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Abstract-The thermal environment of data centers plays a significant role in affecting the energy efficiency and the reliability of data center operation. A dominant problem associated with cooling data centers is the recirculation of hot air from the equipment outlets to their inlets, causing the appearance of hot spots and an uneven inlet temperature distribution. Heat is generated due to the execution of tasks, and it varies according to the power profile of a task. We are looking into the prospect of assigning the incoming tasks around the data center in such a way so as to make the inlet temperatures as even as possible; this will allow for considerable cooling power savings. Based on our previous research work on characterizing the heat recirculation in terms of cross-interference coefficients, we propose a task scheduling algorithm for homogeneous data centers, called XInt, that minimizes the inlet temperatures, and leads to minimal heat recirculation and minimal cooling energy cost for data center operation. We verify, through both theoretical formalization and simulation, that minimizing heat recirculation will result in the best cooling energy efficiency. XInt leads to an inlet temperature distribution that is 2 • C to 5 • C lower than other approaches, and achieves about 20%-30% energy savings at moderate data center utilization rates. XInt also consistently achieves the best energy efficiency compared to another recirculation minimized algorithm, MinHR.

With the increasing popularity of Internet-based information retrieval and cloud computing, saving energy in Internet data centers (a.k.a. hosting centers, server farms) is of increasing importance. Current research approaches are based on dynamically adjusting the active server set in order to turn off a portion of the servers and save energy without compromising the quality of service; the workload is then distributed, conventionally equally (i.e. balanced), across the active servers. Although there is ample work that demonstrates energy savings through dynamic server provisioning, there is little work on thermal-aware server provisioning. This paper provides a formulation of the thermal aware active server set provisioning (TASP), in a nonlinear minimax binary integer programming form, and a series of heuristic approaches to solving them, namely MiniMax, bb-sLRH, CP-sLRH and sLRH. Furthermore, it introduces thermal-aware workload distribution (TAWD) among the active servers. The proposed heuristics are evaluated using a thermal model of the ASU HPCI data center, while the request traffic is based on real web traces of the 1998 FIFA World Cup as well as the SPECweb2009 suite. The TASP heuristics are found to outperform a power-aware-only server set selection scheme (CPSP), by up to 9.3% for the simulated scenario. The order of achieved energy efficiency is: MiniMax (9.3% savings), CP-sLRH (9.2%), bb-sLRH (8.6%), sLRH (5.8%), compared to CPSP.

Abstract-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 coolingaware 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 FCFSBackfill, HTS increases the throughput per unit energy by 6.89% and 5.56%, respectively, over LRH and MTDP (an energy-efficient spatial scheduling algorithm with server consolidation). I. IHigh Performance Computing (HPC) applications require high computation capabilities, often in the range of teraflops. A major issue in contemporary data centers, hosting such high computation facilities, is the high energy consumption in their operations. Indeed, the data centers' energy consumption amounted to nearly 2% of the total energy budget of the US in 2007 and is expected to reach 4% in 2011 [1]; as such, greening the data center operations has been of utmost interest over the years [2]-[8]. Up to half of this energy can be attributed to cooling the data centers (i.e. cooling energy) to keep the operating temperatures within manufacturer specified redline temperatures. This paper focuses on a cyber-physical oriented coordinated job and cooling management in HPC data centers to reduce the total (i.e. computing and cooling) energy consumption of the data centers.The cooling energy depends on two factors: i) the cooling demand, which is driven by the power distribution and the redline temperature; and ii) the cooling behavior, i.e. the behavior of the Computer Room Air Conditioner (CRAC) unit (controlled by varying the thermostat setting), to meet the demand. A major concern in this regard is the possible recirculation and intermixing of hot air generated by running the jobs with the cold air supplied from the CRAC [7]. Recirculation of hot air depends on the data center layout and can cause hot-spots; thus potentially increasing the cooling demand.Techniques to r...