Abstract
Analysing and modelling the characteristics of virtual machine (VM) usage gives cloud providers crucial information when dimensioning cloud infrastructure and designing appropriate allocation policies. In addition, administrators can use these models to build a normal behaviour profile of job requests, in order to differentiate malicious and normal activities. Finally, it allows researchers to design more accurate simulation environments. An open challenge is to empirically develop and verify an accurate model of VM usage for users in these applications. In this paper, we study the VM usage in the popular Amazon EC2 and Windows Azure cloud platforms, in terms of the VM request arrival and departure processes, and the number of live VMs in the system. We find that both the VM request arrival and departure processes exhibit self-similarity and follow the power law distribution. Our analysis also shows that the autoregressive integrated moving average (ARIMA) model can be used to fit and forecast the VM demands, which is an important requirement for managing the workload in cloud services.
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