The case for power management in web servers
Power aware computing
Dynamic cluster reconfiguration for power and performance
Compilers and operating systems for low power
Routing, Flow, and Capacity Design in Communication and Computer Networks
Routing, Flow, and Capacity Design in Communication and Computer Networks
Managing server energy and operational costs in hosting centers
SIGMETRICS '05 Proceedings of the 2005 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
The cost of a cloud: research problems in data center networks
ACM SIGCOMM Computer Communication Review
The Datacenter as a Computer: An Introduction to the Design of Warehouse-Scale Machines
The Datacenter as a Computer: An Introduction to the Design of Warehouse-Scale Machines
Power optimization for dynamic configuration in heterogeneous web server clusters
Journal of Systems and Software
Characterizing cloud computing hardware reliability
Proceedings of the 1st ACM symposium on Cloud computing
Improving the scalability of data center networks with traffic-aware virtual machine placement
INFOCOM'10 Proceedings of the 29th conference on Information communications
Declarative automated cloud resource orchestration
Proceedings of the 2nd ACM Symposium on Cloud Computing
Estimating optimal cost of allocating virtualized resources with dynamic demand
Proceedings of the 23rd International Teletraffic Congress
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Service providers are migrating to on-demand cloud computing services to unburden the task of managing infrastructure, while cloud computing providers expand the number of servers in their data centers because of the increase in load. With this growing need, their energy consumption increases significantly. Conserving energy and reducing the operational cost while satisfying the service level agreement (SLA) becomes important in order to reduce both carbon emissions and the budget for cloud computing providers. On the other hand, the aggregated demands for different services are dynamic over a time horizon. We present a multi-time period optimization model for saving the operational cost by combining two factors: 1)Dynamic Voltage/Frequency Scaling (DVFS), 2)turning servers on/off over a time horizon. We show the impact of the granularity of the duration of the time slots and frequency options on optimal solutions. A parametric study on varying cost of turning servers on/off and power consumption is also presented.