Summary Service-Oriented Computing (SOC) enables the composition of loosely coupled service agents provided with varying Quality of Service (QoS) levels, effectively forming a multiagent system (MAS). Selecting a (near-)optimal set of services for a composition in terms of QoS is crucial when many functionally equivalent services are available. As the number of distributed services, especially in the cloud, is rising rapidly, the impact of the network on the QoS keeps increasing. Despite this and opposed to most MAS approaches, current service approaches depend on a centralized architecture which cannot adapt to the network. Thus, we propose a scalable distributed architecture composed of a flexible number of distributed control nodes. Our architecture requires no changes to existing services and adapts from a centralized to a completely distributed realization by adding control nodes as needed. Also, we propose an extended QoS aggregation algorithm that allows to accurately estimate network QoS. Finally, we evaluate the benefits and optimality of our architecture in a distributed environment.
The foundation for our research is given in @cite_3 where the QoS-aware composition problem (CP) is introduced. Common notions, which we also use, are given, and the problem is formalized and solved with (Linear) Integer Programming (IP), which is still a common way to obtain optimal solutions for the CP. A genetic algorithm (GA) is used in @cite_17 @cite_19 . Besides, many efficient heuristic algorithms have been introduced in @cite_11 @cite_9 @cite_0 , and most recently in @cite_4 @cite_22 @cite_15 . All these approaches share the same definition of the CP which ignores the QoS of the network connecting the services. Except for IP which requires a linear function to compute the utility of a workflow, most approaches can be easily augmented with our two-phased QoS algorithm.
@cite_4 @cite_22 @cite_9 @cite_17 @cite_3 @cite_0 @cite_19 @cite_15 @cite_11
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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. Next generation networks are envisioned to support dynamic and customizable service compositions at Internet scale. To facilitate the communication between distributed software components, on-demand and QoS-aware network service composition across large scale networks emerges as a key research challenge. This paper presents a fast QoS-aware service composition algorithm for selecting a set of interconnected domains with specific service classes. We further show how such algorithm can be used to support network adaptation and service mobility. In simulation studies performed on large scale networks, the algorithm exhibits very high probability of finding the optimal solution within short execution time. In addition, we present a distributed service composition framework utilizing this algorithm. In recent years, quite a few search algorithms have been used to solve Web service composition problem. However, it is still lack of systematic analysis about these methods. In the paper, we attempt to analyze the effect of three typical meta-heuristic search algorithms. The experimental analysis is performed according to the search-based Web service composition framework. In the experiments, we mainly carry out the analysis as follows: different abstract service number, different candidate service number and different QoS constraint strength. Based on the above analysis, some guidelines of realizing Web service composition via search algorithms are yielded. For example, EDA is suitable for the composition problem with large abstract service number. PSO is more effective for the case of large candidate service number. GA is a good choose for the little-scale Web service composition problem. In addition, EDA can effectively settle the composition problem with strong QoS constraint. GA is suitable for the problem with medium constraint strength, and PSO has the poor ability to tackle the QoS constraint with high strength. This paper considers the problem of minimizing the response time for a given database workload by a proper choice of indexes. This problem is NP-hard and known in the literature as the Index Selection Problem (ISP). We propose a genetic algorithm (GA) for solving the ISP. Computational results of the GA on standard ISP instances are compared to branch-and-cut method and its initialisation heuristics and two state of the art MIP solvers: CPLEX and OSL. These results indicate good performance, reliability and efficiency of the proposed approach.