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GAP2WSS: A Genetic Algorithm based on the Pareto Principle for Web Service Selection

SayedHassan Khatoonabadi, Shahriar Lotfi, Ayaz Isazadeh

TL;DR

This work tackles QoS-aware Web service selection under global QoS, interservice, and transactional constraints by formulating it as a constrained multi-task optimization problem. It introduces GAP2WSS, a Pareto-principle-guided genetic algorithm that scores and ranks candidate services per task, prunes to the top $20\%$, and evolves solutions within this reduced space. Empirical results against a penalty-based GA show faster convergence and higher fitness across varying numbers of tasks, candidate services, and constraint counts, demonstrating notable efficiency and efficacy gains. The approach suggests practical impact for real-time service composition and motivates benchmarks and broader applicability of Pareto-based reductions in constrained optimization for Web services.

Abstract

Despite all the progress in Web service selection, the need for an approach with a better optimality and performance still remains. This paper presents a genetic algorithm by adopting the Pareto principle that is called GAP2WSS for selecting a Web service for each task of a composite Web service from a pool of candidate Web services. In contrast to the existing approaches, all global QoS constraints, interservice constraints, and transactional constraints are considered simultaneously. At first, all candidate Web services are scored and ranked per each task using the proposed mechanism. Then, the top 20 percent of the candidate Web services of each task are considered as the candidate Web services of the corresponding task to reduce the problem search space. Finally, the Web service selection problem is solved by focusing only on these 20 percent candidate Web services of each task using a genetic algorithm. Empirical studies demonstrate this approach leads to a higher efficiency and efficacy as compared with the case that all the candidate Web services are considered in solving the problem.

GAP2WSS: A Genetic Algorithm based on the Pareto Principle for Web Service Selection

TL;DR

This work tackles QoS-aware Web service selection under global QoS, interservice, and transactional constraints by formulating it as a constrained multi-task optimization problem. It introduces GAP2WSS, a Pareto-principle-guided genetic algorithm that scores and ranks candidate services per task, prunes to the top , and evolves solutions within this reduced space. Empirical results against a penalty-based GA show faster convergence and higher fitness across varying numbers of tasks, candidate services, and constraint counts, demonstrating notable efficiency and efficacy gains. The approach suggests practical impact for real-time service composition and motivates benchmarks and broader applicability of Pareto-based reductions in constrained optimization for Web services.

Abstract

Despite all the progress in Web service selection, the need for an approach with a better optimality and performance still remains. This paper presents a genetic algorithm by adopting the Pareto principle that is called GAP2WSS for selecting a Web service for each task of a composite Web service from a pool of candidate Web services. In contrast to the existing approaches, all global QoS constraints, interservice constraints, and transactional constraints are considered simultaneously. At first, all candidate Web services are scored and ranked per each task using the proposed mechanism. Then, the top 20 percent of the candidate Web services of each task are considered as the candidate Web services of the corresponding task to reduce the problem search space. Finally, the Web service selection problem is solved by focusing only on these 20 percent candidate Web services of each task using a genetic algorithm. Empirical studies demonstrate this approach leads to a higher efficiency and efficacy as compared with the case that all the candidate Web services are considered in solving the problem.

Paper Structure

This paper contains 32 sections, 15 equations, 14 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Quality of Service (QoS)
  3. The Web Service Selection Problem
  4. Contribution of the Paper
  5. Paper Outline
  6. Related Work
  7. Evolutionary Approaches
  8. Non-Evolutionary Approaches
  9. Problem Formulation
  10. Composition Patterns
  11. Composite Web Service
  12. QoS
  13. Constraints
  14. Utility
  15. Problem Statement
  16. ...and 17 more sections

Figures (14)

  • Figure 1: A conceptual overview of the Web service selection problem
  • Figure 2: The workflow of a composite Web service
  • Figure 3: The pseudocode of GAP2WSS
  • Figure 4: Example of applying the crossover operator
  • Figure 5: Example of applying the mutation operator
  • ...and 9 more figures

Theorems & Definitions (3)

  • Definition 1: Feasible Composite Web Service
  • Definition 2: Optimal Composite Web Service
  • Definition 3: The Web Service Selection Problem