Distance-Guided GA-Based Approach to Distributed Data-Intensive Web
  Service Composition
  Distributed computing which uses Web services as fundamental elements,
enables high-speed development of software applications through composing many
interoperating, distributed, re-usable, and autonomous services. As a
fundamental challenge for service developers, service composition must fulfil
functional requirements and optimise Quality of Service (QoS) attributes,
simultaneously. On the other hand, huge amounts of data have been created by
advances in technologies, which may be exchanged between services.
Data-intensive Web services are of great interest to implement data-intensive
processes. However, current approaches to Web service composition have omitted
either the effect of data, or the distribution of services. Evolutionary
Computing (EC) techniques allow for the creation of compositions that meet all
the above factors. In this paper, we will develop Genetic Algorithm (GA)-based
approach for solving the problem of distributed data-intensive Web service
composition (DWSC). In particular, we will introduce two new heuristics, i.e.
Longest Common Subsequence(LCS) distance of services, in designing crossover
operators. Additionally, a new local search technique incorporating distance of
services will be proposed.
