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Towards energy-aware scheduling in data centers using machine learning
Open accessAbstract
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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“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]
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“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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Cited by
Sorted by citations
Items 101–200 of 261
Cocaña-Fernández, A., Ranilla, J., & Sánchez, L. (2014) · The Journal of Supercomputing · doi:10.1007/s11227-014-1320-9 · Cited by 15
“This technique was first introduced in [32] for Load-Balancing clusters, and was also used in [4,9, 14,19,28,31] and in VMware vSphere2 and Citrix XenServer hypervisors.”
MethodZahedi, S. M., Fan, S., Faw, M., Cole, E., & Lee, B. C. (2017) · ACM Transactions on Computer Systems · doi:10.1145/3014428 · Cited by 15
“In servers, managers could pursue performance while minimizing operating costs, which are incurred from energy and cooling [Beloglazov et al. 2012; Lin et al. 2013; Berral et al. 2010; Goiri et al. 2015].”
BackgroundOptimization of resource-aware parallel and distributed computing: a review
Czarnul, P., Antal, M., Baniata, H., Griebler, D., Kertész, A., Keßler, C. W., Kouloumpris, A., Kovačić, S., Márkus, A., Michael, M. K., et al. (2025) · The Journal of Supercomputing · doi:10.1007/s11227-025-07295-7 · Cited by 18
Multi-stage resource-aware scheduling for data centers with heterogeneous servers
Tran, T. T., Padmanabhan, M., Zhang, P. Y., Li, H., Down, D. G., & Beck, J. C. (2017) · Journal of Scheduling · doi:10.1007/s10951-017-0537-x · Cited by 14
“For example, some works consider cost saving through decreased energy consumption from lowering thermal levels (Tang et al. 2007; Wang et al. 2009), powering down machines (Berral et al. 2010; Gandhi et al. 2012), or geographical load balancing (Le et al. 2011; Liu et al. 2011).”
BackgroundSequence-to-sequence models for workload interference prediction on batch processing datacenters
Buchaca, D., Marcual, J., Berral, J. L., & Carrera, D. (2020) · Future Generation Computer Systems · doi:10.1016/j.future.2020.03.058 · Cited by 17
“Figures 3, 4 and 5 show three different pairs of co-located applications with different properties.”
BackgroundSelf-citationNawrocki, P., Śnieżyński, B., Kołodziej, J., & Szynkiewicz, P. (2020) · IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid) · doi:10.1109/ccgrid49817.2020.00-19 · Cited by 14
“Our solution is based on supervised learning, like in [5], in which it is executed offline.”
BackgroundMethodIncreasing Energy Efficiency in Data Centers Using Energy Management
Dumitru, I., Făgărăşan, I., Iliescu, S. S., Hadjsaïd, Y., & Ploix, S. (2011) · IEEE/ACM International Conference on Green Computing and Communications (GreenCom) · doi:10.1109/greencom.2011.53 · Cited by 14
“Intelligently turning off spare servers that are not being used is an obvious way to reduce both power and cooling costs while maintaining good performance levels [17].”
BackgroundRojek, K. (2018) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.4644 · Cited by 14
Energy efficiency in cloud computing: way towards green computing
Sasikala, P. (2013) · International Journal of Cloud Computing · doi:10.1504/ijcc.2013.058095 · Cited by 14
Toward Energy‐Aware Scheduling Using Machine Learning
Berral, J. L., Goiri, Í., Nou, R., Julià, F., Fitó, J. O., Guitart, J., Gavaldà, R., & Torres, J. (2012) · doi:10.1002/9781118342015.ch8 · Cited by 14
Self-citationOn the Utility of DVFS for Power-Aware Job Placement in Clusters
Pierson, J., & Casanova, H. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-23400-2_24 · Cited by 13
“Consequently, in recent years many authors have studied the power-aware job placement problem, seeking theoretical as well as practical solutions [18, 9, 17, 16, 2].”
BackgroundMethodYang, R., Moreno, I. S., Xu, J., & Wo, T. (2013) · IEEE International Conference on Cloud Computing Technology and Science · doi:10.1109/cloudcom.2013.22 · Cited by 13
“By using virtualization, the first generation of energy-efficient Cloud computing approaches have introduced mechanisms to dynamically resize the pool of servers based on actual demand [3, 4].”
MethodRack aware scheduling in HPC data centers: an energy conservation strategy
Patil, V. A., & Chaudhary, V. (2012) · Cluster Computing · doi:10.1007/s10586-012-0224-9 · Cited by 13
“[24] use a machine learning based approach for power modeling where they predict the power requirements for a VM and make VM placement and migration decisions based on this prediction.”
MethodSingh, V., & Kaushik, V. D. (2023) · Advances in digital crime, forensics, and cyber terrorism book series · doi:10.4018/978-1-6684-8422-7.ch003 · Cited by 12
An Energy-Efficient VM Placement in Cloud Datacenter
Teng, F., Deng, D., Lei, Y., & Magoulès, F. (2014) · IEEE International Conference on High Performance Computing and Communications; IEEE International Conference on Smart City; IEEE International Conference on Data Science and Systems (HPCC/SmartCity/DSS) · doi:10.1109/hpcc.2014.33 · Cited by 12
“…usually thousands in size and that using an exact algorithm to solve the resource allocation problem is difficult, simulated annealing algorithm [10], hybrid genetic algorithm [11], frog leaping algorithm [12] and machine learning algorithm [13] are employed to solve the allocation problem of VMs.”
MethodEvaluating energy savings in WoL-enabled networks of PCs
Ricciardi, S., Santos‐Boada, G., Careglio, D., Palmieri, F., & Fiore, U. (2013) · IEEE International Symposium on Industrial Electronics (ISIE) · doi:10.1109/isie.2013.6739357 · Cited by 11
“These solutions differentiate for the scenarios and the used approaches, among which plug-in based connectors [7], virtualized and non-virtualized environments [14], machine learning techniques [15], bin-packing heuristic for workload consolidation [16][17], etc.”
MethodCaglar, F., Shekhar, S., & Gokhale, A. S. (2014) · IEEE International Symposium on Real-Time Distributed Computing (ISORC) · doi:10.1109/isorc.2014.35 · Cited by 11
“[19] propose a framework that provides intelligent dynamic consolidation of VMs in which deadlinesensitive applications are executing.”
BackgroundTowards energy awareness in Hadoop
Krish, K. R., Iqbal, M., Rafique, M. M., & Butt, A. R. (2014) · Network-aware Data Management · doi:10.5555/2688394.2688397 · Cited by 11
Self-adaptation for Individual Self-aware Computing Systems
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“[4] employ reinforcement learning to developmodels of previous system behaviors.”
Energy-demand estimation of embedded devices using deep artificial neural networks
Hönig, T., Herzog, B., & Schröder‐Preikschat, W. (2019) · ACM Symposium on Applied Computing · doi:10.1145/3297280.3297338 · Cited by 11
“8% compared to energy measurements.”
Straight line programs for energy consumption modelling
Rueda, R., Cuéllar, M. P., Pegalajar, M. C., & Delgado‐Rodríguez, M. (2019) · Applied Soft Computing · doi:10.1016/j.asoc.2019.04.001 · Cited by 11
Automated Selection of Energy-efficient Operating System Configurations
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“, locking mechanisms [15]) were adapted to factor in this new paradigm to implement energy-aware operating systems [6, 33].”
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“(2001) for LoadBalancing clusters, and it was also used in Das et al. (2008), Elnozahy et al.”
MethodEnergy Usage and Carbon Emission Optimization Mechanism for Federated Data Centers
Quan, D. M., Somov, A., & Dupont, C. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33645-4_12 · Cited by 10
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“Other works propose energy management in multiprocessor environments with machine learning techniques such as [13], but there is a lack of simulators that combine performance, energy, and resource management with multicriteria or multiobjective optimization (MOO).”
BackgroundAn Energy-aware Virtual Machine Scheduling Approach for Cloud Data Centers
Li, J., Deng, Y., Zhong, Z., Wu, Z., Pang, S., Cui, L., & Min, G. (2025) · IEEE Transactions on Sustainable Computing · doi:10.1109/tsusc.2025.3549001 · Cited by 9
“Theubiquitously usedmethodsforpredictionarecomputationalfluiddynamics (CFD)models[16],machinelearningtechniques[17],[18], andanalyticalmodels[10],[14].”
Low Power Web: Legacy Design and the Path to Sustainable Net Futures
Willis, M., Hanna, J., Encinas, E., & Auger, J. (2020) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3334480.3381829 · Cited by 9
“In addition to this, cooling systems consume as much as 30-40% of a data center's electricity use [21], an issue that data industries are actively addressing, if not out of concern for the environment, then for bottomline cost savings [3; 11; 17].”
BackgroundComputational intelligence for cloud management current trends and opportunities
Tantar, A., Nguyen, A. Q., Bouvry, P., Dorronsoro, B., & Talbi, E. (2013) · IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec.2013.6557713 · Cited by 9
“first-fit, ordered bestfit and a power-aware greedy λ-Round Robin algorithm [37], with results showing that ordered best-fit stands as a good candidate in terms of results vs execution time.”
BackgroundAnalyzing Resource Trade-offs in Hardware Overprovisioned Supercomputers
Sakamoto, R., Patki, T., Cao, T., Kondo, M., Inoue, K., Ueda, M., Ellsworth, D. A., Rountree, B., & Schulz, M. (2018) · IEEE International Parallel and Distributed Processing Symposium (IPDPS) · doi:10.1109/ipdps.2018.00062 · Cited by 9
“[4] have proposed a machine learning based energy-aware job scheduling framework to reduce power consumption with node on-off control.”
BackgroundEco-Efficient Resource Management in HPC Clusters through Computer Intelligence Techniques
Cocaña-Fernández, A., Guiote, E. S. J., Sánchez, L., & Ranilla, J. (2019) · Energies · doi:10.3390/en12112129 · Cited by 8
Energy-Aware Process Scheduling in Linux
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A software tool to efficiently manage the energy consumption of HPC clusters
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Research on Reinforcement Learning-Based Dynamic Power Management for Edge Data Center
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Study on Machine Learning based Energy Efficiency in Developed Countries
Verma, A., & Kumar, Y. (2020) · International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) · doi:10.1109/i-smac49090.2020.9243421 · Cited by 8
“Supervised Machine Learning methods were used by Berral et al. [4, 5] to predict the resource consumption, response time, and power consumption.”
MethodUnderstanding Tradeoffs between Power Usage and Performance in a Virtualized Environment
Smith, J. W., & Sommerville, I. (2013) · IEEE ... International Conference on Cloud Computing · doi:10.1109/cloud.2013.138 · Cited by 8
“[10] extends these approaches by adapting machine learning techniques to deal with situations where information is incomplete or imperfect and still attempt to maximize the performance gains close to those that could be achieved with perfect knowledge of the system.”
BackgroundNawrocki, P., Śnieżyński, B., Kołodziej, J., & Szynkiewicz, P. (2020) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.6070 · Cited by 8
“After applying the formula (4) in line 8, a set of services S′′ with the lowest expenses taking into account tolerance τ is selected, S′′ = {s ∈ S ∶ e(x, s) ≤ e(x, s∗)(1 + τ)}, and the service with the highest security is selected from this set:”
MethodTowards Energy Awareness in Hadoop
Krish, K. R., Iqbal, M., Rafique, M. M., & Butt, A. R. (2014) · Network-aware Data Management · doi:10.1109/ndm.2014.6 · Cited by 8
“ranging from shutting down unused components to low energy circuit designs [25], [27], [4], [16], [30].”
BackgroundMachine Learning for Predictive Analytics of Compute Cluster JobsPreprint
Andresen, D., Hsu, W. W. Y., Yang, H., & Okanlawon, A. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.01116 · Cited by 8
Sustainability Integration of Artificial Intelligence into the Software Development Life Cycle
Trinh, E., Funke, M., Lago, P., & Bogner, J. (2024) · IEEE International Conference on Software Architecture Companion (ICSA-C) · doi:10.1109/icsa-c63560.2024.00044 · Cited by 9
“Other approaches dynamically optimize deployed device clusters, relying on the concept of equitable resource management, so no node is disproportionately utilized [54, 52, 49, 53].”
MethodImproving the Eco-Efficiency of High Performance Computing Clusters Using EECluster
Cocaña-Fernández, A., Sánchez, L., & Ranilla, J. (2016) · Energies · doi:10.3390/en9030197 · Cited by 7
“This technique has been applied to Load-Balancing clusters in [27–32] and in VMware vSphere [33] and Citrix XenServer [34] hypervisors.”
MethodWang, J., Zhou, B., Liu, W., & Hu, S. (2020) · Scientia Sinica Informationis · doi:10.1360/n112018-00293 · Cited by 7
Support vector regression model for BigData systemsPreprint
Rizzi, A. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1612.01458 · Cited by 7
“Related to the field of “Green” IT, [18] adopts linear regression and M5P decision tree algorithm to predict power consumption, CPU loads, and SLA timings.”
MethodLightpath scheduling and routing for green data centres
Wu, J., Zhang, J. Y., & Savoie, M. J. (2011) · Telecommunication Systems · doi:10.1007/s11235-011-9644-8 · Cited by 6
Data-Guided Approach for Learning and Improving User Experience in Computer Networks
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Energy-aware VM Scheduling in IaaS Clouds using Pliant Logic
Benyi, A., Dombi, J. D., & Kertész, A. (2014) · International Conference on Cloud Computing and Services Science · doi:10.5220/0004842905190526 · Cited by 6
Strategies for Increased Energy Awareness in Cloud Federations
Kecskeméti, G., Kertész, A., Marosi, A. C., & Németh, Z. (2014) · doi:10.1002/9781118711897.ch19 · Cited by 6
Resource management on Cloud systems with Machine Learning
Zhen-yu, F. (2010) · LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) · Cited by 6
Trade-off between Performance and Energy Management in Autonomic and Green Data Centers
Diouani, S., & Medromi, H. (2019) · International Conference on Networking, Information Systems & Security (NISS) · doi:10.1145/3320326.3320332 · Cited by 5
“In this way, SLAs are not just used to give guarantees to end user, they are likewise used by cloud suppliers to efficiently manage Cloud infrastructures, considering competing needs including energy efficiency and accomplishment of SLA while delivering adequate flexibility and adaptability [42] [43].”
BackgroundQian, H., Li, F., Ravindran, R., & Medhi, D. (2014) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2014.2378515 · Cited by 5
“Data center energy costs are approaching overall hardware costs [2] and even worse, continue to increase at a rate that is faster than any other [3].”
BackgroundEnergy efficiency of knowledge-free scheduling in Peer-to-Peer Desktop Grids
Barrondo, A., Tchernykh, A. N., Schaeffer, S. E., & Pecero, J. E. (2012) · International Conference on High Performance Computing & Simulation (HPCS) · doi:10.1109/hpcsim.2012.6266898 · Cited by 5
Data center resource management with temporal dynamic workload
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“Certain preliminary studies have combined learning and resource allocation in data center networks [11, 5].”
BackgroundToward a Workload Allocation Optimizer for Power Saving in Data Centers
Hsu, Y.-F., Kuwahara, H., Matsuda, K., & Matsuoka, M. (2019) · IEEE International Conference on Cloud Engineering · doi:10.1109/ic2e.2019.00019 · Cited by 5
“For example, [16] proposed a framework that provides”
BackgroundThe Computational Sprinting Game
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Energy-Efficient Computing Using Agent-Based Multi-objective Dynamic Optimization
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Nayak, P. K., Singh, R. S., Kushwaha, S., Bevara, P. K., Kumar, V., & Medara, R. (2023) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.7954 · Cited by 4
Exploring the Efficiency of Renewable Energy-based Modular Data Centers at Scale
Sun, J., Gong, Z., Agarwal, A., Noghabi, S. A., Chandra, R., Snir, M., & Huang, J. (2024) · ACM Symposium on Cloud Computing (SoCC) · doi:10.1145/3698038.3698544 · Cited by 4
“Unlike these prior studies, SkyBox develops new algorithms for VM placement and migrations across geo-distributed modular data centers.”
Adaptive management of energy consumption using adaptive runtime models
Bergen, A., Taherimakhsousi, N., & Müller, H. A. (2015) · Software Engineering for Adaptive and Self-Managing Systems · doi:10.5555/2821357.2821380 · Cited by 4
Power-aware server selection in nano data center
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“Considering the broad spectrum of power efficiency in data centers, we divide the related work in four broad categories and highlight the novelty of our work.”
BackgroundLeveraging large sensor streams for robust cloud control
Singh, A., Stephan, E., Elsethagen, T., Macduff, M., Raju, B., Schram, M., van Dam, K. K., Kerbyson, D. J., & Altıntaş, İ. (2016) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2016.7840839 · Cited by 4
“[15] uses piecewise linear regression to predict power consumption and performance.”
CONVERGENCE OF HPC AND AI: TWO DIRECTIONS OF CONNECTION
Ismayilova, N. T., & Ismayilov, E. A. (2018) · Azerbaijan Journal of High Performance Computing · doi:10.32010/26166127.2018.1.2.179.184 · Cited by 4
“On the other hand, there are numerous efforts for reducing hardware requirements by application of different optimization problem algorithms which ensure satisfactory results [6].”
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The Computational Sprinting Game
Fan, S., Zahedi, S. M., & Lee, B. C. (2016) · ACM SIGOPS Operating Systems Review · doi:10.1145/2954680.2872383 · Cited by 4
Energy Aware Load Balancing in Cloudlet Federation
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Towards a global energy management within a data center
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Towards a global energy management within a datacenter
Hadjsaïd, Y., Dumitru, I., Bergeon, S., Ploix, S., & Giap, Q.-H. (2011) · HAL · Cited by 4
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“Berral et al. [246] uti - lized temporal patterns in workload behavior to optimize energy usage, showcasing a reduction in power consump - tion through predictive modeling.”
Fault-Resilient Manufacturing Scheduling with Deep Learning and Constraint Solvers
Lee, H. (2025) · Applied Sciences · doi:10.3390/app15041771 · Cited by 5
Coordinating VMs' Memory Demand Heterogeneity and Memory DVFS for Energy-Efficient VMs Consolidation
Takouna, I., & 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.2014.85 · Cited by 3
“Berral et al [13] have studied the problem of dynamic consolidation of VMs running HPC applications with deadlines that were set in the SLAs.”
MethodTakouna, I., & Meinel, C. (2013) · IEEE/ACM International Conference on Utility and Cloud Computing (UCC) · doi:10.1109/ucc.2013.69 · Cited by 3
“Berral et al [8] have studied the problem of dynamic consolidation of VMs running HPC applications with deadlines that were set in the SLAs.”
MethodDynamic load balancing for a mesh‐based scientific application
Zhai, K., Banerjee, T., Zwick, D., Hackl, J., Koneru, R., & Ranka, S. (2020) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.5626 · Cited by 3
Power Aware Energy Efficient Virtual Machine Migration (PAEEVMM) in Cloud Computing
Kaur, T., & Kumar, A. (2022) · Lecture notes in electrical engineering · doi:10.1007/978-981-16-9885-9_46 · Cited by 3
Energy Optimal Activation of Processors for the Execution of a Single Task with Unknown Size
Anselmi, J., & Gaujal, B. (2022) · Proceedings - International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems · doi:10.1109/mascots56607.2022.00017 · Cited by 3
GOAL: Supporting General and Dynamic Adaptation in Computing Systems
Pervaiz, A., Yang, Y. H., Duracz, A., Bartha, F. A., Sai, R., Imes, C., Cartwright, R., Palem, K. V., Lu, S., & Hoffmann, H. (2022) · ACM SIGPLAN International Symposium on New Ideas, New Paradigms, and Reflections on Programming and Software (Onward!) · doi:10.1145/3563835.3567655 · Cited by 3
“Many existing works suggest control theory [7, 27, 28, 40, 62, 75, 77, 81, 88, 94, 95], machine learning [9, 22, 38, 50, 91], and combinations of the two [41, 43, 57, 65, 85] as the basis for building principled AdaptLogs that perform the orientation/analysis and decision/planning phase of the…”
BackgroundEnhancement of Cloud Computing Environment Using Machine Learning Algorithms MLCE
Seno, M. E., Dhannoon, B. N., & Mohammad, O. K. J. (2023) · Iraqi Journal of Computer Communication Control and System Engineering · doi:10.33103/uot.ijccce.23.4.1 · Cited by 3
Energy-Constrained Workflow Scheduling in Cloud Using E-DSOS Algorithm
Sharma, M., Verma, A., & Sangaiah, A. K. (2018) · Elsevier eBooks · doi:10.1016/b978-0-12-813314-9.00008-6 · Cited by 3
A Swarm-inspired Technique for Self-Organizing and Consolidating Data Centre Servers
Anghel, I. M., Pop, C. B., Cioara, T., Salomie, I., & Vartic, I. (2013) · Scalable Computing Practice and Experience · doi:10.12694/scpe.v14i2.841 · Cited by 3
Herlich, M., & Karl, H. (2012) · ACM International Conference on Future Energy Systems (e-Energy) · doi:10.1145/2208828.2208842 · Cited by 3
Power Aware Resource Allocation in Virtualized Environments through VM Behavior Identification
Jiang, C., Zhang, J., Wan, J., Xu, X., Yin, Y., Yu, R., & Lv, C. (2010) · 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/greencom-cpscom.2010.86 · Cited by 3
“However, running such a virtualized data center at high efficiency requires sophisticated tuning techniques and is a non-trivial task[5, 6 ,7,8,9].”
BackgroundDelayed (de-)activation in servers with a sleep mode
Herlich, M., Bredenbals, N., & Karl, H. (2016) · Sustainable Computing Informatics and Systems · doi:10.1016/j.suscom.2016.04.002 · Cited by 3
Using Machine Learning in Trace-driven Energy-Aware Simulations of High-Throughput Computing Systems
McGough, A. S., Moubayed, N. A., & Forshaw, M. (2017) · ACM/SPEC International Conference on Performance Engineering (ICPE) · doi:10.1145/3053600.3053612 · Cited by 3
“[8] use machine learning to predict energy consumption of virtual machines, using these predictions to inform resource provisioning and scheduling decisions including migration and consolidation.”
MethodEnergy-Efficiency in Cloud Computing Environments
Moreno, I. S., & Xu, J. (2012) · IGI Global eBooks · doi:10.4018/978-1-4666-1879-4.ch002 · Cited by 3
Adaptive Management of Energy Consumption Using Adaptive Runtime Models
Bergen, A., Taherimakhsousi, N., & Müller, H. A. (2015) · International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS) · doi:10.1109/seams.2015.20 · Cited by 3
“Berral for the same purpose used machine learning for a backfilling scheduling approach [7].”
MethodAnalysis of Dynamically Switching Energy-Aware Scheduling Policies for Varying Workloads
Kaushik, P., Kothawale, A., DelValle, R., Jain, A., & Govindaraju, M. (2018) · IEEE ... International Conference on Cloud Computing · doi:10.1109/cloud.2018.00024 · Cited by 3
“[22] proposed a framework that provides an intelligent consolidation methodology.”
BackgroundEnergy and Thermal-aware Resource Management of Cloud Data Centres: A Taxonomy and Future DirectionsPreprint
Ilager, S., & Buyya, R. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.02342 · Cited by 3
“[17], adopt many ML-based regression techniques to predict CPU load, power, SLAs and then use these in scheduling for better decisions.”
MethodA Reliability Score for Video-Analytics Edge-IoT Devices in Urban Environments
Garcia, M., Torra, J. O., Aguilera-Luzón, D., Liu, P., Diván, M. J., & Berral, J. L. (2025) · International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT) · doi:10.1109/dcoss-iot65416.2025.00100 · Cited by 2
“Latest methods towards placement go from classic Machine Learning approaches [14] to modern Deep Reinforcement Learning [15]–[17], and time-series modelling for modelling and pattern recognition for resources usage [18].”
Self-citationCloud Power Meter: Bridging the Gap in Power Measurement for Heterogeneous Cloud
Nasrin, A., Chen, D., & Zong, Z. (2025) · IEEE Transactions on Sustainable Computing · doi:10.1109/tsusc.2025.3607207 · Cited by 2
“PM4 was widely adopted across data-centers for energy-aware scheduling [45], [46], [47].”
Energy-Aware HPC Scheduling with LLM-Based Power Prediction
Menear, K., Wilkinson, A., Dykes, T., Haus, U., & Duplyakin, D. (2025) · International Conference for High Performance Computing, Networking, Storage and Analysis (SC) · doi:10.1145/3731599.3767563 · Cited by 2
“This stochastic nature of an HPC workload can be approximated with numerical models trained on historical data, and many machine learning [5], deep learning [36], and meta-heuristic [40] methods, among others, have been introduced with this objective.”
Modeling Energy Consumption of Industrial Processes with Seq2Seq Machine Learning
Howind, S., & Sauter, T. (2023) · IEEE International Symposium on Industrial Electronics (ISIE) · doi:10.1109/isie51358.2023.10228118 · Cited by 2
“Power profiling is done based on historical data, and while the workload estimation of the CPU often involves machine learning techniques, the actual power consumption profile in relation to the CPU workload can be modeled with a simple graph based on historical data [9][10].”
MethodMultiperiod robust optimization for proactive resource provisioning in virtualized data centers
Takouna, I., Sachs, K., & Meinel, C. (2014) · The Journal of Supercomputing · doi:10.1007/s11227-014-1246-2 · Cited by 2
“[35] have studied the problem of dynamic consolidation of VMs running HPC applications with deadlines that were set in the SLAs.”
Energy Aware Resource Management for Clusters of Web Servers
Kiertscher, S., & Schnor, B. (2013) · 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/greencom-ithings-cpscom.2013.47 · Cited by 2
“There already exist Cherub adaptors for HPC management systems like Torque/Maui [13], PBS [8], Load Sharing Facility [16] and Load Leveler [15]2.”
Energy-Minimal Scheduling of Divisible Loads
Agrawal, P., & Rao, S. (2015) · ACM International Conference on Future Energy Systems (e-Energy) · doi:10.1145/2768510.2768528 · Cited by 2
“scheduling; energy minimization; distributed systems; divisible loads”
EECluster: An Energy-Efficient Tool for managing HPC Clusters
Sánchez, L., Ranilla, J., & Cocaña-Fernández, A. (2015) · e-rph (University of Granada) · Cited by 2
Survey Paper on Cloud Computing
Kumar, V. (2013) · Cited by 2
Yan, Y., Zhang, B., & Guo, J. H. (2016) · Microprocessors and Microsystems · doi:10.1016/j.micpro.2016.06.012 · Cited by 2
Improving Energy Consumption in Iterative Problems Using Machine Learning
Cabrera, A., Almeida, F., Blanco, V., & Castellanos–Nieves, D. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-43222-5_12 · Cited by 2
Sustainability-aware Resource Provisioning in Data Centers
Wang, J., Palanisamy, B., & Xu, J. (2020) · IEEE International Conference on Collaboration and Internet Computing (CIC) · doi:10.1109/cic50333.2020.00018 · Cited by 2
“[40] proposed a machine learning based scheduling framework to deal with uncertain information while maximizing the performance in data centers.”
BackgroundAn Intelligent Scheduling System Architecture for Manufacturing Systems Based on I4.0 Requirements
Varela, L., Shah, V., Lucamba, A. J., Araújo, A., & Machado, J. (2021) · Lecture notes in mechanical engineering · doi:10.1007/978-3-030-77719-7_27 · Cited by 2
Energy-Aware Load Balancing in a Cloudlet Federation
Inam, M., & Nayyer, M. Z. (2021) · International Conference on Energy, Power and Environment · doi:10.3390/engproc2021012027 · Cited by 2
Metadata
- 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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