Virginia Tech Mathematicians Use Algebraic Geometry to Reduce Data Centre Energy Use
Researchers at Virginia Tech have applied algebraic geometry to improve energy efficiency in data centres. By using advanced polynomials and error-correcting codes, they have developed a new method for data storage and retrieval that reduces the need for excessive data replication and energy-intensive searching. This approach helps address the growing energy demands of data centres, which are expected to increase significantly in the coming years. The new technique allows for localised data recovery, improving performance and reducing the energy required during server failures. The research offers a sustainable solution to the power consumption challenges faced by data storage facilities
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Efforts to improve data centre efficiency have led mathematicians at Virginia Tech to develop a novel method of data storage and retrieval. According to reports, the researchers have utilised algebraic geometry to tackle issues arising from high energy consumption in data centres, which is impacting global climate goals. This breakthrough was detailed in IEEE BITS, where the team presented a fresh approach to managing the growing volume of data generated by individuals and corporations.
Innovative Use of Algebraic Structures
As per a report by Phys.org, tt was explained by Gretchen Matthews, professor of mathematics at Virginia Tech and director of the Southwest Virginia node of the Commonwealth Cyber Initiative, that conventional methods of data replication often result in duplicating vast quantities of information. As reported, Matthews noted that smarter alternatives could significantly reduce such redundancy. Hiram Lopez, assistant professor of mathematics, added that the new method employs algebraic structures to fragment data and distribute it across servers positioned in close proximity. This ensures that, in the event of server failure, the missing data can be recovered through neighbouring servers without extensive energy use.
Mathematics Behind the Solution
The use of special polynomials for data storage was highlighted as a significant advancement. Although polynomials have been linked to data storage since the 1960s, recent developments have made them more practical for applications like localised data recovery. Matthews pointed out in IEEE BITS that these structures offer an efficient and reliable way to manage data, addressing issues related to storage and retrieval energy demands.
Addressing Rising Power Consumption
The method arrives at a critical time, as energy demand across the United States continues to rise, driven by the increasing number of data centres. Matthews emphasised in the publication that sustainable improvements in existing systems could play a vital role in managing energy consumption.
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