Abstract
Research collaboration is very important for the success of any research project. However, due to the lack of communication, high cost of collaboration and heterogeneous platform implementation, researchers are not able to share scientific information and research project components especially with the large number of distributed research labs and institutes. To overcome those challenges an advanced computer model needs to be used to facilitate the share of research project components and research information between scientists; the advanced model is called cloud computing. Cloud computing offers researchers the capability to find, use and compose research project components remotely by offering the research project component as a service in SaaS layer. Furthermore, scientists can share research information between research partners using web service technology in DaaS layer. However, to use and compose research components, both a single service component and a series of research components that can support large-scale data demands need to be found. The process involves the integration of research components, which may be provided by different research institutes and labs. This paper aims to provide a QoS-based research component composition architecture for research collaboration in the cloud, based on distance based evolutionary algorithm. The algorithm will be able to compose and optimize research components according to multi-QoS attributes. Using multi domain with multi objective case study, we demonstrate the efficiency and effectiveness of the proposed technique and algorithm through experimental evaluation of component selection.