Reducing power density through activity migration
Proceedings of the 2003 international symposium on Low power electronics and design
Temperature-aware microarchitecture: Modeling and implementation
ACM Transactions on Architecture and Code Optimization (TACO)
Making scheduling "cool": temperature-aware workload placement in data centers
ATEC '05 Proceedings of the annual conference on USENIX Annual Technical Conference
Live migration of virtual machines
NSDI'05 Proceedings of the 2nd conference on Symposium on Networked Systems Design & Implementation - Volume 2
pMapper: power and migration cost aware application placement in virtualized systems
Proceedings of the 9th ACM/IFIP/USENIX International Conference on Middleware
Thermal-aware task scheduling for data centers through minimizing heat recirculation
CLUSTER '07 Proceedings of the 2007 IEEE International Conference on Cluster Computing
vGreen: a system for energy efficient computing in virtualized environments
Proceedings of the 14th ACM/IEEE international symposium on Low power electronics and design
Opportunities and challenges to unify workload, power, and cooling management in data centers
Proceedings of the Fifth International Workshop on Feedback Control Implementation and Design in Computing Systems and Networks
Opportunities and challenges to unify workload, power, and cooling management in data centers
ACM SIGOPS Operating Systems Review
vGreen: A System for Energy-Efficient Management of Virtual Machines
ACM Transactions on Design Automation of Electronic Systems (TODAES)
An economic model for green cloud
Proceedings of the 10th International Workshop on Middleware for Grids, Clouds and e-Science
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In state of the art systems, workload scheduling and server fan speed operate independently leading to cooling inefficiencies. In this work we propose GentleCool, a proactive multi-tier approach for significantly lowering the fan cooling costs without compromising the performance. Our technique manages the fan speed through intelligently allocating the workload across different machines. The experimental results show our approach delivers average cooling energy savings of 72% and improves the mean time between failures (MTBF) of the fans by 2.3X compared to the state of the art.