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sustainable-computing

The rise of cloud computing and storage has led to a proliferation of data centers in the past decade, making them a significant contributor to energy waste and pollution. These facilities require massive amounts of energy to keep them cool and process data, often consuming more power than a small city. The energy is primarily generated by burning fossil fuels, releasing carbon dioxide and other pollutants into the atmosphere. To address this issue, sustainable computing practices are necessary for reducing energy waste in data centers.

1. Introduction

Sustainable computing aims to use computing resources in a way that offers a net-zero impact on the environment by implementing policies, procedures, programs, and attitudes that encompass all aspects of technology use. The goal is to encourage entities that host data centers to reduce the consumption of resources, use energy efficiently, and reduce waste. For example, businesses can invest in renewable energy sources and more efficient cooling systems to reduce the environmental footprint of their data centers.

2. Reducing Energy Waste in Data Centers

Undoubtedly, data centers consume enormous amounts of energy, leading to energy waste. However, by implementing sustainable computing practices, organizations can reduce energy consumption and lower energy costs. Below are various ways to achieve this.

2.1. Server Consolidation

Server consolidation at a data center involves combining servers into one server to achieve the same function. In other words, server consolidation enables organizations to use fewer servers to host data. Removing unneeded servers can help decrease energy consumption and reduce maintenance costs. Furthermore, it ensures less cooling and efficient use of one server and helps businesses standardize their systems and operations [@chandrasekaran2019, @chandrasekaran2020].

2.2. Use Energy-Efficient Servers

Modern servers come with power management tools that can help monitor energy use and ensure data centers use as little energy as possible. For example, some servers have DC voltage regulators, which keep the output voltage of a power supply within a specific range. The device can convert a high voltage into a lower one or maintain a fixed voltage output to ensure energy efficiency [@peng2018].

2.3. Implement Automatic Shutdowns

Many data centers have idle or hung servers that do not deliver information or computing services. For example, any server that has not relayed information for more than three months is idle. Thus, companies can implement automatic shutdowns of servers that have not been in use for a long time. This will allow idle servers to draw less electricity, put less strain on the energy grid, and save on energy costs [@mukherjee2015].

2.4. Use Virtualization

Virtualization allows data centers to combine multiple applications and run them on the same server. Reducing the number of physical servers reduces their electrical consumption. As a result, it will help save the equipment’s operational costs and reduce the load on the HVAC system to keep the server room at an optimal temperature [@siddiqui2020].

2.5. Use Renewable Energy Sources

Organizations can use renewable energy sources to power their data centers. Examples of renewable sources include geothermal, wind, and solar energy. They can also take advantage of biomass or waste-to-energy strategies to reduce reliance on electricity from the national grid. This strategy can save energy costs and benefit the environment [@khalilpourazari2018].

2.6. Use Energy-Efficient UPS Systems

An energy-efficient UPS (uninterruptible power supply) system can minimize electrical losses in a server. It can save energy by running in its dedicated energy-saving mode, referred to as ECO mode. UPS systems use batteries as the emergency power source in the event of a utility power disruption at a data center. Organizations can also maximize the energy efficiency of their UPS systems by continuously monitoring the UPS system to establish load patterns [@mukherjee2015].

2.7. Data Compression

Data compression involves encoding, restricting, converting, or modifying data to reduce size. While data compression saves storage capacity, it also speeds up file transfer and reduces storage hardware costs. Less hardware means less energy consumption [@rosen2019].

3. Conclusion

Sustainable computing is a crucial part of reducing energy waste in data centers. Organizations that host data centers can achieve sustainable computing by implementing energy-efficient hardware and software. They can also use power management tools, such as DC voltage regulators, virtualize their systems, and use renewable energy sources. Reducing energy waste in data centers can also reduce energy and maintenance costs and efficiently use servers.

The topic of data compression is not directly related to energy waste reduction. However, it can contribute to reducing energy consumption by reducing the amount of storage hardware needed in data centers.

4. References

@chandrasekaran2019 Chandrasekaran, K., Arunraj, N. S., & Akbarsha, M. A. (2019). Energy efficient server consolidation with temperature-aware workload allocation and dynamic voltage and frequency scaling. Journal of Ambient Intelligence and Humanized Computing, 10(6), 2237-2250. doi: 10.1007/s12652-019-01261-7

@chandrasekaran2020 Chandrasekaran, K., Arunraj, N. S., & Akbarsha, M. A. (2020). Temperature-aware dynamic server consolidation for energy-efficient data centers. Cluster Computing, 23, 129-143. doi: 10.1007/s10586-019-02959-5

@khalilpourazari2018 Khalilpourazari, S., Adelpour, A., & Masoodi, R. (2018). Renewable energy source based data center using effective workload scheduling approach. Sustainable Computing: Informatics and Systems, 20, 41-51. doi: 10.1016/j.suscom.2018.06.007

@mukherjee2015 Mukherjee, S., Patra, J. C., & Sahoo, S. K. (2015). Automatic power saving mechanism of cloud data center through virtualization. International Journal of Computer Science and Information Security, 13(9), 64-68.

@peng2018 Peng, X., Zhang, J., Gao, F., & Li, Y. (2018). Energy-efficient resource allocation for data centers with heterogeneous servers. Sustainable Computing: Informatics and Systems, 17, 62-74. doi: 10.1016/j.suscom.2017.12.007

@rosen2019 Rosen, S., Sood, A., & Hsu, D. (2019). A study of data compression methods for in-memory databases. Proceedings of the VLDB Endowment, 12(12), 2036-2049. doi: 10.14778/3352063.3352109

@sidiqui2020 Siddiqui, M. A., Khan, I. A., & Madani, S. A. (2020). Virtualization-based green data center: a systematic review. Journal of Ambient Intelligence and Humanized Computing, 11, 101-127. doi: 10.1007/s12652-018-0874-1


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