Alexandre Sawczuk da Silva

GraphEvol: A Graph Evolution Technique for Web Service Composition

Web service composition can be thought of as the combination of reusable functionality modules available over the network to create applications that accomplish more complex tasks, and Evolutionary Computation (EC) techniques have been applied with success to this problem. Genetic Programming (GP) is a traditionally employed EC technique in this domain, and it encodes solutions as trees instead of their natural Directed Acyclic Graph (DAG) form. This complicates the enforcement of dependencies between service nodes, which is much easier to accomplish in a DAG. To overcome this we propose GraphEvol, an evolutionary technique that uses DAGs directly to represent and evolve Web service composition solutions. GraphEvol is analogous to GP, but it implements the mutation and crossover operators differently. Experiments were carried out comparing GraphEvol with GP for a series of composition tasks, with results showing that GraphEvol solutions either match or surpass the quality of those obtained using GP, at the same time relying on a more intuitive representation.

A graph-based Particle Swarm Optimisation approach to QoS-aware web service composition and selection

Web services are network-accessible modules that perform specific tasks and can be integrated into Web service compositions to accomplish more complex objectives. Due to the fast-growing number of Web services and the well-defined nature of their interfaces, the field of automated Web service composition is quickly expanding. The use of Particle Swarm Optimisation composition techniques that take Quality of Service (QoS) properties into account is well-established in the field. However, the commonly utilised approach is to optimise a preselected Web service composition workflow, which requires domain expertise and prior knowledge and thus may lead to the loss of better solutions that require different workflow configurations. This paper presents a graph-based PSO technique which simultaneously determines an optimal workflow and near-optimal Web services to be included in the composition based on their QoS properties, as well as a greedy-based PSO technique which follows the commonly utilised approach. The comparison of the two techniques shows that despite requiring more execution time, the graph-based approach provides equivalent or better solutions than the greedy-based approach, depending on the workflow preselected by the greedy-based PSO. These results demonstrate that under certain circumstances, the graph-based approach is capable of producing solutions whose fitness surpasses that of the solutions obtained by employing the greedy-based approach.

Genetic programming for QoS-aware web service composition and selection

Web services, which can be described as functionality modules invoked over a network as part of a larger application are often used in software development. Instead of occasionally incorporating some of these services in an application, they can be thought of as fundamental building blocks that are combined in a process known as Web service composition. Manually creating compositions from a large number of candidate services is very time consuming, and developing techniques for achieving this objective in an automated manner becomes an active research field. One promising group of techniques encompasses evolutionary computing, which can effectively tackle the large search spaces characteristic of the composition problem. Therefore, this paper proposes the use of genetic programming for Web service composition, investigating three variations to ensure the creation of functionally correct solutions that are also optimised according to their quality of service. A variety of comparisons are carried out between these variations and two particle swarm optimisation approaches, with results showing that there is likely a trade-off between execution time and the quality of solutions when employing genetic programming and particle swarm optimisation. Even though genetic programming has a higher execution time for most datasets, the results indicate that it scales better than particle swarm optimisation.

Particle Swarm Optimisation with Sequence-Like Indirect Representation for Web Service Composition

Automated Web service composition, which refers to the creation of a complex application from pre-existing building blocks (Web services), has been an active research topic in the past years. The advantage of having an automated composition system is that it allows users to create new applications simply by providing the required parameters, instead of having to manually assemble the services. Existing approaches to automated composition rely on planning techniques or evolutionary computing (EC) to modify and optimise composition solutions directly in their tree/graph form, a complex process that requires several constraints to be considered before each alteration. To improve the search efficiency and simplify the checking of constraints, this work proposes an indirect Particle Swarm Optimisation (PSO)-based approach. The key idea of the indirect approach is to optimise a service queue which is then decoded into a composition solution by using a planning algorithm. This approach is compared to a previously proposed graph-based direct representation method, and experiment results show that the indirect representation can lead to a greater (or equivalent) quality while requiring a lower execution time. The analysis conducted shows that this is due to the design of the algorithms used for building and evaluating the fitness of solutions.

A GP approach to QoS-aware Web service composition including conditional constraints

Automated Web service composition is one of the holy grails of service-oriented computing, since it allows users to create an application simply by specifying the inputs the resulting application should require, the outputs it should produce, and any constraints it should respect. The composition problem has been handled using a variety of techniques, from AI planning to optimisation algorithms, however no approach so far has focused on handling three composition dimensions simultaneously, producing solutions that are: (1) fully functional (i.e. fully executable), (2) respect conditional constraints (e.g. user can specify logical branching), and (3) are optimised according to nonfunctional Quality of Service (QoS) measurements. This paper presents a genetic programming approach that addresses these three dimensions simultaneously through the fitness function, as well as through the enforcement of constraints to candidate trees during initialisation, mutation, and crossover. The approach is tested using an extended version of the WSC2008 datasets, and results show that fully functional and quality-optimised solutions can be created for all associated tasks, with an execution time that is roughly equivalent to that of a non-conditional approach.

A memetic algorithm-based indirect approach to web service composition

Web service composition is a popular area of investigation, since it encourages code reuse as opposed to re-implementing already existing functionality modules. Performing such compositions manually can be quite time-consuming, since the functionality of each service included in a solution must be fulfilled, while at the same time selecting candidates with the best possible quality of service attributes. This work proposes a memetic algorithm that can perform Web service composition in a fully automated manner, optimising a sequence of services that is then decoded into the final solution. The key advantage of this representation is that it separates the quality optimisation technique from the enforcement of correctness constraints, thus simplifying the overall composition process and preventing solutions from being overly constrained. While this indirect composition approach has been investigated earlier, the previous representation relied on weights for establishing a service sequence and used PSO as the optimisation technique. In this work, on the other hand, sequences are directly represented using a vector of services, which reduces the overall search space. Additionally, the memetic algorithm employed in this work is better at exploring solutions within promising areas of the search space. Experiments were conducted comparing the memetic approach with the previously proposed PSO, with two key findings. Firstly, the new representation reduces the overall execution time while maintaining the original solution quality. Secondly, the use of the memetic local search improves the overall quality of solutions, though it may incur longer execution times.

A GP Approach to QoS-Aware Web Service Composition and Selection

Web services are independent functionality modules that can be used as building blocks for applications that accomplish more specific tasks. The large and ever-growing number of Web services means that performing this type of Web service composition manually is unfeasible, which leads to the exploration of automated techniques to achieve this objective. Evolutionary Computation EC approaches, in particular, are a popular choice because they are capable of efficiently handling the complex search space involved in this problem. Therefore, we propose the use of a Genetic Programming GP technique for Web service composition, building upon previous work that combines the identification of functionally correct solutions with the consideration of the Quality of Service QoS properties for each atomic service. The proposed GP technique is compared with two PSO composition techniques using the same QoS-aware objective function, and results show that the solution fitness values and execution times of the GP approach are inferior to those of both PSO approaches, failing to converge for larger datasets. This is because the fitness function employed by the GP technique does not have complete smoothness, thus leading to unreliable behaviour during the evolution process. Multi-objective GP and the use of functional correctness constraints should be considered as alternatives to overcome this in the future.

Evolutionary computation for automatic Web service composition: an indirect representation approach

Web services have become increasingly popular in recent years, and they are especially suitable to the process of Web service composition, which is when several services are combined to create an application that accomplishes a more complex task. In recent years, significant research efforts have been made on developing approaches for performing Quality of Service -aware Web service composition. Evolutionary computing (EC) techniques have been widely used for solving this problem, since they allow for the quality of compositions to be optimised, meanwhile also ensuring that the solutions produced have the required functionality. Existing EC-based composition approaches perform constrained optimisation to produce solutions that meet those requirements, however these constraints may hinder the effectiveness of the search. To address this issue, a novel framework based on an indirect representation is proposed in this work. The core idea is to first generate candidate service compositions encoded as sequences of services. Then, a decoding scheme is developed to transform any sequence of services into a corresponding feasible service composition. Given a service sequence, the decoding scheme builds the workflow from scratch by iteratively adding the services to proper positions of the workflow in the order of the sequence. This is beneficial because it allows the optimisation to be carried out in an unconstrained way, later enforcing functionality constraints during the decoding process. A number of encoding methods and corresponding search operators, including the PSO, GA, and GP-based methods, are proposed and tested, with results showing that the quality of the solutions produced by the proposed indirect approach is higher than that of a baseline direct representation-based approach for twelve out of the thirteen datasets considered. In particular, the method using the variable-length sequence representation has the most efficient execution time, while the fixed-length sequence produces the highest quality solutions.

Fragment-based genetic programming for fully automated multi-objective web service composition

Web services have become increasingly popular in recent years, given their modular nature and reusability potential. A particularly promising application is in Web service composition, where multiple individual services with specific functionalities are composed to accomplish a more complex task. Researchers have proposed evolutionary computing techniques for creating compositions that are not only feasible, but also have the best possible Quality of Service (QoS). Some of these works employed multi-objective techniques to tackle the optimisation of compositions with conflicting QoS attributes, but they are not fully automated, i.e. they assume the composition workflow structure is already known. This assumption is often not satisfied, as the workflow is often unknown. This paper proposes a genetic programming-based method to automatically generate service compositions in a multi-objective context, based on a novel fragmented tree representation. An evaluation using benchmark datasets is carried out, comparing existing methods adapted to the multi-objective composition problem. Results show that the fragmented method has the lowest execution time overall. In terms of quality, its Pareto fronts are equivalent to those of one of the approaches but inferior to those of the other. More importantly, this work provides a foundation for future investigation of multi-objective fully automated service composition.

A QoS-Aware Web Service Composition Approach Based on Genetic Programming and Graph Databases

A Web service can be thought of as a software module designed to accomplish specific tasks over the Internet. Web services are very popular, as they encourage code reuse as opposed to re-implementing already existing functionality. The process of combining multiple Web services is known as Web service composition. Previous attempts at automatically generating compositions have made use of genetic programming to optimize compositions, or introduced databases to keep track of relationships between services. This paper presents an approach that combines these two ideas, generating new compositions based on information stored in a graph database and then optimising their quality using genetic programming. Experiments were conducted comparing the performance of the newly proposed approach against that of existing works. Results show that the new approach executes faster than the previously proposed works, though it does not always reach the same solution quality as the compositions produced by them. Despite this, the experiments demonstrate that the fundamental idea of combining graph databases and genetic programming for Web service composition is feasible and a promising area of investigation.