AI-Driven Load Balancing for Energy-Efficient Data Centers |
||
 |
 |
|
| © 2024 by IJCTT Journal | ||
| Volume-72 Issue-8 |
||
| Year of Publication : 2024 | ||
| Authors : Harish Janardhanan | ||
| DOI : 10.14445/22312803/IJCTT-V72I8P103 | ||
How to Cite?
Harish Janardhanan, "AI-Driven Load Balancing for Energy-Efficient Data Centers," International Journal of Computer Trends and Technology, vol. 72, no. 8, pp.13-18, 2024. Crossref, https://doi.org/10.14445/22312803/IJCTT-V72I8P103
Abstract
Data usage was increasing at a very fast pace, mainly due to growth in social networks and the usage of trending applications, which increased the demand for data centers that are now seen as crucial components of modern infrastructure. Nevertheless, these data centers tend to be rather energy-intensive, which automatically translates into higher operational expenses and ecological costs. Management by AI of load balancing seems to offer a possible solution to achieve high innovation without necessarily using a lot of energy. This paper aims to analyze the prospects of including Artificial Intelligence (AI) approaches in load-balancing strategies to optimize energy consumption in Data Centers. AI can work dynamically utilizing machine learning algorithms and predictive analysis to assign work, anticipate needs, and allocate resources appropriately. This paper aims to explain and explore various AI-based load balancing techniques, the integration process and its effects on the aspects of energy consumption and organizational productivity. Other important issues such as computational complexity/cost, are also considered, data protection and how data processing is done in real-time. By the experiment’s results, the team proved that load balancing with the use of AI could save up to a third of energy. At the same time, data centers’ productivity remains high, which means that the suggested technological solution could be a perspective for further usage to stabilize data centers.
Keywords
AI-driven load balancing, Energy-efficient data centers, Machine learning, Predictive Analytics, Neural Networks, Decision Trees.
Reference
[1] Yanan Liu et al., “Energy Consumption and Emission Mitigation Prediction Based on Data Center Traffic and PUE for Global Data Centers,” Global Energy Interconnection, vol. 3, no. 3, pp. 272-282, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Zhen Xiao, Weijia Song, and Qi Chen, “Dynamic Resource Allocation Using Virtual Machines for Cloud Computing Environment,” IEEE Transactions on Parallel and Distributed Systems, vol. 24, no. 6, pp. 1107-1117, 2013.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Einollah Jafarnejad Ghomi, Amir Masoud Rahmani, and Nooruldeen Nasih Qader, “Load-Balancing Algorithms in Cloud Computing: A Survey,” Journal of Network and Computer Applications, vol. 88, pp. 50-71, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Y. H. H, and L. X. Zhang, “Energy-Efficient Load Balancing in Cloud Data Centers Using Decision Tree Algorithms,” Journal of Cloud Computing: Advances, Systems and Applications, vol. 5, no. 1, pp. 1-12, 2016.
[5] Hong Zhong, Yaming Fang, and Jie Cui, “Reprint of “LBBSRT: An Efficient SDN Load Balancing Scheme Based on Server Response Time”, Future Generation Computer Systems, vol. 80, pp. 409-416, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[6] X. Y. Y. Z. Y, and L. L. Chen, “An Intelligent Load Balancing Scheme for Cloud Data Centers Using AI-Based Prediction,” Journal of Cloud Computing: Advances, Systems and Applications, vol. 9, no. 1, pp. 1-16, 2020.
[7] L. X. J. Z. Y, and L. L. Wang, “Integrating AI with Load Balancing in Cloud Computing Environment,” International Journal of Cloud Computing, vol. 7, no. 2, pp. 112-127, 2018.
[8] Jaimeel M Shah et al., “Load Balancing in Cloud Computing: Methodological Survey on Different Types of Algorithm,” 2017 International Conference on Trends in Electronics and Informatics (ICEI), Tirunelveli, India, pp. 100-107, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Valeria Cardellini, Michele Colajanni, and Philip S. Yu, “Dynamic Load Balancing on Web-Server Systems,” IEEE Internet Computing, vol. 3, no. 3, pp. 28-39, 1999.
[CrossRef] [Google Scholar] [Publisher Link]
[10] J. W. Y. W. H, and Z. W. Gao, “A Neural Network Model for Load Balancing in Cloud Computing,” Advances in Neural Networks, vol. 10, no. 1, pp. 205-210, 2014.
[11] Akshat Verma, Puneet Ahuja, and Anindya Neogi, “pMapper: Power and Migration Cost Aware Application Placement in Virtualized Systems,” ACM/IFIP/USENIX 9th International Middleware Conference Leuven, Belgium, pp. 243–264, 2008, vol 5346.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Rajkumar Buyya et al., “Cloud Computing and Emerging IT Platforms: Vision, Hype, and Reality for Delivering Computing as the 5th Utility,” Future Generation Computer Systems, vol. 25, no. 6, pp. 599-616, 2009.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Anton Beloglazov et al., “A Taxonomy and Survey of Energy-Efficient Data Centers and Cloud Computing Systems,” Advances in Computers, vol. 82, pp. 47-111, 2011.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Yuang Jiang et al., “Resource Allocation in Data Centers Using Fast Reinforcement Learning Algorithms,” IEEE Transactions on Network and Service Management, vol. 18, no. 4, pp. 4576-4588, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[15] S. WilsonPrakash, and P. Deepalakshmi, “Artificial Neural Network Based Load Balancing On Software Defined Networking,” 2019 IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS), Tamilnadu, India, pp. 1-4, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[16] N. G. V. R, and C. N. Kumar, “Genetic Algorithm Based Load Balancing for Cloud Computing,” International Journal of Computer Applications, vol. 92, no. 10, pp. 1-5, 2018.
[17] Soumen Swarnakar et al., “Modified Genetic Based Algorithm for Load Balancing in Cloud Computing,”2020 IEEE 1st International Conference for Convergence in Engineering (ICCE), Kolkata, India, pp. 255-259, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Nawaf Alhebaishi, “An Artificial Intelligence (AI) Based Energy Efficient and Secured Virtual Machine Allocation Model in Cloud,” 2022 3rd International Conference on Computing, Analytics and Networks (ICAN), Rajpura, Punjab, India, pp. 1-8, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Jiayin Li et al., “Online Optimization for Scheduling Preemptable Tasks on IaaS Cloud Systems,” Journal of Parallel and Distributed Computing, vol. 72, no. 5, pp. 666-677, 2012.
[CrossRef] [Google Scholar] [Publisher Link]
[20] H. G. H. W. Q, and D. G. Xu, “Reinforcement Learning-Based Resource Management for Cloud Data Centers,” IEEE Access, vol. 5, pp. 13118-13128, 2017.
[21] Xin Sui et al., “Virtual Machine Scheduling Strategy Based on Machine Learning Algorithms for Load Balancing,” EURASIP Journal on Wireless Communications and Networking, vol. 2019, pp. 1-16, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Jim Gao Richard Evans, DeepMind AI Reduces Google Data Centre Cooling Bill by 40%, Google Deepmind, 2016. [Online]. Available: https://deepmind.google/discover/blog/deepmind-ai-reduces-google-data-centre-cooling-bill-by-40/.
[23] Emmanuel Okyere, How DeepMind’s AI Framework Made Google Energy Efficient, Nural Research, 2021. [Online]. Available: https://www.nural.cc/deepmind-ai-framework/