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Additional resources for Advances and New Trends in Environmental and Energy Informatics: Selected and Extended Contributions from the 28th International Conference on Informatics for Environmental Protection
The fitness function is then defined as follows: f ð AÞ ¼ 1 PDC ðAÞ 2 Expansion of Data Centers’ Energetic Degrees of Freedom to Employ. . 33 If the methodology is used to apply demand response management, target power consumption for the data center is given as PDCtarget. In that case, the optimization goal is to minimize the deviance to the given consumption a: a ¼ PDCtarget À PDC In this case, the fitness function uses a instead of PDC(A). The second optimization goal is always to minimize the amount of steps d needed to reach the new allocation state, since the new state should always be reached with as few operations as possible.
Based on this information, the algorithm calculates the amount of steps s (Acurrent, Atarget) that is needed to migrate from the current allocation state Acurrent to the new state Atarget where each step takes a constant amount of time (defined by the duration of migrations and server switches). 30 S. Janacek and W. Nebel 4 Problem Formulation and Methodology The goal of the presented methodology is to let the data center migrate from a current allocation and power state to another state with a specific power consumption, either a minimal or a given consumption under the consideration of the time it needs to enter the desired state.
In order to be able to make sound statements on the quality of our VM allocations, we need further metrics, which allow us to involve the power consumption targets and to get a weighted estimation of the quality on efficiency and deviance to target power consumption. To ensure comparability with other dynamic VM placing approaches, we implement our methodology into a standard framework such as OpenStack. It is planed to evaluate the methodology in an OpenStack testbed by applying it to different kinds of applications and different workloads.