Markovian Workload Characterization for QoS Prediction in the Cloud

Resource allocation in the cloud is usually driven by performance predictions, such as estimates of the future incoming load to the servers or of the Quality-of-Service (QoS) offered by applications to end users. In this context, characterizing web workload fluctuations in an accurate way is fundamental to understand how to provision cloud resources under time-varying traffic intensities. In this paper, the authors investigate the Markovian Arrival Processes (MAP) and the related MAP/MAP/1 queueing model as a tool for performance prediction of servers deployed in the cloud. MAPs are a special class of Markov models used as a compact description of the time-varying characteristics of workloads.