End-to-End Performance Prediction for Selecting Cloud Services Solutions

In cloud computing, in order to select or recommend the best service solutions to end users, the end-to-end QoS requirements (e.g. response time and throughput) have to be computed. A typical cloud solution is a combination of multiple component services such as IaaS, SaaS, PaaS, etc. In a simplified case, there could be two components-software services and infrastructure services. The software service alone can satisfy end user's functional requirements (e.g. business objectives); however, the end-to-end QoS requirements require a collaboration of the multiple components at multiple cloud layers. In this paper, we consider the multilayered cloud architecture for computing the end-to-end performance values for cloud solutions. We propose a new method for measuring cloud component services similarity and predicting the end-to-end performance values of cloud solutions. In this method, the historical performance data of cloud component services is used based on users' past invocations. To evaluate our method and show its effectiveness, series of experiments are conducted. The experimental results demonstrate that our cloud multi-layers based method produces better prediction accuracy than other prediction approaches that consider one cloud layer.