Advancing Green Cloud Computing: A Machine Learning Framework for Energy efficient Task Scheduling
Hema1
*,
Ilakkiya S, Gangalakshmi S, Karthiban S
- 1Anna University, Chennai, Tagore Engineering College, Chennai, India, IN
Conclusion
This project embarked on the crucial endeavor of
intertwining machine learning capabilities with the principles of green cloud
computing to innovate the selection process of task scheduling algorithms. By harnessing
a Multi-Layer Perceptron (MLP) neural network, we meticulously evaluated
various scheduling algorithms against crucial metrics such as Makespan
Efficiency, Throughput Efficiency, Total Energy Consumption (TEC), and Average
Energy Consumption per Task (AEC).
Our findings illuminate the profound potential of
machine learning models, particularly MLPClassifiers, in accurately forecasting
the efficiency of scheduling algorithms within cloud computing environments.
The models showcased remarkable proficiency, with high accuracy levels in
predicting Makespan Efficient and Throughput Efficient algorithms, underscoring
the viability of machine learning in enhancing operational efficiency within
cloud systems.
A pivotal aspect of our research was the emphasis on
energy efficiency, manifesting through the metrics of TEC and AEC. The models
adeptly identified algorithms that optimize energy consumption, marking a
significant stride toward sustainable cloud computing practices. This emphasis
aligns with the growing imperative for environmental sustainability in
technological advancements.
Moreover, the analytical insights derived from the
training loss curves and PCA underscored the effectiveness of our neural
network architecture and feature selection. These insights not only validated
our methodological choices but also highlighted the intricate relationship
between task characteristics and scheduling efficiency.
Author Contributions
Ilakkiya S1, Gangalakshmi S1, Karthiban S1
Assistant Professor, Tagore Engineering College, Chennai, India
References
[1] H. Djigal et al., "Machine and
Deep Learning for Resource Allocation in Multi-Access Edge Computing:
ASurvey," in IEEE Communications Surveys & Tutorials, vol. 24, no. 4,
pp. 2449-2494, Fourthquarter 2022. DOI: 10.1109/COMST.2022.3189519
[2] A. T. Alharbi, R. Buyya, "Energy
efficient fault tolerance techniques in green cloud computing: A systematic
survey and taxonomy," in Future Generation Computer Systems, vol. 107, pp.
903-922, May 2020. DOI: 10.1016/j.future.2020.01.015
[3] H. Haghighi, S. R. Ghorbani, M. Sharifi,
"Energy-aware intelligent scheduling for deadline-constrained workflows in
sustainable cloud computing," in Sustainable Computing: Informatics and
Systems, vol. 25, pp. 204-215, December 2020. DOI: 10.1016/j.suscom.2020.100374
[4] S. Ullah, A. Gani, S. A. Madani, H. A.
Shah, "Task Scheduling in Cloud Computing based on Meta-heuristics:
Review, Taxonomy, Open Challenges, and Future Trends," in Journal of
Network and Computer Applications, vol. 108, pp. 1-20, June 2018. DOI:
10.1016/j.jnca.2018.01.009
[5] "SG-PBFS: Shortest Gap-Priority
Based Fair Scheduling technique for job scheduling in cloud environment,"
in Future Generation Computer Systems, vol. 150, 2024, pp. 232-242. DOI:
10.1016/j.future.2023.04.001
[6] R. N. Dhumane, S. S. Rathod, "An
Improved Task Allocation Strategy in Cloud using Modified K-means Clustering
Technique," in International Journal of Computer Applications, vol. 179,
no. 20, pp. 35-40, April 2018. DOI: 10.5120/ijca2018916686
[7] P. L. Jayabalan, R. Thangarajan,
"Energy Efficiency & Consumption in Data Centre by Dynamic Resource
Allocation Technique for Green Cloud Computing," in Procedia Computer
Science, vol. 50, pp. 556-561, January 2015. DOI: 10.1016/j.procs.2015.04.050
[8] M. Kaur, A. Singh, "Optimizing Cloud Resource
Allocation and Load Balancing through Eco-Efficient Task Scheduling," in
Proceedings of the 2019 3rd International Conference on Computing Methodologies
and
Communication
(ICCMC), pp. 711-716, April 2019. DOI: 10.1109/ICCMC.2019.8819752
[9] Shetty, C., Sarojadevi, H., and Prabhu, S.,
"Machine learning approach to select optimal task scheduling algorithm in
cloud," Turkish Journal of Computer and Mathematics Education (TURCOMAT),
12(6), pp.25652580, 2021. DOI: 10.17762/turcomat.v12i6.4509
[10] Jing, S.Y., Ali, S., She, K. and Zhong, Y., "State-of-the-art
research study for green cloud computing," The Journal of Supercomputing,
vol. 65, pp.445-468, 2013. DOI: 10.1007/s11227-013-0918-6
[11] Mohan Sharma & R. Garg. (2020). An artificial
neural network-based approach for energy efficient task scheduling in cloud
data centers. Sustainable Computing: Informatics and Systems, 26, 100373. DOI:
10.1016/j.suscom.2020.100373
[12] Kaixuan Kang, Ding Ding, Huamao Xie,
Qian Yin, & Jing Zeng. (2022). Adaptive DRL-Based Task Scheduling for
Energy-Efficient Cloud Computing. IEEE Transactions on Network and Service
Management, 19, 4948-4961. DOI: 10.1109/TNSM.2022.3151846
[13] Ding Ding, Xiaocong Fan, Yihuan Zhao,
Kaixuan Kang, Qian Yin, & Jing Zeng. (2020). Q-learning based dynamic task
scheduling for energy-efficient cloud computing. Future Generation Computer
Systems, 108, 361-371. DOI: 10.1016/j.future.2020.06.039
[14] K. K. Arasan & P. Anandhakumar.
(2023). Energy?efficient task scheduling and resource
management in a cloud environment using optimized hybrid technology. Software:
Practice and Experience, 53, 1572 - 1593. DOI: 10.1002/spe.3066
[15] Hashim Ali, M. S. Qureshi, M. B.
Qureshi, A. Khan, M. Zakarya, & M. Fayaz. (2020). An Energy and Performance
Aware Scheduler for Real-Time Tasks in Cloud Datacentres. IEEE Access, 8,
161288-161303. DOI: 10.1109/ACCESS.2020.3021913
Keywords: Green Cloud Computing, Machine Learning, Task scheduling algorithms, Energy efficiency, Resource utilization
Citation: Hema*,Hema ( 2026), Advancing Green Cloud Computing: A Machine Learning Framework for Energy efficient Task Scheduling. , 14(2): 1 - 12
Received: 29/05/2026; Accepted: 03/06/2026;
Published: 08/06/2026
Edited by:
Mr.ERES JOURNALS