Cloud computing is a popular way to address the scalability and efficiency issues of data centers. While the level of development of cloud technologies is already high-enough to easily beat old static cluster configurations, there is still a lot of room for improvement. One of these areas is in the way resource management tools predict the CPU and tasks performance. Normally, resource managers assume that the tasks do not affect each other, and assign resources under this assumption. With a simple experiment this paper shows that this assumption is grossly wrong, leading to overestimation of task performance that can approach 50%. Next, the paper presents a behavioral model that efficiently addresses these issues. Preliminary results obtained for three different hardware platforms demonstrate the benefits of our model in performance prediction.
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