Towards energy-aware scheduling in data centers using machine learning
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As energy-related costs have become a major economical factor for IT infrastructures and data-centers, companies and the research community are being challenged to find better and more efficient power-aware resource management strategies. There is a growing interest in "Green" IT and there is still a big gap in this area to be covered.
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ML can potentially reduce global emissions by helping to streamline supply chains, improve production quality, predict machine breakdowns, optimize heating and cooling systems, and prioritize the use of clean electricity over fossil fuels.
“In particular, ML can potentially reduce global emissions (Figure 4) by helping to streamline supply chains, improve production quality, predict machine breakdowns, optimize heating and cooling systems, and prioritize the use of clean electricity over fossil fuels [74,227,416,880].”
From Tackling climate change with machine learning (2023) · cites this paper as [74]
Berral et al. proposed a framework that demonstrates how such optimization algorithms might be combined with RL, digitized controls, and feedback systems to enable the autonomous control of industrial processes.
“Berral et al. proposed a framework that demonstrates how such optimization algorithms might be combined with RL, digitized controls, and feedback systems to enable the autonomous control of industrial processes [74].”
From Tackling climate change with machine learning (2023) · cites this paper as [74]
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“Adaptation techniques Proposals differ in their approach to reducing node power consumption, with some researchers opting to migrate VMs and set the node to a sleep state [39, 44, 56, 67], compared to incrementally reduce power consumption by using DVFS.”
BackgroundMethodInfluentialThe Computational Sprinting Game
Fan, S., Zahedi, S. M., & Lee, B. C. (2016) · International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) · doi:10.1145/2872362.2872383 · Cited by 45 · see citing works
“In servers, managers could pursue performance while minimizing operating costs, which are incurred from energy and cooling [13, 14, 21, 32].”
BackgroundYoo, S. J. B. (2023) · Journal of Optical Communications and Networking · doi:10.1364/jocn.484577 · Cited by 45 · see citing works
“For example, machine learning can help, and have been significant efforts toward self-driving data centers based on the machine-learned patterns of the data traffic and attributes [54,57,58].”
BackgroundAn Energy-Aware Algorithm for Virtual Machine Placement in Cloud Computing
Zhao, D., Zhou, J., & Li, K. (2019) · IEEE Access · doi:10.1109/access.2019.2913175 · Cited by 44 · see citing works
“It has many advantages such as server consolidation [9], online migration [10], isolation, high availability, flexible deployment and low management overhead [11].”
BackgroundPatel, T., Wagenhäuser, A., Eibel, C., Hönig, T., Zeiser, T., & Tiwari, D. (2020) · IEEE International Parallel and Distributed Processing Symposium (IPDPS) · doi:10.1109/ipdps47924.2020.00087 · Cited by 43 · see citing works
“This is particularly important given recent rising interest in improving the energy efficiency of jobs based on user guidance [7, 31, 32].”
BackgroundAdaptive energy efficient scheduling in Peer-to-Peer desktop grids
Tchernykh, A. N., Pecero, J. E., Barrondo, A., & Schaeffer, S. E. (2013) · Future Generation Computer Systems · doi:10.1016/j.future.2013.07.011 · Cited by 39 · see citing works
Geographic trough filling for internet datacenters
Xu, D., & Liu, X. (2012) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infcom.2012.6195720 · Cited by 38 · see citing works
Thermal-Aware Hybrid Workload Management in a Green Datacenter towards Renewable Energy Utilization
Li, Y., Wang, X., Luo, P., & Pan, Q. (2019) · Energies · doi:10.3390/en12081494 · Cited by 36 · see citing works
“Some methods that dynamically Energies 2019 , 12 , 1494 2 of 18 migrate or consolidate tasks on some less-utilized servers or turn o ff idle servers have been proven to be energy-e ffi cient strategies [4–6].”
MethodGAME-SCORE: Game-based energy-aware cloud scheduler and simulator for computational clouds
Fernández-Cerero, D., Jakóbik, A., Fernández-Montes, A., & Kołodziej, J. (2018) · Simulation Modelling Practice and Theory · doi:10.1016/j.simpat.2018.09.001 · Cited by 35 · see citing works
Resource Management in Cloud Computing Using Machine Learning: A Survey
Goodarzy, S., Nazari, M., Han, R., Keller, E., & Rozner, E. (2020) · International Conference on Machine Learning and Applications · doi:10.1109/icmla51294.2020.00132 · Cited by 27 · see citing works
“Unlike the previous work, [9]’s goal is to maximize SLA while optimizing energy consumption.”
BackgroundSingh, A. K., Leech, C., Basireddy, K. R., Al‐Hashimi, B. M., & Merrett, G. V. (2017) · Journal of Low Power Electronics · doi:10.1166/jolpe.2017.1492 · Cited by 33 · see citing works
Zu, X., Ni, G., & Hu, R. (2025) · Technology in Society · doi:10.1016/j.techsoc.2025.102984 · Cited by 36 · see citing works
An Energy Manager for High Performance Computer Clusters
Alvarruiz, F., Alfonso, C., Caballer, M., & Hern�ndez, V. (2012) · IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA) · doi:10.1109/ispa.2012.38 · Cited by 33 · see citing works
“For instance, [1] presents a method to reduce power in large-scale distributed systems by alternatively switching nodes on and off according to the load.”
MethodMoreno, I. S., & Xu, J. (2011) · IEEE International Conference on Service-Oriented Computing and Applications (SOCA) · doi:10.1109/soca.2011.6166239 · Cited by 32 · see citing works
“Moreover, due to the need to maintain the expected QoS levels, some others such as [11], [12], and [13] have proposed to enhance these “Dynamic Resource Resizing” (DRR) approaches.”
BackgroundTakouna, I., Dawoud, W., & Meinel, C. (2012) · ACM SIGOPS Operating Systems Review · doi:10.1145/2331576.2331580 · Cited by 30 · see citing works
“Berral et al [35] have studied the problem of dynamic consolidation of VMs running HPC applications with deadlines that were set in the SLAs.”
Neural Network-Based Overallocation for Improved Energy-Efficiency in Real-Time Cloud Environments
Moreno, I. S., & Xu, J. (2012) · IEEE International Symposium on Real-Time Distributed Computing (ISORC) · doi:10.1109/isorc.2012.24 · Cited by 29 · see citing works
“Moreover, due to the need to maintain the expected QoS levels, others authors such as [11] and [12] have proposed to enhance these “Dynamic Resource Resizing” (DRR) approaches.”
BackgroundIntegrated neuron circuit for implementing neuromorphic system with synaptic device
Lee, J.-J., Park, J., Kwon, M.-W., Hwang, S., Kim, H., & Park, B. (2017) · Solid-State Electronics · doi:10.1016/j.sse.2017.10.012 · Cited by 28 · see citing works
Analyzing the impact of electricity price forecasting on energy cost-aware scheduling
Grimes, D., Ifrim, G., O’Sullivan, B., & Simonis, H. (2014) · Sustainable Computing Informatics and Systems · doi:10.1016/j.suscom.2014.08.009 · Cited by 27 · see citing works
“Data centres and manufacturing plants are large energy consumers, in critical need to reduce their electricity costs, which account for a big part of their operational cost (electricity accounts for 30-50% of operating costs) [5, 6, 7].”
BackgroundPreemption-Aware Energy Management in Virtualized Data Centers
Salehi, M. A., Krishna, P. R., Deepak, K. S., & Buyya, R. (2012) · IEEE ... International Conference on Cloud Computing · doi:10.1109/cloud.2012.147 · Cited by 27 · see citing works
“[11] investigated a supervised machine learning approach for workload consolidation without SLA violation within a datacenter.”
BackgroundA stochastic process-based server consolidation approach for dynamic workloads in cloud data centers
Naeen, H. M., Zeinali, E., & Haghighat, A. T. (2018) · The Journal of Supercomputing · doi:10.1007/s11227-018-2431-5 · Cited by 26 · see citing works
“Berral et al. [28] have studied the placement of real-time applications.”
BackgroundMethodMaximizing the Energy Efficiency of Virtualized C-RAN via Optimizing the Number of Virtual Machines
Alhumaima, R. S., Ahmed, R. K., & Al-Raweshidy, H. S. (2018) · IEEE Transactions on Green Communications and Networking · doi:10.1109/tgcn.2018.2859407 · Cited by 26 · see citing works
“Also, it requires repeating the process of optimisation several times to guarantee the results, for example see [18].”
BackgroundAn energy management system for cluster infrastructures
Alfonso, C., Caballer, M., Alvarruiz, F., & Hernández, V. (2013) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2013.05.004 · Cited by 25 · see citing works
Ismail, L., & Abed, E. H. (2019) · IEEE Access · doi:10.1109/access.2019.2956881 · Cited by 25 · see citing works
Taniguchi, Y., Suganuma, K., Deguchi, T., Hasegawa, G., Nakamura, Y., Ukita, N., Aizawa, N., Shibata, K., Matsuda, K., & Matsuoka, M. (2015) · IEEE Transactions on Cloud Computing · doi:10.1109/tcc.2015.2440245 · Cited by 23 · see citing works
“Until now, many researchers have considered energy-efficient technologies focusing on individual data center units such as servers [20], [21], [22], air conditioners [9], network equipments [23] and so on.”
BackgroundRedesigning Data Centers for Renewable Energy
Agarwal, A., Sun, J., Noghabi, S. A., Iyengar, S., Badam, A., Chandra, R., Seshan, S., & Shivkumar Kalyanaraman, A. (2021) · ACM Workshop on Hot Topics in Networks (HotNets) · doi:10.1145/3484266.3487394 · Cited by 23 · see citing works
“For the VM placement, we rely on a number of prior works [10, 13, 20, 32, 38, 57, 62–64, 66, 68].”
Energy-aware load dispatching in geographically located Internet data centers
Zheng, X., & Cai, Y. (2011) · Sustainable Computing Informatics and Systems · doi:10.1016/j.suscom.2011.06.002 · Cited by 23 · see citing works
A Survey on Energy Efficiency in Information Systems
Vitali, M., & Pernici, B. (2014) · International Journal of Cooperative Information Systems · doi:10.1142/s0218843014500014 · Cited by 22 · see citing works
Portable Energy-Aware Cluster-Based Edge Computers
Rausch, T., Avasalcai, C., & Dustdar, S. (2018) · IEEE/ACM Symposium on Edge Computing (SEC) · doi:10.1109/sec.2018.00026 · Cited by 22 · see citing works
“Multiple other solutions have been proposed based on machine learning [32], [27] and metaheuristic algorithms [33].”
MethodDeep Learning-Driven Anomaly Detection for Green IoT Edge Networks
Bushehri, A. S., Amirnia, A., Belkhiri, A., Keivanpour, S., de Magalhães, F. G., & Nicolescu, G. (2023) · IEEE Transactions on Green Communications and Networking · doi:10.1109/tgcn.2023.3335342 · Cited by 22 · see citing works
“Other works [45] anticipate running tasks’ future energy footprint and service level agreement metrics like response time using supervised machine learning.”
MethodFrom Prediction to Action: Improving User Experience With Data-Driven Resource Allocation
Bao, Y., Wu, H., & Liu, X. (2017) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2017.2680918 · Cited by 21 · see citing works
“Reference [21] uses the collected information from system behaviors to predict power consumption levels, CPU loads and SLA (Service Level Agreement) timings to improve scheduling decisions in data centers.”
MethodTakouna, I., Alzaghoul, E. F., & Meinel, C. (2014) · IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) · doi:10.1109/ithings.2014.84 · Cited by 21 · see citing works
A swarm-inspired data center consolidation methodology
Pop, C. B., Anghel, I. M., Cioara, T., Salomie, I., & Vartic, I. (2012) · International Conference on Web Intelligence, Mining and Semantics · doi:10.1145/2254129.2254180 · Cited by 21 · see citing works
Tarutani, Y., Hashimoto, K., Hasegawa, G., Nakamura, Y., Tamura, T., Matsudax, K., & Matsuoka, M. (2016) · IEEE International Conference on Cloud Engineering · doi:10.1109/ic2e.2016.39 · Cited by 21 · see citing works
“…system (A-DCIM) [3] learning techniques are well suited to solving problems in data center environments, where there are a variety of interdependent factors [2], and thus the temperature distribution and the power efficiency of air conditioner were predicted by using machine-learning technique.”
MethodCioara, T., Anghel, I. M., Salomie, I., Copil, G., Moldovan, D., & Kipp, A. (2011) · International Symposium on Parallel and Distributed Computing · doi:10.1109/ispdc.2011.32 · Cited by 20 · see citing works
“In [6] the authors reveal that when consolidation is used, an optimal solution for energy/performance trade-off can be defined.”
BackgroundResource provisioning in scalable cloud using bio-inspired artificial neural network model
Rawat, P. S., Dimri, P., Gupta, P., & Saroha, G. P. (2020) · Applied Soft Computing · doi:10.1016/j.asoc.2020.106876 · Cited by 20 · see citing works
Energy-Efficiency in Cloud Computing Environments
Moreno, I. S., & Xu, J. (2011) · International Journal of Cloud Applications and Computing · doi:10.4018/ijcac.2011010102 · Cited by 20 · see citing works
Renugadevi, T., Geetha, K., Prabaharan, N., & Siano, P. (2020) · Applied Sciences · doi:10.3390/app10082701 · Cited by 20 · see citing works
A Pliant-based Virtual Machine Scheduling Solution to Improve the Energy Efficiency of IaaS Clouds
Kertész, A., Dombi, J. D., & Benyi, A. (2015) · Journal of Grid Computing · doi:10.1007/s10723-015-9336-9 · Cited by 20 · see citing works
“1 + ( 1−ν∗ ν∗ )∏n i=1 ( 1−xi xi 1−ν∗ ν∗ )wi (4)”
Goiri, Í., Fitó, J. O., Julià, F., Nou, R., Berral, J. L., Guitart, J., & Torres, J. (2010) · IEEE/ACM International Conference on Grid Computing (GRID) · doi:10.1109/grid.2010.5697964 · Cited by 19 · see citing works
“It results from the aggregation of several services and the used profile is of a whole week, thus representing different classical increases of load of each weekday and the decreases of load of weekends.”
BackgroundSelf-citationEnhanced Weighted Round Robin (EWRR) with DVFS Technology in Cloud Energy-Aware
Alnowiser, A., aldhahri, E. A., Alahmadi, A. M., & Zhu, M. (2014) · International Conference on Computational Science and Computational Intelligence (CSCI) · doi:10.1109/csci.2014.62 · Cited by 19 · see citing works
“It has been a real challenge to design efficient data center architecture while achieving scalability, reliability and high performance at the same time [4].”
BackgroundNawrocki, P., & Śnieżyński, B. (2020) · Wireless Personal Communications · doi:10.1007/s11277-020-07657-9 · Cited by 20 · see citing works
“Our solution is based on supervised learning, like in [5], in which it is executed offline.”
BackgroundMethodTarutani, Y., Hashimoto, K., Hasegawa, G., Nakamura, Y., Tamura, T., Matsuda, K., & Matsuoka, M. (2015) · IEEE International Conference on Cloud Computing Technology and Science · doi:10.1109/cloudcom.2015.49 · Cited by 19 · see citing works
“This work is motivated by the fact that machine-learning technique is well suited to solving problems in data center environments, where there are a variety of interdependent factors [9, 10].”
BackgroundMachine Learning in Compiler OptimisationPreprint
Wang, Z., & O’boyle, M. F. P. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.03441 · Cited by 19 · see citing works
Load Prediction for Energy-Aware Scheduling for Cloud Computing Platforms
Dambreville, A., Tomasik, J., Cohen, J., & Dufoulon, F. (2017) · IEEE International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs.2017.201 · Cited by 18 · see citing works
“[2] use machine learning techniques to predict both power consumption and client satisfaction.”
BackgroundAn Innovative Energy-Aware Cloud Task Scheduling Framework
Alahmadi, A. M., Che, D., Khaleel, M. I., Zhu, M., & Ghodous, P. (2015) · IEEE ... International Conference on Cloud Computing · doi:10.1109/cloud.2015.72 · Cited by 17 · see citing works
“Berral et al.[3] presented a theoretical approach to save”
BackgroundHussin, M. B., Lee, Y. C., & Zomaya, A. Y. (2011) · Proceedings of the International Conference on Parallel Processing · doi:10.1109/icpp.2011.18 · Cited by 17 · see citing works
“The comparative performance evaluation is performed between our approach (Adaptive-RL) and extended versions of three other learning approaches; Online RL [11], Q+ learning [12], and Prediction-based learning [13].”
BackgroundMethodEnergy-efficient virtual machine placement in data centers with heterogeneous requirements
Dai, X., Wang, J. M., & Bensaou, B. (2014) · IEEE International Conference on Cloud Networking · doi:10.1109/cloudnet.2014.6968986 · Cited by 16 · see citing works
Rack Aware Scheduling in HPC Data Centers: An Energy Conservation Strategy
Patil, V. A., & Chaudhary, V. (2011) · IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum · doi:10.1109/ipdps.2011.227 · Cited by 15 · see citing works
Cloud Servers: Resource Optimization Using Different Energy Saving Techniques
Hijji, M., Ahmad, B., Alam, G., Alwakeel, A. M., Alwakeel, M. M., Alharbi, L. A., Aljarf, A., & Khan, M. U. (2022) · Sensors · doi:10.3390/s22218384 · Cited by 15 · see citing works
“Likewise, [35] researched a similar issue of dynamic consolidation of virtual machinerunning applications using machine learning algorithms to optimize the combined energy consumption.”
BackgroundAutonomic performance-per-watt management (APM) of cloud resources and services
Fargo, F., Tunc, C., Al-Nashif, Y. B., & Hariri, S. (2013) · International Conference on Cloud and Autonomic Computing (ICCAC) · doi:10.1145/2494621.2494624 · Cited by 15 · see citing works
- Authors
- Berral, J. L., Goiri, Í., Nou, R., Julià, F., Guitart, J., Gavaldà, R., & Torres, J.
- Affiliations
- Universitat Politècnica de Catalunya (ES)
- Barcelona Supercomputing Center (ES)
- Venue
- Proceedings of the 1st International Conference on Energy-Efficient Computing and Networking
- Publisher
- ACM
- Published
- 2010-04-13
- Pages
- 215-224
- OpenAlex
- W2149472591
- Cited by
- 262
- References
- 28
- Citations by year
- 2026: 2 · 2025: 9 · 2024: 5 · 2023: 10 · 2022: 11 · 2021: 10 · 2020: 25 · 2019: 16 · 2018: 21 · 2017: 14 · 2016: 23 · 2015: 22 · 2014: 26 · 2013: 24 · 2012: 20
- FWCI
- 15.28
- Type
- conference paper
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- Locations
- Energy-Efficient Computing and Networking (publishedVersion)
- UPCommons institutional repository (Universitat Politècnica de Catalunya) (submittedVersion, cc-by-nc-nd)
- RECERCAT (Consorci de Serveis Universitaris de Catalunya) (submittedVersion, cc-by-nc-nd)
- Funders
- Federación Española de Enfermedades Raras (TIN2007-60625)
- Ministerio de Economía y Competitividad (AP2008-0264TIN2008-06582-C03TIN2008-06582-C03-01)
- Seventh Framework Programme (FP7-ICT-216886)
- Generalitat de Catalunya (2009-SGR-9802009-SGR-1428)
- Topics
- Physical Sciences › Computer Science › Information Systems › Cloud Computing and Resource Management
- Physical Sciences › Computer Science › Hardware and Architecture › Energy Efficiency in Computing
- Physical Sciences › Computer Science › Hardware and Architecture › Parallel Computing and Optimization Techniques
- Keywords
- energy-aware scheduling, data centers, green computing, machine learning, energy cost reduction, workload scheduling
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