Machine Learning in Energy and Thermal-aware Resource Management of Cloud Data Centers: A Taxonomy and Future Directions

• Autorzy: Ilager Shashikant , Buyya Rajkumar

Identyfikatory

DOI

10.15439/2024F0004

• Konferencja: Federated Conference on Computer Science and Information Systems (19 ; 08-11.09.2024 ; Belgrade, Serbia)
• Języki publikacji: EN

Abstrakty

EN

Cloud data centres (CDCs) are the backbone infrastructures of modern digital society, but they also consume huge amounts of energy and generate heat. To manage CDC resources efficiently, we must consider the complex interactions between diverse workloads and data centre components. However, most existing resource management systems rely on simple and static rules that fail to capture these complex interactions. Therefore, we require new data-driven Machine learning-based resource management approaches that can efficiently capture the interdependencies between parameters and guide resource management systems. This review describes the in-depth analysis of the existing resource management approaches in CDCs for energy and thermal efficiency. It mainly focuses on learning-based resource management systems in data centres and also identifies the need for integrated computing and cooling systems management. A taxonomy on energy and thermal efficient resource management in data centres is proposed. Furthermore, based on this taxonomy, existing resource management approaches from server level, data centre level, and cooling system level are discussed. Finally, key future research directions for sustainable Cloud computing services are proposed.

Słowa kluczowe

EN

cloud computing; energy efficiency; workload management; sustainable computing; machine learning

PL

przetwarzanie w chmurze; wydajność energetyczna; zarządzanie obciążeniem; przetwarzanie; uczenie maszynowe

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2024
• Tom: Vol. 39
• Strony: 21--34
• Opis fizyczny: Bibliogr. 113 poz., il., wykr.

Twórcy

autor

Ilager Shashikant

• Cloud Computing and Distributed Systems (CLOUDS) Lab, School of Computing and Information Systems, University of Melbourne, Australia
• Institute of Information Systems Engineering, TU Wien, Austria

autor

Buyya Rajkumar

• Cloud Computing and Distributed Systems (CLOUDS) Lab, School of Computing and Information Systems, University of Melbourne, Australia

Bibliografia

• [1] R. Buyya, C. S. Yeo, S. Venugopal, J. Broberg, and I. Brandic, “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.
• [2] A. Fox, R. Griffith, A. Joseph, R. Katz, A. Konwinski, G. Lee, D. Patterson, A. Rabkin, I. Stoica, et al., “Above the clouds: A berkeley view of cloud computing,” University of California, Berkeley, Rep. UCB/EECS, vol. 28, no. 13, p. 2009, 2009.
• [3] S. Brin and L. Page, “The anatomy of a large-scale hypertextual web search engine,” Computer networks and ISDN systems, vol. 30, no. 1-7, pp. 107–117, 1998.
• [4] Amazon, “ Amazon Web Services.”
• [5] Gartner, “Gartner forecasts worldwide public cloud revenue to grow 17 percent in 2020.” https://www.gartner.com/en/newsroom/press-releases/2019-11-13-gartner-forecasts-worldwide-public-cloud-revenue-to-grow-17-percent-in-2020, 2019. [Online; accessed 10-Jan-2021].
• [6] Gartner, “Gartner forecasts worldwide public cloud end-user spending to grow 18 percent in 2021.” https://www.gartner.com/en/newsroom/press-releases/2020-11-17-gartner-forecasts-worldwide-public-cloud-end-user-spending-to-grow-18-percent-in-2021, 2020. [Online; accessed 10-Jan-2021].
• [7] R. Buyya, S. Ilager, and P. Arroba, “Energy-efficiency and sustainability in new generation cloud computing: A vision and directions for integrated management of data centre resources and workloads,” Software: Practice and Experience, vol. 54, no. 1, pp. 24–38, 2024.
• [8] S. Maybury., “How much Energy does your Data Centre Use?..” https://www.metronode.com.au/energy usage/, 2017. [Online; accessed 05-Jan-2021].
• [9] A. Shehabi, S. Smith, D. Sartor, R. Brown, M. Herrlin, J. Koomey, E. Masanet, N. Horner, I. Azevedo, and W. Lintner, “United states data center energy usage report,” 2016.
• [10] J. Koomey, “Growth in data center electricity use 2005 to 2010,” A report by Analytical Press, completed at the request of The New York Times, vol. 9, 2011.
• [11] A. S. Andrae and T. Edler, “On global electricity usage of communication technology: trends to 2030,” Challenges, vol. 6, no. 1, pp. 117–157, 2015.
• [12] P. Johnson and T. Marker, “Data centre energy efficiency product profile,” Pitt & Sherry, report to equipment energy efficiency committee (E3) of The Australian Government Department of the Environment, Water, Heritage and the Arts (DEWHA), 2009.
• [13] H. Viswanathan, E. K. Lee, and D. Pompili, “Self-organizing sensing infrastructure for autonomic management of green datacenters,” IEEE Network, vol. 25, no. 4, pp. 34–40, 2011.
• [14] D. Minoli, K. Sohraby, and B. Occhiogrosso, “Iot considerations, requirements, and architectures for smart buildings-energy optimization and next-generation building management systems,” IEEE Internet of Things Journal, vol. 4, no. 1, pp. 269–283, 2017.
• [15] Q. Liu, Y. Ma, M. Alhussein, Y. Zhang, and L. Peng, “Green data center with iot sensing and cloud-assisted smart temperature control system,” Computer Networks, vol. 101, pp. 104–112, 2016.
• [16] S. Saha and A. Majumdar, “Data centre temperature monitoring with esp8266 based wireless sensor network and cloud based dashboard with real time alert system,” in 2017 Devices for Integrated Circuit (DevIC), pp. 307–310, IEEE, 2017.
• [17] R. Schwartz, J. Dodge, N. A. Smith, and O. Etzioni, “Green ai,” arXiv preprint arXiv:1907.10597, 2019.
• [18] D. Amodei and D. Hernandez, “Ai and compute,” 2018. https://blog.openai.com/ai-and-compute.
• [19] D. Jeff, “ML for system, system for ML, keynote talk in Workshop on ML for Systems, NIPS,” 2018.
• [20] R. Bianchini, M. Fontoura, E. Cortez, A. Bonde, A. Muzio, A.-M. Constantin, T. Moscibroda, G. Magalhaes, G. Bablani, and M. Russinovich, “Toward ml-centric cloud platforms,” Communications of the ACM, vol. 63, no. 2, pp. 50–59, 2020.
• [21] J. Gao, “Machine learning applications for data center optimization,” Google White Paper, 2014.
• [22] W. Torell, K. Brown, and V. Avelar, “The unexpected impact of raising data center temperatures,” Write paper 221, Revision, 2015.
• [23] V. Venkatachalam and M. Franz, “Power reduction techniques for microprocessor systems,” ACM Computing Surveys (CSUR), vol. 37, no. 3, pp. 195–237, 2005.
• [24] E.-Y. Chung, L. Benini, and G. De Micheli, “Dynamic power management using adaptive learning tree,” in Proceedings of the 1999 IEEE/ACM International Conference on Computer-Aided Design. Digest of Technical Papers (Cat. No. 99CH37051), pp. 274–279, IEEE, 1999.
• [25] Y. Lu, D. Wu, B. He, X. Tang, J. Xu, and M. Guo, “Rank-aware dynamic migrations and adaptive demotions for dram power management,” IEEE Transactions on Computers, vol. 65, no. 1, pp. 187–202, 2015.
• [26] R. A. Bridges, N. Imam, and T. M. Mintz, “Understanding GPU power: A survey of profiling, modeling, and simulation methods,” ACM Computing Surveys, vol. 49, no. 3, 2016.
• [27] G. Von Laszewski, L. Wang, A. J. Younge, and X. He, “Power-aware scheduling of virtual machines in dvfs-enabled clusters,” in Proceedings of the 2009 IEEE International Conference on Cluster Computing and Workshops, pp. 1–10, IEEE, 2009.
• [28] P. Arroba, J. M. Moya, J. L. Ayala, and R. Buyya, “Dynamic voltage and frequency scaling-aware dynamic consolidation of virtual machines for energy efficient cloud data centers,” Concurrency and Computation: Practice and Experience, vol. 29, no. 10, p. e4067, 2017.
• [29] M. Safari and R. Khorsand, “Energy-aware scheduling algorithm for time-constrained workflow tasks in dvfs-enabled cloud environment,” Simulation Modelling Practice and Theory, vol. 87, pp. 311–326, 2018.
• [30] S. Wang, Z. Qian, J. Yuan, and I. You, “A dvfs based energy-efficient tasks scheduling in a data center,” IEEE Access, vol. 5, pp. 13090–13102, 2017.
• [31] J.-G. Park, N. Dutt, and S.-S. Lim, “Ml-gov: A machine learning enhanced integrated cpu-gpu dvfs governor for mobile gaming,” in Proceedings of the 15th IEEE/ACM Symposium on Embedded Systems for Real-Time Multimedia, pp. 12–21, 2017.
• [32] S. Ilager, R. Wankar, R. Kune, and R. Buyya, “Gpu paas computation model in aneka cloud computing environments,” Smart Data: State-of-the-Art Perspectives in Computing and Applications, p. 19, 2019.
• [33] X. Xu, W. Dou, X. Zhang, and J. Chen, “Enreal: An energy-aware resource allocation method for scientific workflow executions in cloud environment,” IEEE Transactions on Cloud Computing, vol. 4, no. 2, pp. 166–179, 2015.
• [34] H. Li, J. Li, W. Yao, S. Nazarian, X. Lin, and Y. Wang, “Fast and energy-aware resource provisioning and task scheduling for cloud systems,” in Proceedings of the 18th International Symposium on Quality Electronic Design (ISQED), pp. 174–179, IEEE, 2017.
• [35] A. Beloglazov, J. Abawajy, and R. Buyya, “Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing,” Future Generation Computer Systems, vol. 28, no. 5, pp. 755–768, 2012.
• [36] M. Dabbagh, B. Hamdaoui, M. Guizani, and A. Rayes, “Energy-efficient resource allocation and provisioning framework for cloud data centers,” IEEE Transactions on Network and Service Management, vol. 12, no. 3, pp. 377–391, 2015.
• [37] H. Chen, X. Zhu, H. Guo, J. Zhu, X. Qin, and J. Wu, “Towards energy-efficient scheduling for real-time tasks under uncertain cloud computing environment,” Journal of Systems and Software, vol. 99, pp. 20–35, 2015.
• [38] Q. Huang, S. Su, J. Li, P. Xu, K. Shuang, and X. Huang, “Enhanced energy-efficient scheduling for parallel applications in cloud,” in Proceedings of the 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (ccgrid 2012), pp. 781–786, IEEE, 2012.
• [39] Y. Ding, X. Qin, L. Liu, and T. Wang, “Energy efficient scheduling of virtual machines in cloud with deadline constraint,” Future Generation Computer Systems, vol. 50, pp. 62–74, 2015.
• [40] R. N. Calheiros and R. Buyya, “Energy-efficient scheduling of urgent bag-of-tasks applications in clouds through dvfs,” in Proceedings of the 6th IEEE international conference on cloud computing technology and science, pp. 342–349, IEEE, 2014.
• [41] C. Ghribi, M. Hadji, and D. Zeghlache, “Energy efficient vm scheduling for cloud data centers: Exact allocation and migration algorithms,” in Proceedings of the 13th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing, pp. 671–678, IEEE, 2013.
• [42] J. L. Berral, Í. Goiri, R. Nou, F. Julià, J. Guitart, R. Gavaldà, and J. Torres, “Towards energy-aware scheduling in data centers using machine learning,” in Proceedings of the 1st International Conference on energy-Efficient Computing and Networking, pp. 215–224, 2010.
• [43] M. Cheng, J. Li, and S. Nazarian, “Drl-cloud: Deep reinforcement learning-based resource provisioning and task scheduling for cloud service providers,” in Proceedings of the 23rd Asia and South Pacific Design Automation Conference (ASP-DAC), pp. 129–134, IEEE, 2018.
• [44] A. Beloglazov, R. Buyya, Y. C. Lee, and A. Zomaya, “A taxonomy and survey of energy-efficient data centers and cloud computing systems,” in Advances in Computers, vol. 82, pp. 47–111, Elsevier, 2011.
• [45] S. F. Piraghaj, A. V. Dastjerdi, R. N. Calheiros, and R. Buyya, “A framework and algorithm for energy efficient container consolidation in cloud data centers,” in Proceedings of the 2015 IEEE International Conference on Data Science and Data Intensive Systems, pp. 368–375, IEEE, 2015.
• [46] M. H. Ferdaus, M. Murshed, R. N. Calheiros, and R. Buyya, “Virtual machine consolidation in cloud data centers using aco metaheuristic,” in Proceedings of the European conference on parallel processing, pp. 306–317, Springer, 2014.
• [47] N. T. Hieu, M. Di Francesco, and A. Ylä-Jääski, “Virtual machine consolidation with multiple usage prediction for energy-efficient cloud data centers,” IEEE Transactions on Services Computing, vol. 13, no. 1, pp. 186–199, 2017.
• [48] S.-Y. Hsieh, C.-S. Liu, R. Buyya, and A. Y. Zomaya, “Utilization-prediction-aware virtual machine consolidation approach for energy-efficient cloud data centers,” Journal of Parallel and Distributed Computing, vol. 139, pp. 99–109, 2020.
• [49] F. Farahnakian, P. Liljeberg, and J. Plosila, “Energy-efficient virtual machines consolidation in cloud data centers using reinforcement learning,” in 2014 22nd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing, pp. 500–507, IEEE, 2014.
• [50] D. Basu, X. Wang, Y. Hong, H. Chen, and S. Bressan, “Learn-as-you-go with megh: Efficient live migration of virtual machines,” IEEE Transactions on Parallel and Distributed Systems, vol. 30, no. 8, pp. 1786–1801, 2019.
• [51] A. A. Bhattacharya, D. Culler, A. Kansal, S. Govindan, and S. Sankar, “The need for speed and stability in data center power capping,” Sustainable Computing: Informatics and Systems, vol. 3, no. 3, pp. 183–193, 2013.
• [52] P. Petoumenos, L. Mukhanov, Z. Wang, H. Leather, and D. S. Nikolopoulos, “Power capping: What works, what does not,” in 2015 IEEE 21st International Conference on Parallel and Distributed Systems (ICPADS), pp. 525–534, IEEE, 2015.
• [53] R. Azimi, M. Badiei, X. Zhan, N. Li, and S. Reda, “Fast decentralized power capping for server clusters.,” in HPCA, pp. 181–192, 2017.
• [54] Q. Wu, Q. Deng, L. Ganesh, C.-H. Hsu, Y. Jin, S. Kumar, B. Li, J. Meza, and Y. J. Song, “Dynamo: Facebook’s data center-wide power management system,” ACM SIGARCH Computer Architecture News, vol. 44, no. 3, pp. 469–480, 2016.
• [55] C. Lefurgy, X. Wang, and M. Ware, “Power capping: a prelude to power shifting,” Cluster Computing, vol. 11, no. 2, pp. 183–195, 2008.
• [56] A. Gandhi, M. Harchol-Balter, R. Das, and C. Lefurgy, “Optimal power allocation in server farms,” ACM SIGMETRICS Performance Evaluation Review, vol. 37, no. 1, pp. 157–168, 2009.
• [57] H. Chen, C. Hankendi, M. C. Caramanis, and A. K. Coskun, “Dynamic server power capping for enabling data center participation in power markets,” in Proceedings of the 2013 IEEE/ACM International Conference on Computer-Aided Design (ICCAD), pp. 122–129, IEEE, 2013.
• [58] A. G. Kumbhare, R. Azimi, I. Manousakis, A. Bonde, F. Frujeri, N. Mahalingam, P. A. Misra, S. A. Javadi, B. Schroeder, M. Fontoura, and R. Bianchini, “Prediction-Based power oversubscription in cloud platforms,” in Proceedings of the 2021 USENIX Annual Technical Conference (USENIX ATC 21), (Berkeley, CA, USA), pp. 473–487, USENIX Association, July 2021.
• [59] H. Zhang and H. Hoffmann, “Maximizing performance under a power cap: A comparison of hardware, software, and hybrid techniques,” ACM SIGPLAN Notices, vol. 51, no. 4, pp. 545–559, 2016.
• [60] M. Xu and R. Buyya, “Managing renewable energy and carbon footprint in multi-cloud computing environments,” Journal of Parallel and Distributed Computing, vol. 135, pp. 191–202, 2020.
• [61] A. Khosravi, L. L. Andrew, and R. Buyya, “Dynamic vm placement method for minimizing energy and carbon cost in geographically distributed cloud data centers,” IEEE Transactions on Sustainable Computing, vol. 2, no. 2, pp. 183–196, 2017.
• [62] U. Mandal, M. F. Habib, S. Zhang, B. Mukherjee, and M. Tornatore, “Greening the cloud using renewable-energy-aware service migration,” IEEE network, vol. 27, no. 6, pp. 36–43, 2013.
• [63] Í. Goiri, K. Le, T. D. Nguyen, J. Guitart, J. Torres, and R. Bianchini, “Greenhadoop: leveraging green energy in data-processing frameworks,” in Proceedings of the 7th ACM european conference on Computer Systems, pp. 57–70, 2012.
• [64] Y. Zhang, Y. Wang, and X. Wang, “Greenware: Greening cloud-scale data centers to maximize the use of renewable energy,” in Proceedings of the ACM/IFIP/USENIX International Conference on Distributed Systems Platforms and Open Distributed Processing, pp. 143–164, Springer, 2011.
• [65] J.-P. Lai, Y.-M. Chang, C.-H. Chen, and P.-F. Pai, “A survey of machine learning models in renewable energy predictions,” Applied Sciences, vol. 10, no. 17, p. 5975, 2020.
• [66] J. Gao, H. Wang, and H. Shen, “Smartly handling renewable energy instability in supporting a cloud datacenter,” in Proceedings of the 2020 IEEE International Parallel and Distributed Processing Symposium (IPDPS), pp. 769–778, IEEE, 2020.
• [67] L. Lin and A. A. Chien, “Adapting datacenter capacity for greener datacenters and grid,” in Proceedings of the 14th ACM International Conference on Future Energy Systems, pp. 200–213, 2023.
• [68] A. Radovanovi´c, R. Koningstein, I. Schneider, B. Chen, A. Duarte, B. Roy, D. Xiao, M. Haridasan, P. Hung, N. Care, S. Talukdar, E. Mullen, K. Smith, M. Cottman, and W. Cirne, “Carbon-aware computing for datacenters,” IEEE Transactions on Power Systems, vol. 38, no. 2, pp. 1270–1280, 2023.
• [69] P. Wiesner, I. Behnke, D. Scheinert, K. Gontarska, and L. Thamsen, “Let’s wait awhile: how temporal workload shifting can reduce carbon emissions in the cloud,” in Proceedings of the 22nd International Middleware Conference, Middleware ’21, (New York, NY, USA), p. 260–272, Association for Computing Machinery, 2021.
• [70] S. Pagani, P. S. Manoj, A. Jantsch, and J. Henkel, “Machine learning for power, energy, and thermal management on multicore processors: A survey,” IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 39, no. 1, pp. 101–116, 2018.
• [71] D. Shin, S. W. Chung, E.-Y. Chung, and N. Chang, “Energy-optimal dynamic thermal management: Computation and cooling power co-optimization,” IEEE Transactions on Industrial Informatics, vol. 6, no. 3, pp. 340–351, 2010.
• [72] R. Ayoub, K. Indukuri, and T. S. Rosing, “Temperature aware dynamic workload scheduling in multisocket cpu servers,” IEEE Transactions on Computer-aided Design of Integrated Circuits and Systems, vol. 30, no. 9, pp. 1359–1372, 2011.
• [73] H. F. Sheikh, I. Ahmad, Z. Wang, and S. Ranka, “An overview and classification of thermal-aware scheduling techniques for multi-core processing systems,” Sustainable Computing: Informatics and Systems, vol. 2, no. 3, pp. 151–169, 2012.
• [74] Z. Wang, C. Bash, N. Tolia, M. Marwah, X. Zhu, and P. Ranganathan, “Optimal fan speed control for thermal management of servers,” in Proceedings of the International Electronic Packaging Technical Conference and Exhibition, vol. 43604, pp. 709–719, 2009.
• [75] A. Iranfar, F. Terraneo, G. Csordas, M. Zapater, W. Fornaciari, and D. Atienza, “Dynamic thermal management with proactive fan speed control through reinforcement learning,” in 2020 Design, Automation & Test in Europe Conference & Exhibition (DATE), pp. 418–423, IEEE, 2020.
• [76] W. Zhang, Y. Wen, Y. W. Wong, K. C. Toh, and C.-H. Chen, “Towards joint optimization over ict and cooling systems in data centre: A survey,” IEEE Communications Surveys & Tutorials, vol. 18, no. 3, pp. 1596–1616, 2016.
• [77] A. H. Khalaj and S. K. Halgamuge, “A review on efficient thermal management of air-and liquid-cooled data centers: From chip to the cooling system,” Applied energy, vol. 205, pp. 1165–1188, 2017.
• [78] C. Nadjahi, H. Louahlia, and S. Lemasson, “A review of thermal management and innovative cooling strategies for data center,” Sustainable Computing: Informatics and Systems, vol. 19, pp. 14–28, 2018.
• [79] M. Tian, Energy Optimization by Fan Speed Control for Data Centers. PhD thesis, The George Washington University, 2019.
• [80] ASHRAE, “American society of heating, refrigerating and air-conditioning engineers,” 2018. URL. http://tc0909.ashraetcs.org/.
• [81] R. Zhou, Z. Wang, C. E. Bash, A. McReynolds, C. Hoover, R. Shih, N. Kumari, and R. K. Sharma, “A holistic and optimal approach for data center cooling management,” in Proceedings of the 2011 American Control Conference, pp. 1346–1351, IEEE, 2011.
• [82] Y. Mhedheb, F. Jrad, J. Tao, J. Zhao, J. Kołodziej, and A. Streit, “Load and thermal-aware vm scheduling on the cloud,” in Proceedings of the International Conference on Algorithms and Architectures for Parallel Processing, pp. 101–114, Springer, 2013.
• [83] H. Sun, P. Stolf, and J.-M. Pierson, “Spatio-temporal thermal-aware scheduling for homogeneous high-performance computing datacenters,” Future Generation Computer Systems, vol. 71, pp. 157–170, 2017.
• [84] M. Polverini, A. Cianfrani, S. Ren, and A. V. Vasilakos, “Thermal-aware scheduling of batch jobs in geographically distributed data centers,” IEEE Transactions on cloud computing, vol. 2, no. 1, pp. 71–84, 2013.
• [85] E. K. Lee, H. Viswanathan, and D. Pompili, “Vmap: Proactive thermal-aware virtual machine allocation in hpc cloud datacenters,” in 2012 19th International Conference on High Performance Computing, pp. 1–10, IEEE, 2012.
• [86] S. Ilager, K. Ramamohanarao, and R. Buyya, “Etas: Energy and thermal-aware dynamic virtual machine consolidation in cloud data center with proactive hotspot mitigation,” Concurrency and Computation: Practice and Experience, vol. 0, no. 0, p. e5221, 2019.
• [87] J. V. Wang, C.-T. Cheng, and C. K. Tse, “A thermal-aware vm consolidation mechanism with outage avoidance,” Software: Practice and Experience, vol. 49, no. 5, pp. 906–920, 2019.
• [88] H. Shamalizadeh, L. Almeida, S. Wan, P. Amaral, S. Fu, and S. Prabh, “Optimized thermal-aware workload distribution considering allocation constraints in data centers,” in 2013 IEEE international conference on green computing and communications and ieee internet of things and IEEE cyber, physical and social computing, pp. 208–214, IEEE, 2013.
• [89] P. Xiao, Z. Ni, D. Liu, and Z. Hu, “A power and thermal-aware virtual machine management framework based on machine learning,” Cluster Computing, vol. 24, pp. 2231–2248, 2021.
• [90] S. Akbar, R. Li, M. Waqas, and A. Jan, “Server temperature prediction using deep neural networks to assist thermal-aware scheduling,” Sustainable Computing: Informatics and Systems, vol. 36, p. 100809, 2022.
• [91] S. S. Gill, S. Tuli, A. N. Toosi, F. Cuadrado, P. Garraghan, R. Bahsoon, H. Lutfiyya, R. Sakellariou, O. Rana, S. Dustdar, et al., “Thermosim: Deep learning based framework for modeling and simulation of thermal-aware resource management for cloud computing environments,” Journal of Systems and Software, vol. 166, p. 110596, 2020.
• [92] S. Ilager, K. Ramamohanarao, and R. Buyya, “Thermal prediction for efficient energy management of clouds using machine learning,” IEEE Transactions on Parallel and Distributed Systems, vol. 32, no. 5, pp. 1044–1056, 2020.
• [93] J. Choi, Y. Kim, A. Sivasubramaniam, J. Srebric, Q. Wang, and J. Lee, “A cfd-based tool for studying temperature in rack-mounted servers,” IEEE Transaction on Computers, vol. 57, no. 8, pp. 1129–1142, 2008.
• [94] R. Romadhon, M. Ali, A. M. Mahdzir, and Y. A. Abakr, “Optimization of cooling systems in data centre by computational fluid dynamics model and simulation,” in 2009 Innovative Technologies in Intelligent Systems and Industrial Applications, pp. 322–327, IEEE, 2009.
• [95] A. Almoli, A. Thompson, N. Kapur, J. Summers, H. Thompson, and G. Hannah, “Computational fluid dynamic investigation of liquid rack cooling in data centres,” Applied energy, vol. 89, no. 1, pp. 150–155, 2012.
• [96] Q. Tang, S. K. S. Gupta, and G. Varsamopoulos, “Energy-efficient thermal-aware task scheduling for homogeneous high-performance computing data centers: A cyber-physical approach,” IEEE Transactions on Parallel and Distributed Systems, vol. 19, no. 11, pp. 1458–1472, 2008.
• [97] L. Wang, G. von Laszewski, F. Huang, J. Dayal, T. Frulani, and G. Fox, “Task scheduling with ann-based temperature prediction in a data center: a simulation-based study,” Engineering with Computers, vol. 27, no. 4, pp. 381–391, 2011.
• [98] Y. Tarutani, K. Hashimoto, G. Hasegawa, Y. Nakamura, T. Tamura, K. Matsuda, and M. Matsuoka, “Temperature distribution prediction in data centers for decreasing power consumption by machine learning,” in Proceedings of the 7th IEEE International Conference on Cloud Computing Technology and Science (CloudCom), pp. 635–642, IEEE, 2015.
• [99] R. Lloyd and M. Rebow, “Data driven prediction model (ddpm) for server inlet temperature prediction in raised-floor data centers,” in Proceedings of the 17th IEEE Intersociety Conference on Thermal and Thermomechanical Phenomena in Electronic Systems (ITherm), pp. 716–725, IEEE, 2018.
• [100] B. Fakhim, M. Behnia, S. Armfield, and N. Srinarayana, “Cooling solutions in an operational data centre: A case study,” Applied Thermal Engineering, vol. 31, no. 14-15, pp. 2279–2291, 2011.
• [101] E. K. Lee, H. Viswanathan, and D. Pompili, “Proactive thermal-aware resource management in virtualized hpc cloud datacenters,” IEEE Transactions on Cloud Computing, vol. 5, no. 2, pp. 234–248, 2015.
• [102] B. Porumb, P. Ungureşan, L. F. Tutunaru, A. Şerban, and M. Bălan, “A review of indirect evaporative cooling operating conditions and performances,” Energy Procedia, vol. 85, pp. 452–460, 2016.
• [103] H. Zhang, S. Shao, H. Xu, H. Zou, and C. Tian, “Free cooling of data centers: A review,” Renewable and Sustainable Energy Reviews, vol. 35, pp. 171–182, 2014.
• [104] T. Gao, S. Shao, Y. Cui, B. Espiritu, C. Ingalz, H. Tang, and A. Heydari, “A study of direct liquid cooling for high-density chips and accelerators,” in Proceedings of the 16th IEEE Intersociety Conference on Thermal and Thermomechanical Phenomena in Electronic Systems (ITherm), pp. 565–573, IEEE, 2017.
• [105] A. Capozzoli and G. Primiceri, “Cooling systems in data centers: state of art and emerging technologies,” Energy Procedia, vol. 83, pp. 484–493, 2015.
• [106] B. Cutler, S. Fowers, J. Kramer, and E. Peterson, “Dunking the data center,” IEEE Spectrum, vol. 54, no. 3, pp. 26–31, 2017.
• [107] W. Zhang, Y. Wen, Y. W. Wong, K. C. Toh, and C.-H. Chen, “Towards joint optimization over ict and cooling systems in data centre: A survey,” IEEE Communications Surveys & Tutorials, vol. 18, no. 3, pp. 1596–1616, 2016.
• [108] X. Li, P. Garraghan, X. Jiang, Z. Wu, and J. Xu, “Holistic Virtual Machine Scheduling in Cloud Datacenters towards Minimizing Total Energy,” IEEE Transactions on Parallel and Distributed Systems, vol. 29, no. 6, pp. 1–1, 2017.
• [109] J. Wan, X. Gui, R. Zhang, and L. Fu, “Joint cooling and server control in data centers: A cross-layer framework for holistic energy minimization,” IEEE Systems Journal, vol. 12, no. 3, pp. 2461–2472, 2017.
• [110] S. Li, H. Le, N. Pham, J. Heo, and T. Abdelzaher, “Joint optimization of computing and cooling energy: Analytic model and a machine room case study,” in Proceedings of the 2012 IEEE 32nd International Conference on Distributed Computing Systems, pp. 396–405, IEEE, 2012.
• [111] F. Ahmad and T. Vijaykumar, “Joint optimization of idle and cooling power in data centers while maintaining response time,” ACM Sigplan Notices, vol. 45, no. 3, pp. 243–256, 2010.
• [112] Y. Ran, H. Hu, X. Zhou, and Y. Wen, “Deepee: Joint optimization of job scheduling and cooling control for data center energy efficiency using deep reinforcement learning,” in Proceedings of the 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 645–655, IEEE, 2019.
• [113] A. Mirhoseini, H. Pham, Q. V. Le, B. Steiner, R. Larsen, Y. Zhou, N. Kumar, M. Norouzi, S. Bengio, and J. Dean, “Device placement optimization with reinforcement learning,” in Proceedings of the 34th International Conference on Machine Learning, pp. 2430–2439, JMLR. org, 2017.

Uwagi

1. Main Track: Invited Contributions

2. Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).