Data Center Cooling Optimization: Ambient Intelligence Based Load Management (AILM)

2009 ◽  
Author(s):  
Ankit Somani ◽  
Yogendra K. Joshi
Keyword(s):  
Data Center ◽  
Ambient Intelligence ◽  
Data Centers ◽  
Heat Dissipation ◽  
Job Scheduling ◽  
Scheduling Algorithm ◽  
Load Management ◽  
Inlet Temperature ◽  
Hot Air ◽  
Unit Increase

Early Data centers can consume 25 to 50 times more electric power than a standard office space of the same footprint. In this paper, a simplified computational fluid dynamics/heat transfer (CFD/HT) model for a unit cell of a data center with a hot aisle-cold aisle (HACA) layout is simulated. Inefficiencies dealing with the mixing of hot air present in the room with the cold inlet air, leading to a loss of cooling potential are identified. The need for a thermal aware job-scheduling algorithm which enhances IT productivity, while maintaining the facility within server inlet temperature constraints is established. The inherent non-linearity of such an optimization problem is explained. A novel algorithm called the Ambient Intelligence based Load Management (AILM) is developed which counters the above issues and enhances the net data center heat dissipation capacity for given energy consumption at the facilities end. It gives a scheme to determine how much and where the computer loads should be allocated, based on the differential loss in cooling potential per unit increase in server workload. Enhancements of heat dissipation capacity of over 50% are proved numerically for the representative values considered. An approach to incorporate heterogeneity in data centers, both for lower heat dissipation and liquid cooled racks has been established. Finally, different objective functions are studied and an ideal combination of the IT objectives and thermal constraints is derived.

scholarly journals Minimizing Thermal Stress for Data Center Servers through Thermal-Aware Relocation

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Muhammad Tayyab Chaudhry ◽  
T. C. Ling ◽  
S. A. Hussain ◽  
Atif Manzoor
Keyword(s):  
Thermal Stress ◽  
Data Center ◽  
Data Centers ◽  
Heat Dissipation ◽  
Thermal State ◽  
Inlet Temperature ◽  
Outlet Temperature ◽  
State Monitoring ◽  
Performance Loss ◽  
Cooling Mechanism

A rise in inlet air temperature may lower the rate of heat dissipation from air cooled computing servers. This introduces a thermal stress to these servers. As a result, the poorly cooled active servers will start conducting heat to the neighboring servers and giving rise to hotspot regions of thermal stress, inside the data center. As a result, the physical hardware of these servers may fail, thus causing performance loss, monetary loss, and higher energy consumption for cooling mechanism. In order to minimize these situations, this paper performs the profiling of inlet temperature sensitivity (ITS) and defines the optimum location for each server to minimize the chances of creating a thermal hotspot and thermal stress. Based upon novel ITS analysis, a thermal state monitoring and server relocation algorithm for data centers is being proposed. The contribution of this paper is bringing the peak outlet temperatures of the relocated servers closer to average outlet temperature by over 5 times, lowering the average peak outlet temperature by 3.5% and minimizing the thermal stress.

Impact of Tile Design on Thermal Performance of Open and Enclosed Aisles

2017 ◽  
Author(s):  
Sadegh Khalili ◽  
Mohammad I. Tradat ◽  
Kourosh Nemati ◽  
Mark Seymour ◽  
Bahgat Sammakia
Keyword(s):  
Data Centers ◽  
Area Ratio ◽  
Inlet Temperature ◽  
Open Area ◽  
Cfd Simulations ◽  
Physical Separation ◽  
Hot Air ◽  
Airflow Distribution ◽  
The Impact ◽  
Lower Noise

In raised floor data centers, tiles with high open area ratio or complex understructure are used to fulfill the demand of today’s high-density computing. Using more open tiles reduces pressure drop across the raised floor with the potential advantages of increased airflow and lower noise. However, it introduces the disadvantage of increased non-uniformity of airflow distribution. In addition, there are various tile designs available on the market with different opening shapes or understructures. Furthermore, a physical separation of cold and hot aisles (containment) has been introduced to minimize the mixing of cold and hot air. In this study, three types of floor tiles with different open area, opening geometry, and understructure are considered. Experimentally validated detail models of tiles were implemented in CFD simulations to address the impact of tile design on the cooling of IT equipment in both open and enclosed aisle configurations. Also, impacts of under-cabinet leakage on the IT equipment inlet temperature in the provisioned and under-provisioned scenarios are studied. Finally, a predictive equation for the critical under-provisioning point that can lead to a no-flow condition in IT equipment with weaker airflow systems is presented.

QoS-aware simulation job scheduling algorithm in virtualized cloud environment

2020 ◽  
Vol 11 (05) ◽  
pp. 2050048
Author(s):  
Zhen Li ◽  
Bin Chen ◽  
Xiaocheng Liu ◽  
Dandan Ning ◽  
Xiaogang Qiu
Keyword(s):  
Data Center ◽  
Job Scheduling ◽  
Scheduling Algorithm ◽  
Cloud Data Center ◽  
Cloud Data ◽  
High Productivity ◽  
Complex Simulation ◽  
Size Adjustment ◽  
Computational Service ◽  
Job Scheduling Algorithm

Cloud computing is attracting an increasing number of simulation applications running in the virtualized cloud data center. These applications are submitted to the cloud in the form of simulation jobs. Meanwhile, the management and scheduling of simulation jobs are playing an essential role to offer efficient and high productivity computational service. In this paper, we design a management and scheduling service framework for simulation jobs in two-tier virtualization-based private cloud data center, named simulation execution as a service (SimEaaS). It aims at releasing users from complex simulation running settings, while guaranteeing the QoS requirements adaptively. Furthermore, a novel job scheduling algorithm named adaptive deadline-aware job size adjustment (ADaSA) algorithm is designed to realize high job responsiveness under QoS requirement for SimEaaS. ADaSA tries to make full use of the idle fragmentation resources by tuning the number of requested processes of submitted jobs in the queue adaptively, while guaranteeing that jobs’ deadline requirements are not violated. Extensive experiments with trace-driven simulation are conducted to evaluate the performance of our ADaSA. The results show that ADaSA outperforms both cloud-based job scheduling algorithm KCEASY and traditional EASY in terms of response time (up to 90%) and bounded slow down (up to 95%), while obtains approximately equivalent deadline-missed rate. ADaSA also outperforms two representative moldable scheduling algorithms in terms of deadline-missed rate (up to 60%).

Exergy-Based Optimization Strategies for Multi-Component Data Center Thermal Management: Part I — Analysis

2005 ◽  
Author(s):  
Amip J. Shah ◽  
Van P. Carey ◽  
Cullen E. Bash ◽  
Chandrakant D. Patel
Keyword(s):  
Thermal Management ◽  
Data Center ◽  
Air Conditioning ◽  
Data Centers ◽  
Heat Dissipation ◽  
Real Data ◽  
Management Systems ◽  
Prior Work ◽  
Operating Strategy ◽  
Air Space

As heat dissipation in data centers rises by orders of magnitude, inefficiencies such as recirculation will have an increasingly significant impact on the thermal manageability and energy efficiency of the cooling infrastructure. For example, prior work has shown that for simple data centers with a single Computer Room Air-Conditioning (CRAC) unit, an operating strategy that fails to account for inefficiencies in the air space can result in suboptimal performance. To enable system-wide optimality, an exergy-based approach to CRAC control has previously been proposed. However, application of such a strategy in a real data center environment is limited by the assumptions inherent to the single-CRAC derivation. This paper addresses these assumptions by modifying the exergy-based approach to account for the additional interactions encountered in a multi-component environment. It is shown that the modified formulation provides the framework necessary to evaluate performance of multi-component data center thermal management systems under widely different operating circumstances.

Understanding the True Total Cost of Ownership of Water Cooling for Data Centers

2015 ◽  
Author(s):  
Dustin W. Demetriou ◽  
Vinod Kamath ◽  
Howard Mahaney
Keyword(s):  
Data Center ◽  
Data Centers ◽  
Heat Dissipation ◽  
Cost Effective ◽  
Electric Utility ◽  
Air Cooling ◽  
Water Cooling ◽  
Total Cost Of Ownership ◽  
Total Cost ◽  
Cost Of Ownership

The generation-to-generation IT performance and density demands continue to drive innovation in data center cooling technologies. For many applications, the ability to efficiently deliver cooling via traditional chilled air cooling approaches has become inadequate. Water cooling has been used in data centers for more than 50 years to improve heat dissipation, boost performance and increase efficiency. While water cooling can undoubtedly have a higher initial capital cost, water cooling can be very cost effective when looking at the true lifecycle cost of a water cooled data center. This study aims at addressing how one should evaluate the true total cost of ownership for water cooled data centers by considering the combined capital and operational cost for both the IT systems and the data center facility. It compares several metrics, including return-on-investment for three cooling technologies: traditional air cooling, rack-level cooling using rear door heat exchangers and direct water cooling via cold plates. The results highlight several important variables, namely, IT power, data center location, site electric utility cost, and construction costs and how each of these influence the total cost of ownership of water cooling. The study further looks at implementing water cooling as part of a new data center construction project versus a retrofit or upgrade into an existing data center facility.

Raised Floor Hybrid Cooled Data Center: Effect on Rack Inlet Air Temperatures When In Row Cooling Units are Installed Between the Racks

2015 ◽  
Author(s):  
Tianyi Gao ◽  
James Geer ◽  
Russell Tipton ◽  
Bruce Murray ◽  
Bahgat G. Sammakia ◽  
...  
Keyword(s):  
Flow Rate ◽  
Data Center ◽  
Heat Load ◽  
Data Centers ◽  
Cooling System ◽  
Air Flow ◽  
Inlet Temperature ◽  
Air Flow Rate ◽  
Air Temperatures ◽  
Cooling Unit

The heat dissipated by high performance IT equipment such as servers and switches in data centers is increasing rapidly, which makes the thermal management even more challenging. IT equipment is typically designed to operate at a rack inlet air temperature ranging between 10 °C and 35 °C. The newest published environmental standards for operating IT equipment proposed by ASHARE specify a long term recommended dry bulb IT air inlet temperature range as 18°C to 27°C. In terms of the short term specification, the largest allowable inlet temperature range to operate at is between 5°C and 45°C. Failure in maintaining these specifications will lead to significantly detrimental impacts to the performance and reliability of these electronic devices. Thus, understanding the cooling system is of paramount importance for the design and operation of data centers. In this paper, a hybrid cooling system is numerically modeled and investigated. The numerical modeling is conducted using a commercial computational fluid dynamics (CFD) code. The hybrid cooling strategy is specified by mounting the in row cooling units between the server racks to assist the raised floor air cooling. The effect of several input variables, including rack heat load and heat density, rack air flow rate, in row cooling unit operating cooling fluid flow rate and temperature, in row coil effectiveness, centralized cooling unit supply air flow rate, non-uniformity in rack heat load, and raised floor height are studied parametrically. Their detailed effects on the rack inlet air temperatures and the in row cooler performance are presented. The modeling results and corresponding analyses are used to develop general installation and operation guidance for the in row cooler strategy of a data center.

Parametric Study of Hybrid Cooling Solution for Thermal Management of Data Centers

2007 ◽  
Author(s):  
Veerendra Mulay ◽  
Saket Karajgikar ◽  
Dereje Agonafer ◽  
Roger Schmidt ◽  
Madhusudan Iyengar
Keyword(s):  
Thermal Management ◽  
Data Center ◽  
Parametric Study ◽  
Heat Load ◽  
Data Centers ◽  
High Heat ◽  
Inlet Temperature ◽  
Air Cooling ◽  
Air Conditioners ◽  
Hybrid Cooling

The power trend for Server systems continues to grow thereby making thermal management of Data centers a very challenging task. Although various configurations exist, the raised floor plenum with Computer Room Air Conditioners (CRACs) providing cold air is a popular operating strategy. The air cooling of data center however, may not address the situation where more energy is expended in cooling infrastructure than the thermal load of data center. Revised power trend projections by ASHRAE TC 9.9 predict heat load as high as 5000W per square feet of compute servers’ equipment footprint by year 2010. These trend charts also indicate that heat load per product footprint has doubled for storage servers during 2000–2004. For the same period, heat load per product footprint for compute servers has tripled. Amongst the systems that are currently available and being shipped, many racks exceed 20kW. Such high heat loads have raised concerns over limits of air cooling of data centers similar to air cooling of microprocessors. A hybrid cooling strategy that incorporates liquid cooling along with air cooling can be very efficient in these situations. A parametric study of such solution is presented in this paper. A representative data center with 40 racks is modeled using commercially available CFD code. The variation in rack inlet temperature due to tile openings, underfloor plenum depths is reported.

Thermal Management Issues in Operational Data Centers: Computational Fluid Dynamics Analysis and Experimental Study

2017 ◽  
Vol 9 (3) ◽  
Author(s):  
Babak Fakhim ◽  
Srinarayana Nagarathinam ◽  
Steven W. Armfield ◽  
Masud Behnia
Keyword(s):  
Thermal Management ◽  
Data Center ◽  
Data Centers ◽  
Electricity Consumption ◽  
Field Measurements ◽  
Hot Air ◽  
Computational Fluid Dynamics Analysis ◽  
Higher Power ◽  
Operational Data ◽  
Management Issues

The increase in the number of data centers in the last decade, combined with higher power density racks, has led to a significant increase in the associated total electricity consumption, which is compounded by cooling inefficiencies. Issues, such as hot air recirculation in the data center room environment, provide substantial challenges in thermal manageability. Three operational data centers have been studied to identify the cooling issues. Field measurements of temperature were obtained and were compared to numerical simulations to evaluate the overall thermal behavior of the data centers and to identify the thermal issues.

Expanded Assessment of a Practical Thermally Aware Energy-Optimized Load Placement Strategy for Open-Aisle, Air-Cooled Data Centers

2013 ◽  
Vol 135 (3) ◽  
Author(s):  
Dustin W. Demetriou ◽  
H. Ezzat Khalifa
Keyword(s):  
Real Time ◽  
Data Center ◽  
Data Centers ◽  
Thermal Environment ◽  
Inlet Temperature ◽  
Airflow Rate ◽  
Time Data ◽  
Improve Energy Efficiency ◽  
Air Temperatures ◽  
Holistic Optimization

This paper expands on the work presented by Demetriou and Khalifa (Demetriou and Khalifa, 2013, “Thermally Aware, Energy-Based Load Placement in Open-Aisle, Air-Cooled Data Centers,” ASME J. Electron. Packag., 135(3), p. 030906) that investigated practical IT load placement options in open-aisle, air-cooled data centers. The study found that a robust approach was to use real-time temperature measurements at the inlet of the racks to remove IT load from the servers with the warmest inlet temperature. By considering the holistic optimization of the data center load placement strategy and the cooling infrastructure optimization, for a range of data center IT utilization levels, this study investigated the effect of ambient temperatures on the data center operation, the consolidation of servers by completely shutting them off, a complementary strategy to those presented by Demetriou and Khalifa (Demetriou and Khalifa, 2013, “Thermally Aware, Energy-Based Load Placement in Open-Aisle, Air-Cooled Data Centers,” ASME J. Electron. Packag., 135(3), p. 030906) for increasing the IT load beginning with servers that have the coldest inlet temperature and finally the development of load placement rules via either static (i.e., during data center benchmarking) or dynamic (using real-time data from the current thermal environment) allocation. In all of these case studies, by using a holistic optimization of the data center and associated cooling infrastructure, a key finding has been that a significant amount of savings in the cooling infrastructure's power consumption is seen by reducing the CRAH's airflow rate. In many cases, these savings can be larger than providing higher temperature chilled water from the refrigeration units. Therefore, the path to realizing the industry's goal of higher IT equipment inlet temperatures to improve energy efficiency should be through both a reduction in air flow rate and increasing supply air temperatures and not necessarily through only higher CRAH supply air temperatures.

scholarly journals Server consolidation: A technique to enhance cloud data center power efficiency and overall cost of ownership

2021 ◽  
Vol 17 (3) ◽  
pp. 155014772199721
Author(s):  
Mueen Uddin ◽  
Mohammed Hamdi ◽  
Abdullah Alghamdi ◽  
Mesfer Alrizq ◽  
Mohammad Sulleman Memon ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Data Center ◽  
Data Centers ◽  
Scheduling Algorithm ◽  
Cloud Data Center ◽  
Server Consolidation ◽  
Cloud Data ◽  
Task Scheduling Algorithm ◽  
Cloud Data Centers

Cloud computing is a well-known technology that provides flexible, efficient, and cost-effective information technology solutions for multinationals to offer improved and enhanced quality of business services to end-users. The cloud computing paradigm is instigated from grid and parallel computing models as it uses virtualization, server consolidation, utility computing, and other computing technologies and models for providing better information technology solutions for large-scale computational data centers. The recent intensifying computational demands from multinationals enterprises have motivated the magnification for large complicated cloud data centers to handle business, monetary, Internet, and commercial applications of different enterprises. A cloud data center encompasses thousands of millions of physical server machines arranged in racks along with network, storage, and other equipment that entails an extensive amount of power to process different processes and amenities required by business firms to run their business applications. This data center infrastructure leads to different challenges like enormous power consumption, underutilization of installed equipment especially physical server machines, CO2 emission causing global warming, and so on. In this article, we highlight the data center issues in the context of Pakistan where the data center industry is facing huge power deficits and shortcomings to fulfill the power demands to provide data and operational services to business enterprises. The research investigates these challenges and provides solutions to reduce the number of installed physical server machines and their related device equipment. In this article, we proposed server consolidation technique to increase the utilization of already existing server machines and their workloads by migrating them to virtual server machines to implement green energy-efficient cloud data centers. To achieve this objective, we also introduced a novel Virtualized Task Scheduling Algorithm to manage and properly distribute the physical server machine workloads onto virtual server machines. The results are generated from a case study performed in Pakistan where the proposed server consolidation technique and virtualized task scheduling algorithm are applied on a tier-level data center. The results obtained from the case study demonstrate that there are annual power savings of 23,600 W and overall cost savings of US$78,362. The results also highlight that the utilization ratio of already existing physical server machines has increased to 30% compared to 10%, whereas the number of server machines has reduced to 50% contributing enormously toward huge power savings.

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