Dimensionless Parameters for Evaluation of Thermal Design and Performance of Large-scale Data Centers

Sharma, Ratnesh; Bash, Cullen; Patel, Chandrakant D.

doi:10.2514/6.2002-3091

Cited by 121 publications

(46 citation statements)

References 2 publications

(2 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In [12], researchers at HP Labs defined Supply Heat Index (SHI) and Return Heat Index (RHI) to characterize the energy efficiency of data center cooling systems. SHI is defined as SHI = Enthalpy rise due to infiltration in cold aisles Total enthalpy rise at the rack exhausts and RHI is defined as 1 − SHI.…”

Section: E Evaluation Using Heat Indexesmentioning

confidence: 99%

Thermal-aware task scheduling for data centers through minimizing heat recirculation

Tang

Gupta

Varsamopoulos

2007

2007 IEEE International Conference on Cluster Computing

123

100

View full text Add to dashboard Cite

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.

show abstract

Section: E Evaluation Using Heat Indexesmentioning

confidence: 99%

Thermal-aware task scheduling for data centers through minimizing heat recirculation

Tang

Gupta

Varsamopoulos

2007

2007 IEEE International Conference on Cluster Computing

123

100

View full text Add to dashboard Cite

show abstract

“…Basic mathematical modeling and parameters for profiling datacenter are proposed in [21]. However, capturing complex thermodynamic phenomena using complex Computational Fluid Dynamic (CFD) models [22] is prohibitive in terms of computational overhead.…”

Section: Related Workmentioning

confidence: 99%

VMAP: Proactive thermal-aware virtual machine allocation in HPC cloud datacenters

Lee

Viswanathan

Pompili

2012

2012 19th International Conference on High Performance Computing

View full text Add to dashboard Cite

Abstract-Clouds provide the abstraction of nearly-unlimited computing resources through the elastic use of federated resource pools (virtualized datacenters). They are being increasingly considered for HPC applications, which have traditionally targeted grids and supercomputing clusters. However, maximizing energy efficiency and utilization of cloud datacenter resources, avoiding undesired thermal hotspots (due to overheating of over-utilized computing equipment), and ensuring quality of service guarantees for HPC applications are all conflicting objectives, which require joint consideration of multiple pairwise tradeoffs. The novel concept of heat imbalance, which captures the unevenness in heat generation and extraction, at different regions inside a HPC cloud datacenter is introduced. This thermal awareness enables proactive datacenter management through prediction of future temperature trends as opposed to the state-of-the-art reactive management based on current temperature measurements. VMAP, an innovative proactive thermal-aware virtual machine consolidation technique is proposed to maximize computing resource utilization, to minimize datacenter energy consumption for computing, and to improve the efficiency of heat extraction. The effectiveness of the proposed technique is verified through experimental evaluations with HPC workload traces under singleas well as federated-datacenter scenarios (in the machine rooms at Rutgers University and University of Florida).

show abstract

“…In this paper, we investigate data centers from a holistic, cyber-physical aspect that encompasses both their computing and cooling equipment; we explore the potential of reducing the operational cost of data centers by making cooling more efficient-and thus more economical-through thermal-aware task management. Heat recirculation plays a significant role in a data center's energy efficiency [7], [8]; as computing devices in a data center emit heat by running tasks, the cooling system must supply cold air to their air inlets at a temperature below their redline temperature, i.e. the maximum allowed operational temperature specified by the device manufacturer.…”

Section: Introductionmentioning

confidence: 99%

Energy-Efficient Thermal-Aware Task Scheduling for Homogeneous High-Performance Computing Data Centers: A Cyber-Physical Approach

Tang

Gupta

Varsamopoulos

2008

IEEE Trans. Parallel Distrib. Syst.

376

View full text Add to dashboard Cite

Abstract-High Performance Computing data centers have been rapidly growing, both in number and size. Thermal management of data centers can address dominant problems associated with cooling such as the recirculation of hot air from the equipment outlets to their inlets, and the appearance of hot spots. In this paper, we show through formalization that minimizing the peak inlet temperature allows for the lowest cooling power needs. Using a low-complexity, linear heat recirculation model, we define the problem of minimizing the peak inlet temperature within a data center through task assignment (MPIT-TA), consequently leading to minimal cooling requirement. We also provide two methods to solve the formulation, XInt-GA, which uses a genetic algorithm and XInt-SQP, which uses sequential quadratic programming. Results from small-scale data center simulations show that solving the formulation leads to an inlet temperature distribution that, compared to other approaches, is 2°C to 5°C lower and achieves about 20%-30% cooling energy savings at common data center utilization rates. Moreover, our algorithms consistently outperform MinHR, a recirculation-reducing task placement algorithm in the literature.

show abstract

Dimensionless Parameters for Evaluation of Thermal Design and Performance of Large-scale Data Centers

Cited by 121 publications

References 2 publications

Thermal-aware task scheduling for data centers through minimizing heat recirculation

Thermal-aware task scheduling for data centers through minimizing heat recirculation

VMAP: Proactive thermal-aware virtual machine allocation in HPC cloud datacenters

Energy-Efficient Thermal-Aware Task Scheduling for Homogeneous High-Performance Computing Data Centers: A Cyber-Physical Approach

Contact Info

Product

Resources

About