Temperature-aware dynamic resource provisioning in a power-optimized datacenter

The current energy and environmental cost trends of datacenters are unsustainable. It is critically important to develop datacenter-wide power and thermal management (PTM) solutions that improve the energy efficiency of the datacenters. This paper describes one such approach where a PTM engine decides on the number and placement of ON servers while simultaneously adjusting the supplied cold air temperature. The goal is to minimize the total power consumption (for both servers and air conditioning units) while meeting an upper bound on the maximum temperature seen in any server chassis in the data center. To achieve this goal, it is important to be able to predict the incoming workload in terms of requests per second (which is done by using a short-term workload forecasting technique) and to have efficient runtime policies for bringing new servers online when the workload is high or shutting them off when the workload is low. Datacenter-wide power saving is thus achieved by a combination of chassis consolidation and efficient cooling. Experimental results demonstrate the effectiveness of the proposed dynamic resource provisioning method. 1

[1]  Stephen P. Boyd,et al.  Processor Speed Control With Thermal Constraints , 2009, IEEE Transactions on Circuits and Systems I: Regular Papers.

[2]  Daniel M. Dias,et al.  A scalable and highly available web server , 1996, COMPCON '96. Technologies for the Information Superhighway Digest of Papers.

[3]  Jeffrey S. Chase,et al.  Making Scheduling "Cool": Temperature-Aware Workload Placement in Data Centers , 2005, USENIX Annual Technical Conference, General Track.

[4]  Suman Nath,et al.  Energy-Aware Server Provisioning and Load Dispatching for Connection-Intensive Internet Services , 2008, NSDI.

[5]  Philip S. Yu,et al.  Dynamic Load Balancing on Web-Server Systems , 1999, IEEE Internet Comput..

[6]  Massoud Pedram,et al.  Minimizing data center cooling and server power costs , 2009, ISLPED.

[7]  Luiz André Barroso,et al.  Web Search for a Planet: The Google Cluster Architecture , 2003, IEEE Micro.

[8]  Enrique V. Carrera,et al.  Load balancing and unbalancing for power and performance in cluster-based systems , 2001 .

[9]  N. Rasmussen Calculating Total Cooling Requirements for Data Centers , 2007 .

[10]  Sandeep K. S. Gupta,et al.  Energy-Efficient Thermal-Aware Task Scheduling for Homogeneous High-Performance Computing Data Centers: A Cyber-Physical Approach , 2008, IEEE Transactions on Parallel and Distributed Systems.

[11]  Luca Benini,et al.  Specification and analysis of power-managed systems , 2004, Proceedings of the IEEE.

[12]  Jerome A. Rolia,et al.  Workload Analysis and Demand Prediction of Enterprise Data Center Applications , 2007, 2007 IEEE 10th International Symposium on Workload Characterization.

[13]  Randy H. Katz,et al.  Above the Clouds: A Berkeley View of Cloud Computing , 2009 .