Efficiency In A Green Data Centre Assignment Sample

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Introduction of Efficiency In A Green Data Centre Assignment

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Green data centre is a system which is developed for data analyses, management and for the store purpose. These systems are meant to enhance the energy related productivity and reduce the impact which harms the environment. Green data centre related concepts are very important in management in the context of environment related issues. These systems are totally good for reducing the energy and the less uses of energy make fast and more useful to conventional servers. This servers related data centre used several types of technologies to manage the recovery related issues and handle the data related issues. Data centre related power facilities produce a massive heat which is wasted but this system uses advanced spray systems to cool down the system and handle the energy for better purpose.

2.0 Literature Review

2.1 Empirical studies

According to Luo, et al. 2018, data centers are a leading industrial segment which consumes a lot of energy on a worldwide basis. In this research paper, the researchers highlighted the heat related issues which were mainly used for technological related issues. They constructed a framework and formed decision making equipment which identified the relationship between resource related waste heat and energy related equilibrium and formed a strategy to reduce the heat related issues. In this research paper, they showed several types of research related works to enhance the energy related issues on the basis of data centers (Saha, 2018). The researchers constructed a framework to identify recovery related to waste heat issues. They proposed several types of steps of the framework to present the latest recommendations for data centers which are totally suitable for the related technology and restore the heat energy which is generally wasted. They constructed 4 steps to finalize the framework and gave a summary related to the case study and showed spatial appearance and risk related factors. They formed a theoretical and calculation methodology which was displayed to make the decision and provide support to the data centers to handle proper technology for the management of waste heat recovery.

According to Busby et al. 2021, in the context of expansion of the worldwide market for information related centers, the continuous change of cooling in data centers to more effective processes. In this research paper, they showed a thermal based analytical process which conducts the effective rear door exchangers related to the heat issues (Luo et al. 2019). The prime goal of the research paper was to realize the impacts of the heat related issues on the data centers and structured some skills which utilizes the heat related to the data centers and formed a field management to structuring the heat exchangers related aspects. In the context of causes related to the data centers, they discovered that the relationship between racks related to the high loaded and low loaded. The outcomes of the research paper provide support to understand the data centers related heat issues and consider some strategies to remodel the designs on the basis of the data centre. In this research paper, researchers showed the structuring of the information in the context of the server related to the data centre, room level, loaded and unloaded racks and as well as field work. Analyzing the data they discover that there was some waste heat identified in the server located in the velocity profile. They identified that fans played a significant role in the context of heat sink issues in the data centre (Alsulami et al. 2019). They also identified the different kinds of loaded risks on the basis of velocity and management strategy for the velocity in the research paper.

On the basis of Tijsma, 2019, green computing is the main concept of the research paper. He introduced green computing related methods and exercises in a wide way (Tijsma, 2019). He showed interrelationship between hazardous components and the products in the context of reducing the energy consumption. Various organizations took several strategies to decrease the dangerous effect on the environment as well as their operations. This research paper guided contemporary researchers to understand the significance of green computing for sustainable development. The researcher identified different types of issues on the basis of green computing. In this research paper, several types of surveys based on research methods had been taken. The researcher constructed some strategies for enhancing the data centre related heat issues and improved the cloud computing system. The researcher made the analytical process on different types of green computing problems and identified the connection between environment and technology related aspects, design based on green computing systems, information related to technology related aspects and different types of industrial sectors.

According to Cosar, 2019, there were several countries already managing their energy for more energy efficiency. Most organizations reuse the energy and materials and enhance the technology to use more energy. Organization related to economic aspects and enhancement conducts the green economy which improves the goods and services and reduces the physical elements and improves the ecosystem. The main perspectives of the green economy are to enhance the prosperity related to human behavior and equity related to the social traits. Low carbon level and efficiency is the main discussion here (Cosar, 2019). The organization in the context of economic development and green growth. In this research paper, assessment related to environmental sustainability is related to the IT segments and important public authors. There are different kinds of research based methodology to construct the theory including data collection, size of the sample, design of the sample, methods used for data collection; different types of processes related to the study are very important factors . According to the Alsulami 2019 (Alsulami et al. 2019), it investigated the previous research and produced the energy and the framework of efficiency of energy in information technology able to produce the green supply chain management. For sustainability they introduced very unique concept taxonomy.

According to Ayachi, 2018[4] Wang he introduced the mechanism that supports the maximum resources which are utilized by the use of active energy consumption by ending the minimization process of time.

 

2.2 Theories and models

There are several types of theories and models which enhance the efficiency of green data centers which includes theories based on deploy software, theory related to SLA, enhance the energy efficiency in the context of hardware structure, theory related to the improvement of the energy potency, and so on.

In the theory based on deploy software, different strategies must be accepted to manage the growth related information and reduce the storage capacity (Brindha et al. 2018). Data centre related workload is very significant to the data centre related aspects. In this theory, there are different types of solutions related to software like VMware, Microsoft Hypervisor and different types of strategies must be required for managing computing the data and improving efficiency. In the context of SLA, the main motive related to the data centre is to give the help to compute green data service and manage the business related necessity of end users. There are several key components for the better SLA services including promising providers, changing aspects according to time and also examining the delivery system related to the SLA services. In the theory related to the improved energy efficiency, virtualization is very important related to the business and as well as improving IT related to different types of sectors and organizations (Flachenecker and Rentschler, 2018). The technology related to virtualization is a very useful technique that provides the support to improve the software related system. In this theory, virtualization related to the server system reduces the heat issues to enhance the services. For better services, the system needs cooling equipment. Virtualization based on server systems provide and support to the organization to recover the information quickly from disaster (Ayachi et al. 2018). Better and improved infrastructure reduces the time and as well as power consumption and space. in the theory of data efficiency, metrics related aspects are mainly discussed. Metrics examine the process and act as an indicator of improvement. There are several types of characteristics which identify the data process which includes initiative process, specific metric name, different types of aspects related to granular analysis, providing data and metric data classification related to the economical, environmental and technological aspects. 

2.3 Literature gap

There is no doubt that the review of the literature is done with accuracy. But there are some barriers that exist. It could be better if there is much time to review the literature as more research works can be reviewed. It is discovered that some of the research works are outdated and that cannot be referred to as the review wants updated work in the context of present cases scenario. There is unauthentic data provided in some of the literature and that is a very big barrier to the researchers. There is a little bit of a hampered situation in finding literature in the situation of Covid-19 pandemic (Busby et al. 2021). Some of the literature does not provide the sufficient information related to the field which the researchers want and these factors take some time and become the obstacles to the literature. The gaps are also found in communication among the researchers during literature review (Green et al. 2018). So it is very clear and obvious that there is insufficient data/information or missing pieces of information or data in the research literature. Always every drawback does not mean the negative impact but for the completion of the research work it is important and careful analysis for the gap so that it is not generated or occurs during the research work (Nugradi, 2022). This literature gap is this kind of area for the particular project work from where the further scope of this particular project can be generated just because there is some loopholes are present from where the unexplored research work can be explored and the outdated research work may be dated through the further research work.

2.4 Dependent and Independent variables

To construct the theory, there are some variables which provide support to manage all the data, analyze it and make the final structure. The variables are classified into two segments like dependent and independent variables (Alam, 2019). The dependent variables include different types of key factors related to the thesis work and specific examined effects, several types of aspects related to the behavior and some strategies related to research work.

The independent variables included some factors which influence the behavioral motives, different types of technology related factors are one of the major key factors in the context of research related work.

2.5 Conceptual framework

Conceptual framework

(Source: Self-created)

3.0 Methodology

Methodology follows some important strategies and important procedures that consist of the entire procedure. With the abstract of green data technology and mobile computing the high speed data demanding intensive application has in turn led to complex architecture with high capacity services. Data centers basically consist of house components and end up with the combined cooling system and consume a very high amount of electricity that increases the application of carbon footprint . The demand of energy and data centres increases the information technology professionals and computing technology (Kachris et al. 2019). To estimate and intensify maximum performance of data is required. Multitude methods and quantifying energy intensity is required. The main aim of the computer system shifted from the computer system to its power and its energy (Mozaffari et al. 2020). Basically a green data centre is the process of a heated server. When the data computing computer is heated because of collecting data there is some method and applications are installed that reduce the generated head from the computer. Like in mobile and other data collecting devices the cache data is formed and due to the reason of cache data the computing system generates the head and slows down the speed (Mendez Escobar, 2019). So there are some processes and methods present that help to reduce the extra data and become the reason for the cooling factor. 

Research methodology framework

(Source: Self-created)

3.1 research approach

Researcher’s different approach and philosophy that leads to reach and help the strategic objective that gives the proper result. Several approaches are used in the research (Murphy, 2018). Some publications may use some research approach that implies the data collection method. In general there are two types of differences and these are quantitative methods and qualitative methods. For general plan and procedure different types of plan and view research approach are applied. Different types of approaches are deductive approach, inductive approach approach and adductive approach. The topicality of hypothesis distinctive points between deductive approach and inductive approaches is there (Sugesh and Sivasubramanian, 2021). Deductive approaches show us theories, assumptions and validity of the test in hand. On the other hand the inductive approach is the emergence of new generalizations and new theories. Surprising facts and puzzles in the research process is called adductive research.

3.2 research strategies

How a research is conveyed for solving an inquiry issue or how the research is done this whole systematic process or planned strategy is known as research strategies in methodology. It is acknowledged that there are different types of method research strategies. It has found that there are several ways in research strategies. The term methodology stands for research type, research approach, and Research framework and research paradigm. In research methodology there are two types of combination like data qualitative and data qualitative (Kasayanond, 2019). For data research the methodology provides specific frame work, and a more operative applied level. Quantitative methods consist of true experiment research that means the research is testing the hypothesis experiments, quasi experiment research that is single subject studies and correlation, non experimental research that means survey in methodology. On the other hand the qualitative research strategies consist of different types of research and these are phenomenological research, ethnological research, grounded theory research, narrative research, case study research.

3.3 data collection method

Data is necessary for any kind of research. Without data it is not possible to gain appropriate outcomes [23]. Data can be collected from different kinds of sources. One is the primary source and the other is the secondary source. Primary sources are necessary for the research. Some primary sources are interviews and surveys. Sometimes questionnaires are essential to gain the actual outcomes. On the other hand, some secondary sources are also required to gather various types of information. The website, journal papers are necessary to gather relevant information about the topic. Secondary data collection method is that kind of data collection method that has already been published in any book, journal or newspaper and any online portals. There is a huge amount of data about the specific research area in the particular business studies and irrespective of the nature of the research area. So application of an accurate set of data and its criteria selects the secondary data. That is used in the studies and plays an important role in terms of enhancing the research level reliability and research level validity. The criteria comprehend but not limited the published data (Li et al. 2019). The qualification of achieved data, discussions quality, source of data reliability, depth of analysis and the context of the collection of the particular text towards the development of the research work. Secondary data collection method is more convenient than the primary data collection method. Secondary data collection methods offer different types of advantages like reducing the data time and expenses, effort. Although the secondary data collection methods have big drawbacks like it does not produce the fresh or new data literature and its expansion.

Primary data is that kind of data that has not been published anywhere in terms of any book, journal, newspaper and online portals. Primary data is very unique and very convenient for finding the data. Generally primary data analysis and primary data collection is very time consuming and give more effort rather than a secondary data collection method. Primary data collection methods are two types one is quantitative and another one qualitative. Quantitative data collection methods are based on the mathematical data and mathematical expression and its formula. Methods of quantitative data collection basically analyze the data with questionnaires like median, mode, and mean and others. Qualitative data collection method is the opposite method of the quantitative method. There is no involvement of the mathematical approach. It makes the relation among the sounds, feelings, emotions and the elements of colors which belong to non quantitative approach.

 

3.4 Analysis technique

The techniques which help to break the problems into the elements are called analysis techniques. There are three methods which are used in this analytical part. They are the regression method, group method and the multiple equation method. This method helps to find out the way to solve the various problems [24]. It will help to manage the individual problem. Communication, creativity, research and data analysis are the parts of the analysis technique. The ability to interact with different people is called communication skill. It will increase the analytical skill of an individual. Creativity makes a person unique. They can develop things which are different from the others . The people who can think critically have the ability to better understand (Visa et al. 2020). There are many critical issues which need technical skill. Data analysis is mainly the part of statistics but there are many patterns of data analysis. This analytical part mainly needs a decision maker who can take the right decision with the help of the intelligent. Analytical part is basically important for finance management. Financial information is important to solve any problem. These problems are solved by the data analysts who are skilled in finance management [16]. As the discussion is about finance, investment is there. There is a relation between investment and finance. Communication is important to increase the ability of teamwork. The analytical issues are analyzed by the data analysts.

4.0 Results

Using the green data centre is convenient for reducing the heating issue of the computing system. In the green data centre there is huge traffic in the data centre. That is why the using server becomes slow and the huge workload is stuck and not performing well (Odun-Ayo et al. 2019). As a result, after using the green data centre it reduces the heating issue and reduces the heavy traffic. Traditionally the green data centre is used as a cooling factor for the computing data system and reducing the heat. To continue the whole process using the heavy power and that is why the system may heat since the power consumption for the process is very high for the IT infrastructure. But after using the green data centre, using power usage is somewhat low because using all the data is not only the data storage and all also it is used for different parts of the IT infrastructure (Tighe et al. 2013). Green data centers generate effective and efficient data making a minimal impact on the environment. So as a result green data centers produce the maximum output and focus on the protection to the environment. Decision support system and developing the designing and retrofitting of the data centre is developed by the green data computing system. It is very much clear and obvious for the IT professionals that they are facing a lot of data centers and also facing some same type of confusing environment. When they provide their reports and publish their project they are not aware of the needed power consumption particularly needed for this particular project or report purpose. A decision making support system provides some different mechanisms that aid and assist the decision making to a given domain and based on the existing knowledge and data related experience (Mendez Escobar, 2019). Basically a DSS or decision making support system is an application that is based on the computer and gives the application of the multifaceted issues and gives the respective solution to them. A decision making support system is considered as an analogous that plays an advisory role to the human experts and the expert nature. The capacity of data handled by the decision making system is considered the vital role beyond the efficiency of the human experts. For the sustainability purpose of the initiatives of the green data centre the decision making support system plays a very crucial role in every aspect of the fields such as management, mechanical engineering, routing aircraft, medicine farming and any other field.

5.0 Discussion

The heart of the IT driven company is green data centre. Consuming power for this is almost 10-100 megawatts and the cost of the company runs millions of dollars in one month. To mitigate large scale data consumption it has designed very carefully and it incorporates optimization operations. For the green data centre operations and designs are the competitive advantages. Today's generation are habituated with the internet and they all need the vast computing system for their particular work that is why they need the data centre and IT infrastructure (Tijsma, 2019). For the green data centre IT infrastructure is very crucial and it is the most important factor for the data centre because the data centre is used for storing huge data, and running server side huge workloads, and for scientific data processing. Since today's large data centers are consuming very high power consumption, almost 10-100 megawatt of electricity, which is why the equipment for energy consumption is very hungry. But this huge data is not used particularly in IT equipments. 50 percent of the power used for the power transmission, cooling for the IT equipment and other purposes [27]. For the green data centre there are different types of aspects present: they all together make the cooling process and make the IT infrastructure. These are power infrastructure, data centre design, data centre monitoring, cooling infrastructure etc. Power infrastructure basically distribution of small scale networks in the electric grid. Massachusetts Green High-Performance Computing Centre is used for the IT infrastructure according to its favorite geographical location to achieve its high energy operation. There are several thousand data centers and thousands of monitor power for infrastructure. The use of data monitor power is cooling the process and use of water in the facility (Murphy, 2018). In the earlier age the data centers were used for cooling and cooling the data centre but now it is also used for the cooling factor as well as for the low power usage effectiveness.

6.0 Conclusion

Though there are some gaps in literature review, it is very effective and useful for the research and it is important for the research work. The conceptual framework shows how one variable is dependent on other variables. The relation between the variables is also there in this literary work. The reviewed literature is very effective to provide support and enhance the knowledge of the subjects and very helpful to the research. Besides, the theories and models on the basis of the subject are very important in this chapter. The study in the context of green data centers which enhance the computing system and reduce the heat related issues to the servers. Less power consumption is very helpful to the servers and it is more efficient to improve the software related system and servers. In the context of the green data centre, recovery is related to the waste energy in the data centre. This system is very helpful to enhance the hardware related technology and develop the performance related operation and provide support to form the strategy to achieve the whole energy flow related to the data centre. Discussions of the literature gaps are also effective for development of the research.

Reference list

Books

  1. Flachenecker, F. and Rentschler, J. eds., 2018. Investing in resource efficiency: The economics and politics of financing the resource transition. Springer.
  2. Kachris, C., Falsafi, B. and Soudris, D. eds., 2019. Hardware Accelerators in Data Centers. Springer International Publishing.
  3. Visa, I., Duta, A., Moldovan, M., Burduhos, B. and Neagoe, M., 2020. Solar energy conversion systems in the built environment. Springer.

Journals

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  2. Cosar, M., 2019. Carbon footprint in data centre: A case study. Feb-Fresenius Environmental Bulletin, p.600.
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  4. Busby, M., GREEN, D. and COMBR?NCK, M., 2021. A Computational Fluid Dynamic Investigation of a Data Centre Employing Rear Door Heat Exchangers. Journal of Engineering Technology and Applied Sciences7(1), pp.1-30.
  5. Sugesh, R.J. and Sivasubramanian, A., 2021. Modelling and Analysis of a Plus-Shaped PN Junction Phase Shifter for Data Centre Applications. WSEAS Transactions on Electronics12, pp.32-37.
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  7. Alsulami, O.Z., Musa, M.O., Alresheedi, M.T. and Elmirghani, J.M., 2019, July. Visible light optical data centre links. In 2019 21st International Conference on Transparent Optical Networks (ICTON)(pp. 1-5). IEEE.
  8. Alam, K.M.M., 2019. Development of microcontroller based air quality monitoring system for data centre.
  9. Li, Y., Wen, Y., Tao, D. and Guan, K., 2019. Transforming cooling optimization for green data center via deep reinforcement learning. IEEE transactions on cybernetics50(5), pp.2002-2013.
  10. Saha, B., 2018. Green computing: Current research trends. International Journal of Computer Sciences and Engineering6(3), pp.467-469.
  11. Odun-Ayo, I., Eweoya, I., Toro-Abasi, W. and Bogle, S., 2019. Networking for Cloud and Data Centre-A Systematic Mapping Study.
  12. Murphy, A.R., 2018. Techno-Economic Study of an Energy Sharing Network Comprised of a Data Centre and MURBs for Cold Climate.
  13. Brindha, M.S., Sakthivel, N.K. and Subasree, S., 2018. IFF-DCN: an intelligent flow forecast technique for distributed centre networks in cloud data centers.
  14. Mendez Escobar, R., 2019. Master thesis: Assessment of the data centre equipment circular economy.
  15. Mozaffari, S., Badawy, G. and Down, D., 2020. Integrated Control of Multiple Data Centre Cooling Units. In 2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)(pp. 1-4). IEEE.
  16. Kasayanond, A., 2019. Environmental sustainability and its growth in Malaysia by elaborating the green economy and environmental efficiency. 670216917.
  17. Chávez Tapia, M.A., Multicore fibre-based clock synchronisation for ultra-low latency data centre interconnections.
  18. Green, M.A., Hishikawa, Y., Dunlop, E.D., Levi, D.H., Hohl?Ebinger, J. and Ho?Baillie, A.W., 2018. Solar cell efficiency tables (version 52). Progress in Photovoltaics: Research and Applications26(7), pp.427-436.
  19. Nugradi, D.N.A., 2022. Post occupancy evaluation for green building certificated (case study UGM law centre building). In IOP Conference Series: Earth and Environmental Science(Vol. 969, No. 1, p. 012065). IOP Publishing.
  20. Haghighatafshar, S., Nordlöf, B., Roldin, M., Gustafsson, L.G., la Cour Jansen, J. and Jönsson, K., 2018. Efficiency of blue-green stormwater retrofits for flood mitigation–Conclusions drawn from a case study in Malmö, Sweden. Journal of environmental management207, pp.60-69.
  21. Tighe, M., Keller, G., Shamy, J., Bauer, M. and Lutfiyya, H., 2013, October. Towards an improved data centre simulation with DCSim. In Proceedings of the 9th International Conference on Network and Service Management (CNSM 2013)(pp. 364-372). IEEE.
  22. Nama, G.F., Komarudin, M., Priambodo, H. and Septama, H.D., 2014. Electricity, Temperature, and Network Utilization Monitoring at University of Lampung Data Centre Using Low Cost Low Power Single Board Mini Computer.
  23. Dhiab, I., Barouni, Y., Khalfallah, S. and Slama, J.B.H., 2019. Performance evaluation of a hybrid IP/SDN network in data centre network architectures. IET Communications13(9), pp.1185-1191.
  24. Jayathilake, S.A.N.J.E.E.W.A., 2019. Artificial Intelligence and Machine Learning Approach to Measuring Energy Consumption in Data Centre Facilities(Doctoral dissertation, University of East London).
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