A Meta-Heuristic-Based Approach for Qos-Aware Service Composition

Abstract

Recently, with the rapid increase in the number of web services, QoS-aware Web Service Composition(QWSC) has become a popular topic in both industry and academia. Meta-heuristic algorithm, as an effective way to solve classical optimization problems, has been successfully applied to QWSC nowdays. However, such approach has intrinsic drawbacks and usually lack of good performance in large-scale scenarios. For example, some meta-heuristic algorithms are suitable for continuous search space, while the search space of QWSC is discrete. For solving those problems which were commonly faced when applying meta-heuristic algorithm on QWSC, in this research, we firstly introduce a preprocessing approach for constructing fuzzy continuous neighborhood relations of concrete services, which makes the local search strategy of meta-heuristic algorithms be as effective in discrete space as in continuous space, thus improving the optimization performance. Second, we combine Harris Hawks Optimization (HHO) algorithm and logical chaotic sequence to propose an improved meta-heuristic algorithm named CHHO for solving QWSC. The ergodic and chaotic characteristics of chaotic sequences are used to enhance the ability of the CHHO to jump out of the local optimum for further optimization. Experimental results show that the CHHO has better optimization performance by comparing with the existing mainstream algorithms when solving QWSC problems. Additionally, the preprocessing approach not only greatly improves the optimization performance of the CHHO but also can be freely utilized in other meta-heuristics based approaches. INDEX TERMS Meta-heuristic algorithm, QoS-aware web service composition, Harris hawks optimization, fuzzy continuous, logical chaotic sequence.

Efficient Service Selection in Multimedia Documents Adaptation Processes

“…In addition, the adopted problem formulation is different. The main advantages are summarized as follows: Most of existing approaches dedicated to services selection paradigm target mainly the optimization of QoS parameters and the improvement of execution time (see for instance [ 20 , 25 , 26 , 34 – 36 ]). This is necessary but not sufficient on its own.…”

“… Static approaches : they perform the service composition according to a prior knowledge about QoS values without considering dynamic changes in QoS (e.g. [ 20 , 21 , 25 , 26 , 35 , 37 ]). They belong to three subcategories: Exact methods : they seek optimal solutions using deterministic methods such as constraint programming and linear integer programming; however, the computational complexity does not always allow finding them.…”

“…Starting from the fact that existing formulations of the services selection paradigm deal with QoS parameters through benchmarks or probability distributions (simulation) (e.g. [ 20 , 25 , 34 , 36 ]), our formulation reinforces the problem definition by modeling the offers provided by services’ providers. This is an influential aspect since it reflects many real-world situations in practice.…”

“…In fact, most of existing works that deal with the services selection paradigm generate datasets (QoS values) through probability distributions or benchmarks without considering the correlation between these values (e.g. [ 20 , 25 , 34 , 36 ]). We argue then that the proposed synthetic methods allow dealing with large, dynamic and various datasets leading to more reliable and deeper analysis and comparisons between solving-methods.…”

Distributed Data-intensive Web Service Composition

“…It can be modelled as a multi-objective optimisation problem to capture varied trade-offs among different conflicting QoS attributes. Single-objective approaches assume that users' QoS preferences can be quantified in advance [5,109]. In multi-objective algorithms can simultaneously search for a set of compromised solutions [160,202,210], for when to choose from in the absence of preferences (e.g., the weight of each QoS attribute).…”