A Quality-Driven Recommender System for IaaS Cloud Services
As the number of cloud services continues to increase, selecting services of interest across one or more cloud service environments using existing service selection methods raises a number of concerns such as performance, efficiency, end-to-end reliability and most importantly quality of search results. Clients often spend a considerable amount of time manually reading cloud providers' documentation to determine services that can meet their objectives and satisfy the application's requirements. Furthermore, cloud service providers' Quality of Service (QoS) claims for published services might not always be trustworthy and current cloud service selection methods do not take into consideration the dynamism of cloud environments as they are constantly changing. In addressing these challenges, we developed the Cloud Application Management (CAM), a multilayered framework that employs a meta-heuristic approach that is based on QoS for cloud services (QSCS) for enabling clients to effectively manage and control the quality of their applications deployed in the cloud. CAM supports the self-adaptive nature of the service selection process and adapts to the changes in clients' requirements and interests.
2018 IEEE International Conference on Big Data (Big Data)
Al-Masri, E. and Meng, L., "A Quality-Driven Recommender System for IaaS Cloud Services" (2018). School of Engineering and Technology Publications. 340.