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Intelligent Cloud Datacenter Management Through Energy-Aware Scheduling and Dynamic Performance Optimization

Dr. Tolga ENSARİ

School of Computer Engineering, Istanbul University, Istanbul, Turkey

Abstract

Cloud datacenters are fundamental to modern digital infrastructure, supporting applications such as artificial intelligence, big data analytics, online services, and enterprise computing. However, increasing computational demand has resulted in higher energy consumption, operational expenditure, and environmental impact. Traditional datacenter management techniques often prioritize resource availability and performance without adequately considering energy efficiency and dynamic workload variations. This study investigates intelligent cloud datacenter management through energy-aware scheduling and dynamic performance optimization. The proposed approach integrates workload prediction, virtual machine placement, resource allocation, server consolidation, and dynamic power management to improve operational efficiency. An intelligent scheduling framework is conceptualized to distribute computational tasks according to workload characteristics, server utilization, energy consumption, and service-level agreement requirements. Dynamic optimization techniques are employed to adjust processing resources in response to changing workloads while maintaining acceptable response times and system reliability. The research methodology combines analytical modeling, simulation-based experimentation, and comparative performance evaluation. Key performance indicators include total energy consumption, power usage effectiveness, resource utilization, task completion time, response latency, and operational cost. The proposed framework is expected to reduce unnecessary energy consumption, improve resource utilization, and maintain application performance under variable workloads. The study contributes a systematic approach to sustainable cloud infrastructure management by integrating energy-aware decision-making with adaptive performance optimization

Article Information

Journal

International Journal of Emerging Trends in Engineering and Management Research

Volume (Issue)

Vol. 8 No. 6 (2023): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR)

DOI

https://doi.org/10.15662/ijetemr.2023.0806013

Pages

14791-14799

Published

December 19, 2023
Copyright

Open Access

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Dr. Tolga ENSARİ (%2023). Intelligent Cloud Datacenter Management Through Energy-Aware Scheduling and Dynamic Performance Optimization. International Journal of Emerging Trends in Engineering and Management Research , Vol. 8 No. 6 (2023): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR) , pp. 14791-14799. https://doi.org/10.15662/ijetemr.2023.0806013

References

1. Armbrust, M., Fox, A., Griffith, R., Joseph, A. D., Katz, R., Konwinski, A., Lee, G., Patterson, D., Rabkin, A., Stoica, I., & Zaharia, M. (2010). A view of cloud computing. Communications of the ACM, 53(4), 50–58.
2. Skourletopoulos, G., Mavromoustakis, C. X., Mastorakis, G., Batalla, J. M., Dobre, C., Panagiotakis, S., & Pallis, E. (2017). Big data and cloud computing: A survey of the state-of-the-art and research challenges. In Studies in Big Data (Vol. 22, pp. 23–41). Springer.
3. Rengarajan, A. (2022). Developing AI Powered Enterprise Integration Platforms Through Cloud Native Observability and Automated Governance. International Journal of Science, Research and Technology, 5(6), 8970-8977.
4. Bhuiyan, R. H., Aesha, U. H., Khan, S. M., & Jabeer, M. A. (2023). AI-driven digital twin technologies for advanced manufacturing systems. American Journal of Engineering, Mechanics and Architecture, 1(10).
5. Kommineni, R. (2022). Monitoring as code for self-hosted Elasticsearch: Independent, delta-based lifecycle management of alerting watchers and dashboards across multi-cloud environments. International Journal of Science, Research and Technology (IJSRAT), 5(2), 7437–7446.
6. Raja, G. V., & Mali, R. K. (2021). Federated learning frameworks for privacy-preserving artificial intelligence applications. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 4(3), 4946-4950.
7. Basavala, S. R. (2022). Security assessment framework for microservices-based applications using automated penetration testing. International Journal of Emerging Trends in Engineering and Management Research, 7(4), 12179–12190.
8. Kalakoti, M. K. R. (2022). Dependency-aware migration and resilience validation of stateful applications to enterprise cloud. International Research Journal of Innovative Engineering, 6(2), 9997–10005.
9. Jinna, K. R., & Subramani, S. K. (2022). Supply-chain-aware CI/CD for regulated enterprises: Integrating artifact signing, secret injection and promotion gates in Kubernetes-native pipelines. International Journal of Computer Technology and Electronics Communication, 5(6), 16275-16285.
10. Karuturi, Y. (2023). Energy-aware capacity governance for large-scale SAP HANA deployments. International Journal of Research and Applied Innovations, 6(3), 6000–6009.
11. Nunnaguppala, L. S. C., Sayyaparaju, K. K., & Padamati, J. R. (2021). Securing The Cloud: Automating Threat Detection with SIEM, Artificial Intelligence & Machine Learning", International Journal For Advanced Research In. Science & Technology, 11(3), 385.
12. Polamarasetty, V. K. (2023). Modern enterprise HR technology through Workday-based benefits and absence management. International Journal of Engineering & Extended Technologies Research (IJEETR), 5(4), 6908–6913.
13. Soundappan, S. J. (2021). DataOps: Orchestrating Reliable ML Data Pipelines. International Journal of Research and Applied Innovations, 4(4), 5533-5537.
14. Gopinathan, V. R., & Mali, R. K. (2022). Building cognitive technology frameworks through artificial intelligence SAP cloud automation and enterprise intelligence. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 5(4), 7152-7162.
15. Reddy, K. V. (2021). UVM methodology for industry-specific applications using AI in hardware verification. International Journal of Science, Research and Technology, 4(3), 5731–5744.
16. Bhuiyan, R. H., Aesha, U. H., Khan, S. M., & Jabeer, M. A. (2023). AI-driven digital twin technologies for advanced manufacturing systems. American Journal of Engineering, Mechanics and Architecture, 1(10).
17. Padmanabham, S. (2022). Secure enterprise architecture for pharmacy benefit management platforms. International Journal of Engineering & Extended Technologies Research (IJEETR), 4(5), 5381-5386.
18. Vemireddy, S. (2023). Engineering regulatory-aware data processing systems for tax advisory platforms. International Journal of Science, Research and Technology, 6(1), 9243–9249.
19. Kopparthi, G. S. (2023). Trustworthy artificial intelligence for high-stakes decision-making: Explainability, human oversight, and system reliability. International Research Journal of Innovative Engineering, 7(4), 12847–12860.
20. Mudunuri, P. R. (2023). Governance-aware infrastructure-as-code for regulated research environments. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(4), 9017-9027.
21. Venkatasalam, K., Rajendran, P., & Thangavel, M. (2019). Improving the accuracy of feature selection in big data mining using accelerated flower pollination (AFP) algorithm. Journal of medical systems, 43(4), 96.
22. Gadige, C. D. (2023). API-led connectivity in enterprise CRM: A reference architecture for Salesforce and heterogeneous enterprise systems. International Research Journal of Innovative Engineering, 7(2), 12125–12135.
23. Natta, P. K. (2022). Computer-vision-assisted retail inventory automation: Integrating autonomous scan data with worklist completion systems. International Journal of Science, Research and Technology, 5(6), 8957–8969.
24. Bandari, U., & Thilagaraj, S. V. (2023). Defensive and offensive data strategy in manufacturing M&A: A comparative evaluation of data lake, data fabric, and data mesh integration patterns. International Journal of Research and Applied Innovations, 6(1), 8403-8413.
25. Selvarajan, K. (2022). Cloud-native architectures for high-throughput distributed data processing. International Journal of Emerging Trends in Engineering and Management Research (IJETEMR), 7(5), 12538–12548.
26. Madhan, E. S., Kannan, K. S., Rani, P. S., Rani, J. V., & Anguraj, D. K. (2021). A distributed submerged object detection and classification enhancement with deep learning. Distrib. Parallel Databases, 41, 1-17.
27. Gopinathan, V. R., & Mali, R. K. (2022). Building cognitive technology frameworks through artificial intelligence SAP cloud automation and enterprise intelligence. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 5(4), 7152-7162.
28. Jayaraman, S., Rajendran, S., & P, S. P. (2019). Fuzzy c-means clustering and elliptic curve cryptography using privacy preserving in cloud. International Journal of Business Intelligence and Data Mining, 15(3), 273-287.
29. Randhi, K. S. (2022). A scalable integration framework for enterprise application metadata aggregation and synchronization. International Journal of Research Publications in Engineering, Technology and Management, 5(2), 6564–6581.
30. Neela, S. (2023). Resilient middleware ecosystems: Integrating fault tolerance, recovery-oriented design, and observability for real-time enterprise integration. International Journal of Emerging Research in Engineering and Technology, 4(1), 186–189. https://doi.org/10.63282/3050-922X.IJERET-V4I1P120
31. Ramasamy, M. (2022). Closed-loop network automation: Architectural principles from Catalyst Center-scale systems. International Research Journal of Innovative Engineering, 6(3), 10698–10708.
32. Chippagiri, S., & Ravula, P. (2021). Cloud-Native Development: Review of Best Practices and Frameworks for Scalable and Resilient Web Applications. International Journal of New Media Studies: International Peer Reviewed Scholarly Indexed Journal, 8(2), 13-21.
33. Katakam, R. (2023). Integrating SAP finance and banking systems for efficient enterprise financial management. International Research Journal of Innovative Engineering, 7(2), 12136–12147.
34. Sugumar, R. (2022). Explainable Deep Learning Framework for Financial Fraud Detection and Intelligent Risk Analysis. International Journal of Emerging Trends in Engineering and Management Research, 7(5), 12528.