An Efficient Deadlock Avoidance Technique
IEEE Transactions on Computers
Managing energy and server resources in hosting centers
SOSP '01 Proceedings of the eighteenth ACM symposium on Operating systems principles
Xen and the art of virtualization
SOSP '03 Proceedings of the nineteenth ACM symposium on Operating systems principles
Power-aware QoS Management in Web Servers
RTSS '03 Proceedings of the 24th IEEE International Real-Time Systems Symposium
Resource overbooking and application profiling in shared hosting platforms
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Live migration of virtual machines
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Statistical QoS Guarantee and Energy-Efficiency in Web Server Clusters
ECRTS '07 Proceedings of the 19th Euromicro Conference on Real-Time Systems
Virtualization's next frontier: security
Proceedings of the 35th annual ACM SIGUCCS fall conference
Queue - Virtualization
Entropy: a consolidation manager for clusters
Proceedings of the 2009 ACM SIGPLAN/SIGOPS international conference on Virtual execution environments
Expressive power-based resource allocation for data centers
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Proceedings of the 1st International Conference on Energy-Efficient Computing and Networking
Black-box and gray-box strategies for virtual machine migration
NSDI'07 Proceedings of the 4th USENIX conference on Networked systems design & implementation
Behavioral model for cloud aware load and power management
Proceedings of the 2013 international workshop on Hot topics in cloud services
Inter cloud capable dynamic resource management with model of behavior
Proceedings of the 28th Annual ACM Symposium on Applied Computing
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Dynamically reassigning virtual machines (VMs) to servers is a widely addressed idea to save energy in data centers. VMs are consolidated in times of low overall resource demand. Unused servers are switched off to save energy. Mainly two major challenges must be addressed to realize this approach. First, the resource demand of VMs expected in the future must be estimated to take care of delays caused by VM migrations and server startups. An upcoming resource shortage must have been resolved right before it actually occurs. Second, a scheduling algorithm is needed that, based on a current distribution of VMs to servers, can guarantee to find a sequence of operations that resolves any upcoming resource shortage right in time. Within this paper, we present a novel approach that addresses both of these challenges. In contrast to previous work, this approach can guarantee not to cause any resource shortages, if the actual resource demand of the VMs meets the expected one. We performed a simulation based evaluation with a set of VMs. The underlying resource demand time series were measured in a data center operated by a medium-sized IT service provider. A data center model was used to estimate the energy consumption. Overall energy savings of about 23% could be achieved compared to a static approach. Resource shortages occurred in less than 0.1% of time. They could be resolved by the approach in less then 20 minutes.