@inproceedings{Hoy2010,Author = {Hoyer, Marko and Schröder, Kiril and Nebel, Wolfgang},Title = {Statistical static capacity management in virtualized data centers supporting fine grained QoS specification},Year = {2010},Month = {04},type = {inproceedings},note = {From an ecological but also from an economical and in the meantime a technical view the fast ongoing increase of power consumption in today’s data centers is no longer feasible. Methodologies, more efficiently using energy in data centers, must be develop},Abstract = {From an ecological but also from an economical and in the meantime a technical view the fast ongoing increase of power consumption in today’s data centers is no longer feasible. Methodologies, more efficiently using energy in data centers, must be developed. One step into this direction is to increase the utilization of the hardware in data centers by using virtualization techniques. The efficiency of such techniques strongly depends on provisioning and allocating the resources. Statistical static allocation approaches have been proven to use resources very efficiently by overbooking hardware using the fact that typical applications rarely need their maximum demand and especially seldom all at the same time. In our work we analyze these approaches and point out two major drawbacks. First, we show that guaranteeing QoS (Quality of Service) aspects by two parameters, as it is done in these approaches, is inflexible and often leads to suboptimal solutions. Second, such conventional approaches require statistical independent resource demands of the virtual machines which prevent them from being used in most common data centers. To overcome these drawbacks, we first suggest a more fine grained way of specifying QoS guarantees that saved up to 20% of resources to be reserved for a single virtual machine in our examples. Furthermore, we present a new allocation approach that is able to deal with any kind of correlations in the resource demand. Compared to a pessimistic approach, that reserves the maximum required resources for each virtual machines all over the time, our approach can save 27% of required hardware resources in a typical data center scenario.}}@COMMENT{Bibtex file generated on }