Using Regression Techniques to Predict Large Data Transfers
International Journal of High Performance Computing Applications
Seamless live migration of virtual machines over the MAN/WAN
Future Generation Computer Systems - IGrid 2005: The global lambda integrated facility
The efficacy of live virtual machine migrations over the internet
VTDC '07 Proceedings of the 2nd international workshop on Virtualization technology in distributed computing
A multi-site virtual cluster system for wide area networks
LASCO'08 First USENIX Workshop on Large-Scale Computing
pMapper: power and migration cost aware application placement in virtualized systems
Proceedings of the 9th ACM/IFIP/USENIX International Conference on Middleware
PowerNap: eliminating server idle power
Proceedings of the 14th international conference on Architectural support for programming languages and operating systems
Entropy: a consolidation manager for clusters
Proceedings of the 2009 ACM SIGPLAN/SIGOPS international conference on Virtual execution environments
Cutting the electric bill for internet-scale systems
Proceedings of the ACM SIGCOMM 2009 conference on Data communication
Minimizing data center cooling and server power costs
Proceedings of the 14th ACM/IEEE international symposium on Low power electronics and design
Virtual Infrastructure Management in Private and Hybrid Clouds
IEEE Internet Computing
Computer Networks: The International Journal of Computer and Telecommunications Networking
Cost of Virtual Machine Live Migration in Clouds: A Performance Evaluation
CloudCom '09 Proceedings of the 1st International Conference on Cloud Computing
Thermal aware server provisioning and workload distribution for internet data centers
Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing
Server workload analysis for power minimization using consolidation
USENIX'09 Proceedings of the 2009 conference on USENIX Annual technical conference
Evaluation of delta compression techniques for efficient live migration of large virtual machines
Proceedings of the 7th ACM SIGPLAN/SIGOPS international conference on Virtual execution environments
CloudNet: dynamic pooling of cloud resources by live WAN migration of virtual machines
Proceedings of the 7th ACM SIGPLAN/SIGOPS international conference on Virtual execution environments
Black-box and gray-box strategies for virtual machine migration
NSDI'07 Proceedings of the 4th USENIX conference on Networked systems design & implementation
Renewable energy provisioning for ICT services in a future internet
The future internet
GreenSlot: scheduling energy consumption in green datacenters
Proceedings of 2011 International Conference for High Performance Computing, Networking, Storage and Analysis
Utilizing green energy prediction to schedule mixed batch and service jobs in data centers
ACM SIGOPS Operating Systems Review
GreenWare: greening cloud-scale data centers to maximize the use of renewable energy
Middleware'11 Proceedings of the 12th ACM/IFIP/USENIX international conference on Middleware
Proactive dynamic resource management in virtualized data centers
Proceedings of the 2nd International Conference on Energy-Efficient Computing and Networking
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Within the last few years, the development of data centers has been moving into high-grade flexible architectures that adapt to the needs (by means of virtualization). This flexibility can be used by load management methods to minimize the energy demand. Depending on quality of service and the hardware used, the application of a load and power management (LPM) results in a big dynamic range of the number of servers currently required. Previous energy models for data centers did not take into account this dynamic sufficiently and thus are not suitable for cloud data centers. Therefore, we present two contributions in this paper. First, we enhance an existing LPM for virtual machines, which has been designed for single data centers, enabling it to interact in flexible environments, for example in inter cloud LPM systems. Second, we develop a model which abstracts the behavior of the LPM concerning the server allocation. This model can be consulted for forecasts and obtains an average precision of 93%.