CBC Approach for Evaluating Potential SaaS on the Cloud
This work addresses the need for service providers and users to assess SaaS quality more comprehensively, but it appears incremental as it builds on existing evaluation models by adding user requirements.
The paper tackles the problem of evaluating potential Software as a Service (SaaS) on the cloud by proposing a new evaluation model based on Constraint Based Clustering (CBC), which incorporates both quality attributes and potential user requirements, though no concrete results or numbers are provided.
The cloud computing is evolving as a key computing platform for sharing resources like infrastructure, platform, software etc. This has proven to be an essential requirement for extending many existing applications. Software as a service (SaaS) is referred as on-demand software supplied by service providers in which software and associated data are hosted on the cloud and it can be accessed by service users using a thin client via a web browser. SaaS is commonly utilized and it provides many benefits to service users. To realize these benefits, it is essential to evaluate potential quality of SaaS, not only to the service users but also to the service providers. They have to evaluate their services against requirements of service users. The existing evaluation models are focusing only on quality attributes of SaaS. In this paper, a new evaluation model is proposed based on the data mining technique of Constraint Based Clustering (CBC). The proposed model gives emphasis on potential requirements of service users along with quality attributes of services.