Approximation algorithms for bin packing: a survey
Approximation algorithms for NP-hard problems
Proceedings of the seventeenth ACM symposium on Operating systems principles
Managing energy and server resources in hosting centers
SOSP '01 Proceedings of the eighteenth ACM symposium on Operating systems principles
Stream-Packing: Resource Allocation in Web Server Farms with a QoS Guarantee
HiPC '01 Proceedings of the 8th International Conference on High Performance Computing
A Case For Grid Computing On Virtual Machines
ICDCS '03 Proceedings of the 23rd International Conference on Distributed Computing Systems
Xen and the art of virtualization
SOSP '03 Proceedings of the nineteenth ACM symposium on Operating systems principles
Multi-dimensional storage virtualization
Proceedings of the joint international conference on Measurement and modeling of computer systems
Resource overbooking and application profiling in shared hosting platforms
OSDI '02 Proceedings of the 5th symposium on Operating systems design and implementationCopyright restrictions prevent ACM from being able to make the PDFs for this conference available for downloading
Façade: Virtual Storage Devices with Performance Guarantees
FAST '03 Proceedings of the 2nd USENIX Conference on File and Storage Technologies
Virtualization's next frontier: security
Proceedings of the 35th annual ACM SIGUCCS fall conference
Queue - Virtualization
Power-aware dynamic placement of HPC applications
Proceedings of the 22nd annual international conference on Supercomputing
Multidimensional bin packing algorithms
IBM Journal of Research and Development
The origin of the VM/370 time-sharing system
IBM Journal of Research and Development
System/370 extended architecture: facilities for virtual machines
IBM Journal of Research and Development
Trusted virtual domains: toward secure distributed services
HotDep'05 Proceedings of the First conference on Hot topics in system dependability
On the utility of DVFS for power-aware job placement in clusters
Euro-Par'11 Proceedings of the 17th international conference on Parallel processing - Volume Part I
Energy-aware service allocation
Future Generation Computer Systems
Energy-efficient and SLA-aware management of IaaS clouds
Proceedings of the 3rd International Conference on Future Energy Systems: Where Energy, Computing and Communication Meet
Proactive dynamic resource management in virtualized data centers
Proceedings of the 2nd International Conference on Energy-Efficient Computing and Networking
Adaptive resource configuration for Cloud infrastructure management
Future Generation Computer Systems
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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.