WO2013124864A1

WO2013124864A1 - A method and system for cooling optimization of a data center

Info

Publication number: WO2013124864A1
Application number: PCT/IN2013/000104
Authority: WO
Inventors: Harshad Bhagwat; Amarendra K Singh; Sankara Narayanan D; Rajesh Jayaprakash; Anand Sivasubramaniam
Original assignee: Tata Consultancy Services Limited
Priority date: 2012-02-21
Filing date: 2013-02-18
Publication date: 2013-08-29

Abstract

A method and system is provided for fast and complete optimization of operational parameters of computer room air conditioners (CRAC) of data center using a fast thermal model for cooling optimization. Particularly, the invention provides a method and system for accepting user input for values of one or more operational parameters of the said data center; predicting temperatures at various locations of the said data center; carrying out thermal evaluation for detecting potential thermal problems and identifying reasons behind these problems and cooling inefficiencies; optimizing operational parameters of CRAC for the data center using an optimizer and fast thermal model and generating recommendations regarding optimized operational parameters for mitigating potential thermal problems and cooling inefficiencies.

Description

A METHOD AND SYSTEM FOR COOLING OPTIMIZATION OF A DATA

CENTER

FIELD OF THE INVENTION

The present invention relates to thermal management of data centers. Particularly the invention provides a method and system for cooling optimization of data centers using a fast thermal model.

BACKGROUND OF THE INVENTION

A data center typically contains electronic equipment such as servers, telecom equipment, networking equipment, switches and other electronic equipment which are arranged on racks or frames. The heat generated by such electronic components is cooled with the help of cooling units. Typically, the cooling units are computer room air conditioners (CRAC) or computer room air handlers (CRAH) which supply cold air for cooling. More advanced cooling units such as in-row coolers, rear door coolers, liquid cooled cabinets and chip cooling techniques have now come into practice.

Data centers are considered as energy guzzlers. With the drastic increase in energy cost, the huge energy consumption is one of the major concerns of the data center managers. Power consumed by cooling equipments contributes to a major portion of the total data center power consumption. The main challenge is to ensure safety of electronic equipments by ensuring appropriate temperatures in the data center and at the same time ensuring optimum cooling efficiency of the data center. Due to poor design of the data center, data center mangers may face a lot of problems such as hot spots, low tile flow rates and so on. General measures which may be taken to handle the problem are decreasing supply temperature of cooling units, increasing cooling capacity near the problem area and so on. These measures may decrease the cooling efficiency. Cooling capacity of the data center is designed and typically run for maximum heat load conditions. In practice, data centers rarely operated at maximum conditions. Typically, cooling units are controlled according to heat loads in a very elementary manner. Workload placement decisions are taken without considering cooling related issues such as cooling availability, inlet temperatures and so on. All these practices lead to poor cooling efficiency. In addition, due to the obvious urge to increase space utilization of the data center, consolidation and virtual izati on exercises are being carried out. Existing cooling infrastructure of a data center may not be sufficient to take concentrated heat loads resulting due to consolidation and virtualization. Data center managers face number of such major challenges in thermal management of data centers.

Various attempts are being made to minimize the cooling costs i.e. cooling optimization of the data center. Some of these attempts include transformation of old data centers, efficient design of new data centers, dynamic controlling of cooling units, consolidation and temperature aware workload scheduling. Efficient design of data center includes proper arrangement of racks, tiles, CRACs etc., adequate plenum depth, use of airflow management techniques such as aisle containment, CRAC operation moderation etc. Problems which are being faced in old data centers such as hot spots and low cooling efficiency have been solved by carrying out design and operational changes. Efficient control schemes have been developed to control parameters of CRAC such as supply temperature, supply flow rate or tile flow rates in accordance to changes in heat generation, temperature, pressure and airflows in the data center. These control schemes make CRAC run at optimum efficiency while maintaining satisfactory temperatures in the data center at the same time. Different algorithms for workload placements have been developed which take cooling related parameters such as recirculation, CRAC capacities into account while executing placement requests. Numerical models such as Computational Fluid Dynamics (CFD) models and data based models using neural networks are being used to facilitate these attempts. During cooling optimization of data center, design as well as operational parameters is altered to find the optimum configuration. Especially, while optimizing legacy data center, only few design parameters are allowed to be changed due to various constraints. On the other hand, a data center manager can easily alter operational parameters of CRAC for quick and easy mitigation of thermal problems like hot spots and increasing cooling efficiency. Hence more focus during optimization of these legacy data centers is on optimizing operational parameters of CRAC. Hence, optimizing operational parameters of CRAC is typical and easy way for cooling optimization of data center.

Existing attempts for optimization of data center use CFD models to quantify the cooling characteristics of the data center. Typically, local search is used for optimization using CFD models. This local search starts with a base configuration. CFD model is used to analyze this base configuration to get guidance about next better configuration. Then this next configuration is analyzed with CFD model to get guidance about next configuration. As the time for CFD computations are high, above method is iterated only few times and the best configuration obtained from these iterations is considered as the optimum configuration. In an alternative method, few potential configurations are selected and are analyzed using CFD models. The best configuration amongst these configurations is considered as the optimum configuration. It can be seen that, in both of these methods, higher time of CFD computations is causing the search to stop at sub-optimal or locally optimal configuration. Globally optimum configuration cannot be obtained using any of these methods. Due to computational time of CFD models, a formal optimizer cannot be used for complete optimization of a typical large data center.

Hence there is a need for a method and system which could use a fast thermal model for quick prediction of cooling characteristics and a formal optimizer so as to quickly and fully optimize the operating parameters of CRAC of the data center. There is a need to devise a solution which can be used for optimization of operational parameters of CRAC of the data center. OBJECTIVES OF THE INVENTION

In accordance with the present invention, the primary objective is to provide a method and system for cooling optimization of data centers using a fast thermal model.

Another objective of the invention is to provide a method and system for fast and complete optimization of operational parameters of CRAC of data center using a fast thermal model and a formal optimizer for cooling optimization.

Another objective of the invention is to provide a method and system for accepting user input for values of one or more operational parameters of the said data center.

Another objective of the invention is to provide a method and system for predicting temperatures at various locations of the said data center using the fast thermal model.

Another objective of the invention is to provide a method and system for thermal evaluation for detecting potential thermal problems in the said data center and causes behind these problems and cooling inefficiencies.

Another objective of the invention is to provide a method and system for optimizing operational parameters of CRAC for the data center using an optimizer and a fast thermal model and generating corresponding recommendations regarding optimized operational parameters of CRAC for mitigating potential thermal problems and cooling inefficiencies.

SUMMARY OF THE INVENTION

Before the present methods, systems, and hardware enablement are described, it is to be understood that this invention in not limited to the particular systems, and methodologies described, as there can be multiple possible embodiments of the present invention which are not expressly illustrated in the present disclosure. It is also to be understood that the terminology used in the description is for the purpose of describing the particular versions or embodiments only, and is not intended to limit the scope of the present invention.

The present invention provides a method and system for cooling optimization of data centers using a fast thermal model.

In an embodiment of the invention a method and system is provided for fast and complete optimization of operational parameters of CRAC of data center using a fast thermal model and a formal optimizer for cooling optimization.

In an embodiment of the invention a method is provided for accepting user input for values of one or more operational parameters of the said data center; predicting temperatures at various locations of the said data center using the fast thermal model; carrying out thermal evaluation of data center for detecting potential thermal problems and identifying causes behind these thermal problems and cooling inefficiencies; optimizing operational parameters of CRAC for the data center using an optimizer and fast thermal model; generating recommendations regarding optimized operational parameters of CRAC for mitigating potential thermal problems and cooling inefficiencies.

In an embodiment of the invention a system is provided for cooling optimization of data centers using a fast thermal model, the system comprising of an User Input Module, adapted to accept user input for values of one or more operational parameters of the said data center; an Temperatures Prediction Module, adapted to predict temperatures at various locations of the said data center using the fast thermal model; an Thermal Evaluation Module, adapted to carry out thermal evaluation for detecting potential thermal problems and identifying causes behind these thermal problems and cooling inefficiencies; Optimization and Recommendation Generation Module, adapted to optimize operational parameters of CRAC for the data center using an optimizer and fast thermal model and to generate corresponding recommendations regarding optimized operational parameters for mitigating potential thermal problems and cooling inefficiencies; an Output Module, adapted to output the results of thermal evaluation and generated recommendations and a Cooling Power Calculation Module, adapted to calculate cooling power.

The above said method and system are preferably for cooling optimization of data centers using a fast thermal model but also may be used for many other applications.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing summary, as well as the following detailed description of preferred embodiments, are better understood when read in conjunction with the appended drawings. For the purpose of illustrating the invention, there is shown in the drawings exemplary constructions of the invention; however, the invention is not limited to the specific methods and system disclosed. In the drawings:

Figure 1: shows a flow diagram of the process for cooling optimization of data centers using a fast thermal model.

Figure 2: shows a system diagram for cooling optimization of data centers using a fast thermal model.

DETAILED DESCRIPTION OF THE INVENTION

Some embodiments of this invention, illustrating all its features, will now be discussed in detail.

The words "comprising," "having," "containing," and "including," and other forms thereof, are intended to be equivalent in meaning and be open ended in that an item or items following any one of these words is not meant to be an exhaustive listing of such item or items, or meant to be limited to only the listed item or items. It must also be noted that as used^ herein, the singular forms "a," "an," and "the" include plural references unless the context clearly dictates otherwise. Although any systems and methods similar or equivalent to those described herein can be used in the practice or testing of embodiments of the present invention, the preferred, systems and methods are now described.

The disclosed embodiments are merely exemplary of the invention, which may be embodied in various forms.

The present application provides a method for cooling optimization of a data center, the said method comprises processor implemented steps of: a. accepting user input for values of one or more operational parameters of the said data center;

b. predicting temperatures at various locations of the said data center using a fast thermal model;

c. carrying out thermal evaluation for detecting potential thermal problems in the said data center and identifying causes behind said thermal problems and cooling inefficiencies;

d. optimizing operational parameters of CRAC for the data center using an optimizer and the fast thermal model and generating recommendations regarding optimized operational parameters of CRAC for mitigating said potential thermal problems and cooling inefficiencies; and

e. outputting the results of said thermal evaluation and generating recommendations. The present application provides a system for cooling optimization of data centers, the system comprising of: a. an User Input Module (202), adapted to accept user input for values of one or more operational parameters of the said data center; b. a Temperature Prediction Module (204), adapted to predict temperatures at various locations of the said data center using a fast thermal model;

c. a Thermal Evaluation Module (206), adapted to carry out thermal evaluation for detecting potential thermal problems in the said data center and identifying causes behind said thermal problems and cooling inefficiencies;

d. an Optimization and Recommendation Generation Module (208), adapted to optimize operational parameters of CRAC for the data center using an optimizer and fast thermal model and generating recommendations regarding optimized operational parameters of CRAC for mitigating potential thermal problems and cooling inefficiencies ;

e. an Output Module (210), adapted to output the results of thermal evaluation and generated recommendations; and

f. a Cooling Power Calculation Module (212), adapted to calculate cooling power.

The present application uses specific terminologies such as computer room air conditioners (CRAC), rack, etc. only for simplicity. The subject matter of the present application is applicable to any type of electronic equipment such as servers, networking equipment, telecommunications equipment etc. arranged in any fashion, any type of air delivery mechanism such as raised floor, overhead ducts etc., any type of air cooling infrastructure and any type of cooling units.

The data center may contain plurality of racks, housing various electronic and electric equipment and the racks may be arranged in rows. Heat generated by the electronic and electric equipment is cooled by CRACs which are situated near periphery of the data center. These CRACs enable cold air to flow into the under-floor plenum. This cold air is delivered to intended places (e.g. fronts of racks) through tiles or vents. The equipment typically has fans for taking in cold air. This air picks up the heat generated and the hot air is exhausted. Some of the hot air is returned back to CRAC and some of this hot air may mix with cold air from tiles and recirculated into inlets of equipment. This recirculation of hot air may cause rising of temperature at inlets of racks above recommended temperature suggested by manufacturer of equipment. These locations of high temperatures are called hot spots. The heat generation inside racks change with time depending upon amount of workload put onto the equipment inside the racks.

The present invention provides a method for quick and complete optimization of operational parameters of CRAC. The invention takes various inputs from user. It then uses a fast thermal model which calculates temperatures at various locations in the data center like at rack inlets. Then thermal evaluation of data center is carried out to detect any thermal problems. The method also does causal analysis to find out reasons behind these thermal problems and cooling inefficiencies. This fast thermal model is coupled with an optimizer to find the optimum operational parameters of CRAC. This optimization is aimed at mitigating thermal problems if any and optimizing cooling efficiency. The present invention utilizes the concept of influence indices for fast prediction of temperatures and for causal analysis of thermal problems, the disclosure of which is incorporated herein by reference of Indian patent application 652/MUM/201 1 and US patent application 13/234,763.

Referring to Figure 1 is a flow diagram of the process for cooling optimization of data centers using a fast thermal model.

The process starts at the step 102, user input are accepted for values of one or more operational parameters of the said data center. At the step 104, temperatures are predicted at various locations of the said data center using the fast thermal model. At the step 106, thermal evaluation is carried out for detecting potential thermal problems in the said data center and reasons behind these thermal problems and cooling inefficiencies are identified. At the step 108, the operational parameters of CRAC are optimized for the data center using an optimizer and a fast thermal model for mitigation of said thermal problems and cooling inefficiencies. Precise recommendations regarding optimized operational parameters of CRAC are generated. The process ends at the step 110 in which the results of thermal evaluation and recommendations regarding optimum operational parameters of CRAC are outputted to the user.

Referring to Figure 2 is a system diagram for cooling optimization of data centers using a fast thermal model.

In an embodiment of the invention, the said system (200) for cooling optimization of data centers using a fast thermal model comprising an User Input Module (202); a Temperatures Prediction Module (204); a Thermal Evaluation Module (206); an Optimization and recommendations generation Module (208); an Output Module (210); and a Cooling Power Calculation Module (212).

The method and system of the present invention describes optimization of the operational parameters of CRAC of the data centers. It should be noted that following discussion only illustrates one of the procedures to be used. These procedure may need some modification, some parts may be added, removed or the process may be used iteratively while actual use. Some of the steps may involve offline work to be carried out. For example, calculation of influence indices using CFD model may take longer time to be used for operational mode, hence this calculation may be done offline. The database of influence indices is then saved for use during actual operation of the presented method and system.

In an embodiment of the invention, the User Input Module (202) is adapted to accept user input for values of one or more operational parameters of the said data center. The said one or more operational parameters of the said data center are selected from the group comprising of but not restricted to power consumption of racks and CRAC supply temperatures and CRAC flow rates. Inputs from a user regarding power consumption of racks, CRAC supply temperatures and CRAC flow rates are entered. The said power consumption of racks, CRAC supply temperature and CRAC flow rates values may be measured values, typical values or maximum values. This user input module can be graphic or text based interface. The user can supply the inputs either one by one or as an ordered list. In an embodiment of the invention, the Temperatures Prediction Module (204) is adapted to predict temperatures at various locations like rack inlets, CRAC inlets of the said data center using the fast thermal model. This fast thermal model makes use of method of prediction of temperatures, the disclosure of which is incorporated herein by reference of Indian patent application 652/MUM/201 1 and US patent application 13/234,763. The temperatures at various locations in the data center are predicted using the fast thermal model and further a temperature map of the predicted temperature at various locations in the data center is also prepared using the said fast thermal model.

In an embodiment of the invention, the Thermal Evaluation Module (206) is adapted to carry out thermal evaluation of data center. The predicted temperatures are analyzed to detect potential thermal problems. The predicted temperatures at various locations of the said data center are checked for potential hot spots in the said data center. The inputted values of temperatures or predicted temperatures at supply and returns of one or more CRACs are also checked for overloading of the said CRACs by calculating the amount of cooling provided by said CRACs. In an alternate embodiment of the invention, influence indices of the said data center are causally analyzed for detecting potential causes of thermal problems or cooling inefficiencies. For example, if hot spot is determined after analysis of predicted temperature, exact cause of the hot spots may be determined from appropriate influence indices. The influence indices are used for causal analysis of thermal problem, the disclosure of which is incorporated herein by reference of Indian patent application 652/MUM/201 1 and US patent application 13/234,763.

Further, the Optimization and Recommendation Generation Module (208) is adapted to optimize operational parameters of CRAC for the data center using an optimizer and the fast thermal model to generate corresponding recommendations. These recommendations are aimed towards either mitigation of thermal problems like hot spots or improvement of cooling efficiency of the data center. In one of the embodiments of the invention, precise recommendations regarding CRAC supply temperatures may also be generated. A combination of CRAC supply temperatures is a tuple consisting of ordered list of supply temperatures of the individual CRACs. The optimum CRAC supply temperature combination is the one that maintains the rack inlet temperatures just below predefined threshold at the same time ensures minimum cooling power. The rack temperatures for a particular combination of CRAC supply temperatures are predicted using said fast thermal model. The predefined threshold is set for each rack according to the type of equipment housed in the racks.

In an embodiment of the invention, the Cooling Power Calculation Module (212) is adapted to calculate cooling power. The cooling power may be calculated using a relation between cooling power such as power consumed by the CRACs and CRAC supply temperature and CRAC flow rate. This relation may be determined by using component level models, manufacturer's data or by experimentation.

Exhaustive searching may be used to search for the optimum combination of CRAC supply temperatures from all possible combinations. The complexity of this exhaustive search and time taken for searching, increases rapidly with the number of CRACs and racks. Better techniques such as Genetic algorithm, Hill climbing, Simulated annealing etc. may be used for fast and efficient searching.

In one of the embodiments of the invention, genetic algorithm is used for searching optimum combination of CRAC temperatures. A genetic algorithm (GA) is a search heuristic that mimics the process of natural evolution. This heuristic is routinely used to generate useful solutions to optimization and search problems. Genetic algorithms belong to the larger class of evolutionary algorithms (EA), which generate solutions to optimization problems using techniques inspired by natural evolution, such as inheritance, mutation, selection, and crossover. The evolution usually starts from a population of randomly generated individuals and happens in generations. In each generation, the fitness of every individual in the population is evaluated, multiple individuals are stochastically selected from the current population (based on their fitness), and modified (recombined and possibly randomly mutated) to form a new population. The new population is then used in the next iteration of the algorithm. Commonly, the algorithm terminates when either a maximum number of generations has been produced, or a satisfactory fitness level has been reached for the population.

In this case, randomly selected combinations of CRAC supply temperatures called the initial population are evolved to get to the optimum CRAC supply temperature combination which gives minimum cooling power and keeps rack inlet temperatures just below threshold. Genetic algorithm based optimizer might struggle to evolve towards the optimal solution if the number of supply temperature combinations which don't contribute to any hot spots for the given power levels are very less. But the reliability of this algorithm maybe improved by adding to the initial population, a supply temperature combination that doesn't contribute to any hot spots while corresponds to relatively less cooling power, before starting the evolution. This combination can be found by predicting the rack temperatures for a small specific range of CRAC supply temperature combinations for the given power levels and choosing the CRAC supply temperature combination that doesn't contribute to any hot spots while corresponding to least cooling power. The determination of optimum CRAC supply temperatures is completed within few seconds because of use of fast thermal model and a formal optimizer such as genetic algorithm. Genetic algorithm based optimizer explained above may be implemented using any computer programming language such as C, C++, JAVA, etc.

In another embodiment of the invention, Optimization and Recommendation Generation Module may be adapted to optimize CRAC flow rates as well. In this case, Temperatures Prediction Module and Thermal Evaluation Module use appropriate set of influence indices calculated for that particular combination of CRAC flow rates. Optimization and Recommendation Generation Module then may determine optimum combination of CRAC supply temperatures for many combinations of CRAC flow rates and the combination which gives lower cooling power and keeps rack inlet temperatures below threshold may be chosen as optimum combination of CRAC flow rates along with optimum combination of CRAC supply temperature.

The Output Module (210) is adapted to output the results of thermal evaluation and generated recommendations for optimized operational parameters of CRAC for mitigating potential thermal problems and cooling inefficiencies. The output module may be graphical or text based. The thermal map prepared from predicted temperatures and calculated cooling power for the optimum recommendation may also be shown on the output module.

The machine may comprise a server computer, a client user computer, a personal computer (PC), a tablet PC, a laptop computer, a desktop computer, a control system, a network router, switch or bridge, or any machine capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by that machine. Further, while a single machine is illustrated, the term "machine" shall also be taken to include any collection of machines that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methodologies discussed herein.

The machine may include a processor (e.g., a central processing unit (CPU), a graphics processing unit (GPU, or both), a main memory and a static memory, which communicate with each other via a bus. The machine may further include a video display unit (e.g., a liquid crystal displays (LCD), a flat panel, a solid state display, or a cathode ray tube (CRT)). The machine may include an input device (e.g., a keyboard) or touch-sensitive screen, a cursor control device (e.g., a mouse), a disk drive unit, a signal generation device (e.g., a speaker or remote control) and a network interface device.

Dedicated hardware implementations including, but not limited to, application specific integrated circuits, programmable logic arrays and other hardware devices can likewise be constructed to implement the methods described herein. Applications that may include the apparatus and systems of various embodiments broadly include a variety of electronic and computer systems. Some embodiments implement functions in two or more specific interconnected hardware modules or devices with related control and data signals communicated between and through the modules, or as portions of an application-specific integrated circuit. Thus, the example system is applicable to software, firmware, and hardware implementations.

In accordance with various embodiments of the present disclosure, the methods described herein are intended for operation as software programs running on a computer processor. Furthermore, software implementations can include, but not limited to, distributed processing or component/object distributed processing, parallel processing, or virtual machine processing can also be constructed to implement the methods described herein.

The illustrations of arrangements described herein are intended to provide a general understanding of the structure of various embodiments, and they are not intended to serve as a complete description of all the elements and features of apparatus and systems that might make use of the structures described herein. Many other arrangements will be apparent to those of skill in the art upon reviewing the above description. Other arrangements may be utilized and derived therefrom, such that structural and logical substitutions and changes may be made without departing from the scope of this disclosure. Figures are also merely representational and may not be drawn to scale. Certain proportions thereof may be exaggerated, while others may be minimized. Accordingly, the specification and drawings are to be regarded in an illustrative rather than a restrictive sense.

The preceding description has been presented with reference to various embodiments. Persons skilled in the art and technology to which this application pertains will appreciate that alterations and changes in the described structures and methods of operation can be practiced without meaningfully departing from the principle, spirit and scope.

Claims

WE CLAIM:

1. A method for cooling optimization of a data center, the said method comprises processor implemented steps of: a. accepting user input for values of one or more operational parameters of the said data center;

e. outputting the results of said thermal evaluation and generated recommendations.

2. The method as claimed in claim 1, wherein the said one or more operational parameters of the said data center are selected from the group comprising of but not restricted to power consumption of racks, CRAC supply temperatures and CRAC flow rates.

3. The method as claimed in claim 2, wherein the said power consumption of racks, CRAC supply temperature and CRAC flow rates values are selected from the group comprising of but not restricted to measured values, typical values or maximum values.

4. The method as claimed in claim 1, further comprises of checking predicted temperature at various locations of the said data center for potential hot spots in the said data center.

5. The method as claimed in claim 1, further comprises of checking inputted values of temperatures or predicted temperatures at supply and returns of one or more CRACs for overloading of the said CRACs by calculating the amount of cooling provided by said CRACs.

6. The method as claimed in claim 1, wherein the said various locations of the said data center are selected from the group comprising of but not restricted to rack inlets or CRAC inlets.

7. The method as claimed in claim 1, further comprises of predicting cooling characteristics in terms of influence indices.

8. The method as claimed in claim 1, further comprises of analyzing influence indices of the said data center causally for detecting potential causes of said thermal problems or cooling inefficiencies.

9. The method as claimed in claim 8, wherein the said influence indices are used to develop said fast thermal model.

10. The method as claimed in claim 1, further comprises of preparing temperature map of the predicted temperature at various locations in the data center using the fast thermal model.

1 1. The method as claimed in claim 1, further comprises of calculating cooling power of the said data center using a relation between input power of the CRAC and CRAC supply temperature and CRAC flow rate.

12. An system for cooling optimization of data centers, the system comprising of: a. an User Input Module (202), adapted to accept user input for values of one or more operational parameters of the said data center;

b. a Temperature Prediction Module (204), adapted to predict temperatures at various locations of the said data center using a fast thermal model; c. a Thermal Evaluation Module (206), adapted to carry out thermal evaluation for detecting potential thermal problems in the said data center and identifying causes behind said thermal problems and cooling inefficiencies; d. an Optimization and Recommendation Generation Module (208), adapted to optimize operational parameters of CRAC for the data center using an optimizer and fast thermal model and generating recommendations regarding optimized operational parameters of CRAC for mitigating potential thermal problems and cooling inefficiencies ;

13. The system as claimed in claim 12, wherein the said Cooling Power Calculation Module (212) calculate cooling power using a relation between cooling power such as power consumed by CRAC and CRAC supply temperature and CRAC flow rate.

14. The system as claimed in claim 12, wherein the said Output Module (210) output the results of thermal evaluation and generated recommendations in graphical or text based interface.

15. The system as claimed in claim 12, wherein the said Output Module (210) further comprises of displaying thermal map derived form predicted temperatures and calculated cooling power for the optimum recommendation.