Optimization Strategies for Data Centre Energy Efficiency
Ahmed, Abrar (2024)
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-2024052114127
https://urn.fi/URN:NBN:fi:amk-2024052114127
Tiivistelmä
This thesis investigates Optimization Strategies for Data Centre Energy Efficiency, aiming to address the growing concern of energy consumption in data centers. The research explores the practical implementation of energy efficiency measures and identifies emerging trends poised to reshape the landscape of data center management. Through qualitative interviews with professionals from various industries and regions, the study delves into specific instances where energy efficiency measures have led to improved performance and reliability in data centers.
The findings reveal that the implementation of energy efficiency measures, such as server virtualization, efficient cooling systems, and AI optimization, has resulted in significant reductions in energy consumption and enhancements in data center performance. However, challenges such as initial investment costs, skilled personnel requirements, and integration complexities were commonly cited by participants. Despite these challenges, the study highlights the potential of emerging technologies like AI, machine learning, edge computing, big data analytics, and intelligent monitoring to further enhance energy efficiency in data centers.
Furthermore, the research sheds light on the importance of effective planning and strategic implementation of optimization strategies. Recommendations derived from the qualitative analysis offer practical insights for organizations seeking to optimize energy efficiency in their data centers. By bridging the gap between theory and practice, this thesis contributes valuable knowledge to the field of data center management, offering actionable strategies for mitigating energy consumption while improving performance and reliability.
The findings reveal that the implementation of energy efficiency measures, such as server virtualization, efficient cooling systems, and AI optimization, has resulted in significant reductions in energy consumption and enhancements in data center performance. However, challenges such as initial investment costs, skilled personnel requirements, and integration complexities were commonly cited by participants. Despite these challenges, the study highlights the potential of emerging technologies like AI, machine learning, edge computing, big data analytics, and intelligent monitoring to further enhance energy efficiency in data centers.
Furthermore, the research sheds light on the importance of effective planning and strategic implementation of optimization strategies. Recommendations derived from the qualitative analysis offer practical insights for organizations seeking to optimize energy efficiency in their data centers. By bridging the gap between theory and practice, this thesis contributes valuable knowledge to the field of data center management, offering actionable strategies for mitigating energy consumption while improving performance and reliability.