Thermal-Aware Resource Management for Green Cloud and Edge Infrastructures
DOI:
https://doi.org/10.63503/j.ijssic.2025.174Keywords:
Edge Infrastructure, Thermal Operation, DVFS, RC Models, Selective Migration, Cloud-EdgeAbstract
Raising the workload concentrations in both cloud and edge infrastructure has compounded the probability of thermal hotspots, which have caused more rack power expenditure, increased hardware depreciation, and increased operational expenses. To overcome this issue, the current paper will suggest a thermal-conscious resource management framework that coordinates prediction, scheduling, and adaptive control into one orchestration strategy. The methodology makes use of hybrid RC-based thermal models alongside on-line learning in order to obtain calibrated temperature forecasts. This prediction helps to make decisions concerning the location of workloads, dynamic voltage and frequency scaling (DVFS), and selective migration to maintain safe thermal operation without impacting performance. Experimental analysis indicates that the framework can cut the peak temperature by a maximum of 12°C, radically decrease the cooling power by half, and cut the workload latency by three-fifths in connection with typical scheduling. And long-term reliability is better 20% after 10 hours, but the cost activity always reduces by 20%. Collectively, this evidence implies that thermal-aware orchestration can substantially improve energy performance, reliability, and sustainability through providing greener and more resilient cloud-edge ecosystems.
References
[1] Md Aadil Hasan, & Gulshan Shrivastava. (2025). Cloud Computing: Enhancing Security, Driving Innovation, and Shaping the Future. International Journal on Computational Modelling Applications, 2(1), 53–64. https://doi.org/10.63503/j.ijcma.2025.51
[2] Garimella, S. V., Persoons, T., Weibel, J. A., & Gektin, V. (2016). Electronics thermal management in information and communications technologies: Challenges and future directions. IEEE Transactions on Components, Packaging and Manufacturing Technology, 7(8), 1191-1205. doi: 10.1109/TCPMT.2016.2603600.
[3] Hatamipour, M. S., Mahiyar, H., & Taheri, M. (2007). Evaluation of existing cooling systems for reducing cooling power consumption. Energy and Buildings, 39(1), 105-112. https://doi.org/10.1016/j.enbuild.2006.05.007
[4] Polverini, M., Cianfrani, A., Ren, S., & Vasilakos, A. V. (2013). Thermal-aware scheduling of batch jobs in geographically distributed data centers. IEEE Transactions on cloud computing, 2(1), 71-84. doi: 10.1109/TCC.2013.2295823
[5] Bao, L., He, Z., Tan, J., Chen, Y., & Zhao, M. (2023). Thermal-aware task scheduling and resource allocation for UAV-and-Basestation hybrid-enabled MEC networks. IEEE Transactions on Green Communications and Networking, 7(2), 579-593. doi: 10.1109/TGCN.2023.3241954
[6] Anita Mohanty. (2024). Dynamic Predictive Models for Hospital Readmission Risk . International Journal on Computational Modelling Applications, 1(2), 10–19. https://doi.org/10.63503/j.ijcma.2024.26
[7] Taheri, G., Khonsari, A., Entezari-Maleki, R., Baharloo, M., & Sousa, L. (2018). Temperature-aware dynamic voltage and frequency scaling enabled MPSoC modeling using stochastic activity networks. Microprocessors and Microsystems, 60, 15-23. https://doi.org/10.1016/j.micpro.2018.03.011
[8] Sheikh, H. F., Ahmad, I., & Fan, D. (2015). An evolutionary technique for performance-energy-temperature optimized scheduling of parallel tasks on multi-core processors. IEEE Transactions on Parallel and Distributed Systems, 27(3), 668-681. doi: 10.1109/TPDS.2015.2421352
[9] Lee, Y., Chwa, H. S., Shin, K. G., & Wang, S. (2018). Thermal-aware resource management for embedded real-time systems. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 37(11), 2857-2868. doi: 10.1109/TCAD.2018.2857279
[10] Zheng, W., Ma, K., & Wang, X. (2016, May). TECfan: Coordinating thermoelectric cooler, fan, and DVFS for CMP energy optimization. In 2016 IEEE International Parallel and Distributed Processing Symposium (IPDPS) (pp. 423-432). IEEE. doi: 10.1109/IPDPS.2016.19
[11] Arroba, P., Buyya, R., Cárdenas, R., Risco‐Martín, J. L., & Moya, J. M. (2024). Sustainable edge computing: Challenges and future directions. Software: Practice and Experience, 54(11), 2272-2296. https://doi.org/10.1002/spe.3340
[12] Hanumaiah, V., & Vrudhula, S. (2012). Energy-efficient operation of multicore processors by DVFS, task migration, and active cooling. IEEE Transactions on Computers, 63(2), 349-360. doi: 10.1109/TC.2012.213
[13] Baziana, P. A. (2024). Optical data center networking: A comprehensive review on traffic, switching, bandwidth allocation, and challenges. IEEE Access. doi: 10.1109/ACCESS.2024.3513214.
[14] Lee, J. S., Skadron, K., & Chung, S. W. (2009). Predictive temperature-aware DVFS. IEEE Transactions on Computers, 59(1), 127-133. doi: 10.1109/TC.2009.136.
[15] Gupta, M., Bhargava, L., & Indu, S. (2021). Mapping techniques in multicore processors: current and future trends. The Journal of Supercomputing, 77(8), 9308-9363. https://doi.org/10.1007/s11227-021-03650-6
[16] Mahmoud, H., Thabet, M., Khafagy, M. H., & Omara, F. A. (2021). An efficient load balancing technique for task scheduling in heterogeneous cloud environment. Cluster Computing, 24(4), 3405-3419. https://doi.org/10.1007/s10586-021-03334-z
[17] Khan, M. W., Zeeshan, M., Farid, A., & Usman, M. (2020). QoS-aware traffic scheduling framework in cognitive radio based smart grids using multi-objective optimization of latency and throughput. Ad Hoc Networks, 97, 102020. https://doi.org/10.1016/j.adhoc.2019.102020
[18] Lin, J., Lin, W., Huang, H., Lin, W., & Li, K. (2023). Thermal modeling and thermal-aware energy saving methods for cloud data centers: A review. IEEE Transactions on Sustainable Computing, 9(3), 571-590. doi: 10.1109/TSUSC.2023.3346332.
[19] Kheder, M. Q., & Mohammed, A. A. (2024). Real-time traffic monitoring system using IoT-aided robotics and deep learning techniques. Kuwait Journal of Science, 51(1), 100153. https://doi.org/10.1016/j.kjs.2023.10.017
[20] Lozano, M. A., Ramos, J. C., & Serra, L. M. (2010). Cost optimization of the design of CHCP (combined heat, cooling and power) systems under legal constraints. Energy, 35(2), 794-805. https://doi.org/10.1016/j.energy.2009.08.022
[21] MirhoseiniNejad, S., Moazamigoodarzi, H., Badawy, G., & Down, D. G. (2020). Joint data center cooling and workload management: A thermal-aware approach. Future Generation Computer Systems, 104, 174-186. https://doi.org/10.1016/j.future.2019.10.040
[22] Gorbet, R. B., Morris, K. A., & Wang, D. W. (2002). Passivity-based stability and control of hysteresis in smart actuators. IEEE Transactions on control systems technology, 9(1), 5-16. doi: 10.1109/87.896741.
[23] Naganandhini, S., Sundar, C., Sathya, R., & Venkatesan, G. (2023, December). Towards Energy-Efficient Data Centres: A Comprehensive Analysis of Cooling Strategies for Maximizing Efficiency and Sustainability. In 2023 Intelligent Computing and Control for Engineering and Business Systems (ICCEBS) (pp. 1-6). IEEE. doi: 10.1109/ICCEBS58601.2023.10448782.
