SILENCE: distributed adaptive sampling for sensor-based autonomic systems
Proceedings of the 8th ACM international conference on Autonomic computing
Cooling-aware workload placement with performance constraints
Performance Evaluation
A 'cool' load balancer for parallel applications
Proceedings of 2011 International Conference for High Performance Computing, Networking, Storage and Analysis
An energy aware framework for virtual machine placement in cloud federated data centres
Proceedings of the 3rd International Conference on Future Energy Systems: Where Energy, Computing and Communication Meet
Optimizing network performance and carbon offset through opportunistic reclustering
Concurrency and Computation: Practice & Experience
GDCSim: A simulator for green data center design and analysis
ACM Transactions on Modeling and Computer Simulation (TOMACS) - Special issue on simulation in complex service systems
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High Performance Computing (HPC) data centers are becoming increasingly dense; the associated power-density and energy consumption of their operation is increasing. Up to half of the total energy is attributed to cooling the data center; greening the data center operations to reduce both computing and cooling energy is imperative. To this effect: i) the Energy Inefficiency Ratio of SPatial job scheduling (a.k.a. job placement) algorithms, also referred as SP-EIR, is analyzed by comparing the total (computing + cooling) energy consumption incurred by the algorithms with the minimum possible energy consumption, while assuming that the job start times are already decided to meet the Service Level Agreements (SLAs); and ii) a coordinated cooling-aware job placement and cooling management algorithm, Highest Thermostat Setting (HTS), is developed. HTS is aware of dynamic behavior of the Computer Room Air Conditioner (CRAC) units and places the jobs in a way to reduce the cooling demands from the CRACs. Dynamic updates of the CRAC thermostat settings based on the cooling demands can enable a reduction in energy consumption. Simulation results based on power measurements and job traces from the ASU HPC data center show that: i) HTS reduces the SP-EIR by 15% compared to LRH, a thermal-aware spatial scheduling algorithm; and ii) in conjunction with FCFS-Backfill, HTS increases the throughput per unit energy by 6.89% and 5.56%, respectively, over LRH and MTDP (an energy-effcient spatial scheduling algorithm with server consolidation).