services. Therefore, selecting a composition plan of web services among numerous plans satisfies client requirements and has become a challenging and time-consuming problem. This paper has proposed a new composition plan optimizer with constraints based on genetic algorithm. The proposed method can find the composition plan that satisfies user constraints efficiently. The performance of the method is evaluated in a simulated environment.

QoS-Driven Web Service Composition Using Learning-Based Depth First Search

2009 IEEE Conference on Commerce and Enterprise Computing, 2009

The goal of the Web Service Composition (WSC) problem is to find an optimal composition of web services to satisfy a given request using their syntactic and/or semantic features. In this paper, in particular, we study the Quality of Services (QoS)-driven WSC problem to optimize service quality criteria, e.g., response time and/or throughput. We propose a novel solution based on Learning-based Depth First Search (LDFS). Given a set of web service descriptions including QoS information and a requirement web service, we reduce the QoS-driven WSC problem into a planning problem on a statetransition system. We then find the optimal solution for the problem using a dynamic programming based on LDFS which recently has shown a promising result.