Composing Distributed Data-intensive Web Services Using a Flexible Memetic Algorithm

Abstract

Web Service Composition (WSC) is a particularly promising application of Web services, where multiple individual services with specific functionalities are composed to accomplish a more complex task, which must fulfil functional requirements and optimise Quality of Service (QoS) attributes, simultaneously. Additionally, large quantities of data, produced by technological advances, need to be exchanged between services. Data-intensive Web services, which manipulate and deal with those data, are of great interest to implement data-intensive processes, such as distributed Data-intensive Web Service Composition (DWSC). Researchers have proposed Evolutionary Computing (EC) fullyautomated WSC techniques that meet all the above factors. Some of these works employed Memetic Algorithms (MAs) to enhance the performance of EC through increasing its exploitation ability of in searching neighbourhood area of a solution. However, those works are not efficient or effective. This paper proposes an MA-based approach to solving the problem of distributed DWSC in an effective and efficient manner. In particular, we develop an MA that hybridises EC with a flexible local search technique incorporating distance of services. An evaluation using benchmark datasets is carried out, comparing existing state-ofthe-art methods. Results show that our proposed method has the highest quality and an acceptable execution time overall.

Distributed Data-intensive Web Service Composition

“…That is, composite services produced by our proposed method show significantly better performance in the presence of bandwidth fluctuations. Although the F-MA method for the distributed DWSC provides solutions with good fitness at the design stage, as verified in [158], there is no guarantee that the composite services can remain robust. Significantly better values concerning the statistical test have been highlighted in Table 7.1.…”

“…However, with the increasing number of services available in the service repository, it is impractical to manually design compositions' workflow. This idea gives rise to the fully-automated WSC where the optimisation and the construction of workflows are performed simultaneously [45,128,151,157,158,159,205]. Different workflow structures can be automatically generated during the composition and services can be selected to achieve optimal QoS.…”

“…The second research category employs Evolutionary Computation (EC) techniques [22,30,46,51,54,88,129,158,187,198]. EC techniques are stochastic population-based search meta-heuristic algorithms.…”

“…The decoding process is a graph-building algorithm [54] and automatically transforms sequences into a corresponding executable service composition, i.e., a feasible workflow [158]. The decoding process is a crucial part of our fully-automated DWSC and is responsible for the functional correctness of the composite service.…”

“…Cutting-edge adaptive MAs for DDWSC (i.e., adaptive clusterguided, adaptive distance-guided and adaptive Combined clusterdistance-guided MAs (Com-C-D)) are developed by integrating recent MAs for DDWSC with our priority-based selection method. Empirical experiments show that our proposed MAs significantly outperform existing methods for DDWSC problems, including the abstraction refinement method [29], cluster-guided MA [157] (Section 4.3) and flexible distance-guided MA [158] (Section 4.5), as well as their combined version. Additionally, empirical evidence strongly demonstrates that both novelty and improvability measures play irreplaceable roles in SelectionP riority and must be used jointly to achieve high performance.…”