Exploiting Client-Side Collected Measurements to Perform QoS Assessment of IaaS

Delivering reliable service offerings to clients remain a challenging aspect in today's cloud infrastructure. A broad number of research studies have undertaken the service evaluation process from one side; that is, the infrastructure's perspective. Conversely, clients' assessment to the service has been mostly neglected. In this paper, we propose a client-side service evaluation approach which mainly relies on the clients' assessment of infrastructure's service offerings. The proposed approach utilizes the strength of the Social Network Analysis (SNA) principles in conjunction with the Generalized Extreme Value Theorem (EVT) to converge to a precise Quality of Service (QoS) model. Our goal in this research is to build precise QoS models to predict the performance of clients that exhibit similar behaviors. Thus, we develop a novel SNA-based clustering algorithm that analyzes the strength of the interconnection links between clients and cluster related clients in communities of similar behaviors. The proposed approach is effective in providing Infrastructure as a Service (IaaS) providers with a better assessment tool to evaluate and improve their service offerings. The experimental results of the proposed approach on GENI's SEATTLE platform demonstrate its ability to enhance the prediction process of the performance of IaaS service offerings.

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