Comprehensive Quality-Aware Automated Semantic Web Service Composition

Abstract

Automated web service composition is one of the ultimate goals of serviceoriented computing. It loosely couples web services to accommodate users' complex requirements. Evolutionary Computation (EC) techniques combined with AI planning have been successfully proposed to efficiently produce composite services with near-optimal Quality of Semantic Matchmaking (QoSM) and/or Quality of Service (QoS), which measure the satisfaction of the functional and non-functional requirements from users, respectively. Despite some recent progress, both the effectiveness and efficiency of existing approaches need further improvement to enhance the competitive advantage of service providers. The overall goal of this thesis is to propose novel EC-based fully automated service composition approaches that can effectively and efficiently solve challenging singleobjective, multi-objective, and dynamic service composition problems.Firstly, this thesis proposes two novel Estimation of Distribution Algorithm (EDA) based approaches (called EDA-NHM and EDA-EHM) and one memetic EDA-based approach with four different local search operators to single-objective fully automated web service composition that jointly optimizes QoSM and QoS. EDA-NHM and EDA-EHM are proposed with novel permutation-based and DAG-based representations to model the distribution of composition solutions with respect to varied service composition workflows. Two sampling techniques are also studied in EDA-NHM and EDA-EHM to effectively and efficiently sample new promising permutations and functionally valid DAGs, respectively. These two EDA-based approaches are compared to state-of-the-art works. The comparisons reveal that EDA-NHM produces better-quality composite services than EDA-EHM and the state-of-the-art works. On the other hand, EDA-EHM achieves the highest efficiency among all the competing EC-based methods, delivering moderate effectiveness. Furthermore, one proposed memetic approaches built upon EDA-NHM (called MEEDA-LOP) pushes the cutting-edge performance in terms of effectiveness and efficiency.Secondly, this thesis studies two categories of multi-objective service composition problems: one category aims to generate a set of approximated Pareto optimal solutions for users to choose from, while the other category aims to generate multiple composite services for multiple user segments with distinctive preferences on QoSM. To effectively and efficiently handle the first category of problems, a memetic approach based on Non-dominated Sorting Genetic Algorithm II (NSGA-II), called MNSGA2-EDA, is proposed by enhancing NSGA-II with EDA-based local search. The novelty of this method lies in the innovative use of EDA for effective and efficient local improvements, rather than for global exploration. MNSGA2-EDA is compared to state-of-the-art multi-objective works, for studying its performance. We found that MNSGA2-EDA achieves much higher effectiveness and efficiency in finding Pareto optimal solutions. The second category of problems can be naturally treated as multit...

EDA-based approach to comprehensive quality-aware automated semantic web service composition

“…In the second category, service composition solutions are represented as permutations, which are then decoded into solutions represented as DAGs [10], [31], [33]. PSO is utilized to find an optimized queue of services (i.e., a permutation), which can be decoded into a corresponding DAG-based composite service [33].…”

“…. , m. In Step 2, these permutation-based individuals are decoded into DAG-based solutions using a forward graph building technique [10], based on which, the fitness in Eq. 5 of each individual can be calculated.…”

“…Service composition solutions are commonly represented as Directed Acyclic Graphs (DAGs) [5], [8], [10], [11], [32], [33]. Let G = (V, E) be a DAG-based composite solution from Start to End, where nodes correspond to the services and edges correspond to the robust causal links.…”

“…AI planning and Evolutionary Computation (EC) are two of the most widely used techniques for semi-automated and fully-automated web service composition [5], [7], [10], [13], [15], [16], [17]. AI planning techniques focus on creating valid composite services, where the functional correctness is always ensured with gradually constructed workflows.…”

“…EC techniques have been widely used to solve service composite problems that aim to optimize either one or both of QoSM and QoS, and are potentially more useful in practice as they can efficiently find "good enough" composite solutions. Important approaches [5], [6], [7], [8], [9], [10], [11], [12], [13] based on Genetic Algorithms (GA) [19], Genetic Programming (GP) [20], Particle Swarm Optimization (PSO) [21] and Estimation of Distribution Algorithm (EDA) [22], have been widely investigated in the literature.…”