Customized dynamic load balancing for a network of workstations
HPDC '96 Proceedings of the 5th IEEE International Symposium on High Performance Distributed Computing
Energy Management for Server Clusters
HOTOS '01 Proceedings of the Eighth Workshop on Hot Topics in Operating Systems
Power-aware QoS Management in Web Servers
RTSS '03 Proceedings of the 24th IEEE International Real-Time Systems Symposium
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
Energy conservation policies for web servers
USITS'03 Proceedings of the 4th conference on USENIX Symposium on Internet Technologies and Systems - Volume 4
Traffic Flow Forecasting Based on Pattern Recognition to Overcome Memoryless Property
MUE '07 Proceedings of the 2007 International Conference on Multimedia and Ubiquitous Engineering
IEEE Transactions on Parallel and Distributed Systems
Energy-efficient server clusters
PACS'02 Proceedings of the 2nd international conference on Power-aware computer systems
Properties of energy-price forecasts for scheduling
CP'12 Proceedings of the 18th international conference on Principles and Practice of Constraint Programming
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Reducing the power consumption and operational cost of IT servers is of great concern today. With the growth of the Internet and online services, the number of data centers is increasing day by day. Servers for many cloud applications and other large providers are spread globally. Energy costs across the globe vary dynamically. Servers operate at varied energy costs based on their location and time of use. The load of a server varies based on its geographical location and the time of operation. This paper focuses on exploiting the dynamic nature of electrical power pricing, so that a cost saving is obtained by geo-location of requests to servers operating at lower costs at particular times. There exist patterns of load that are similar for different types of servers. Scheduling decisions are made considering both loads and operating costs of the servers into account, i. e., requests are scheduled to run on servers operating at low cost that also have low expected load. In order to meet the business requirements of an application, scheduling decisions for requests that have stringent SLA considerations or high server affinity, are made by assigning high priority for these requests. Geo-location of requests is done for low priority requests.