{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T19:17:07Z","timestamp":1774725427585,"version":"3.50.1"},"reference-count":47,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T00:00:00Z","timestamp":1771027200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Future Generation Computer Systems"],"published-print":{"date-parts":[[2026,8]]},"DOI":"10.1016\/j.future.2026.108428","type":"journal-article","created":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T00:14:59Z","timestamp":1771028099000},"page":"108428","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":1,"special_numbering":"C","title":["Real-time AI-powered monitoring for energy-efficient scheduling in multi-node heterogeneous systems"],"prefix":"10.1016","volume":"181","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-8682-145X","authenticated-orcid":false,"given":"Taha Abdelazziz","family":"Rahmani","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9694-7586","authenticated-orcid":false,"given":"Ghalem","family":"Belalem","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1530-9524","authenticated-orcid":false,"given":"Sidi Ahmed","family":"Mahmoudi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5107-2918","authenticated-orcid":false,"given":"Omar Rafik","family":"Merad-Boudia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"issue":"14","key":"10.1016\/j.future.2026.108428_bib0001","doi-asserted-by":"crossref","first-page":"15700","DOI":"10.1007\/s11227-023-05266-4","article-title":"A machine learning-based resource-efficient task scheduler for heterogeneous computer systems","volume":"79","author":"Hayat","year":"2023","journal-title":"J. Supercomput."},{"issue":"23","key":"10.1016\/j.future.2026.108428_bib0002","article-title":"Equalizer: energy-efficient machine learning-based heterogeneous cluster load balancer","volume":"36","author":"Rahmani","year":"2024","journal-title":"Concur. Comput. Practice Exp."},{"issue":"1","key":"10.1016\/j.future.2026.108428_bib0003","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1109\/4434.580451","article-title":"WWW Traffic reduction and load balancing through server-based caching","volume":"5","author":"Bestavros","year":"1997","journal-title":"IEEE Concurrency"},{"key":"10.1016\/j.future.2026.108428_bib0004","doi-asserted-by":"crossref","first-page":"49760","DOI":"10.1109\/ACCESS.2021.3065170","article-title":"Migration-based load balance of virtual machine servers in cloud computing by load prediction using genetic-based methods","volume":"9","author":"Hung","year":"2021","journal-title":"IEEE Access"},{"issue":"4","key":"10.1016\/j.future.2026.108428_bib0005","doi-asserted-by":"crossref","first-page":"4883","DOI":"10.1007\/s10586-023-04215-3","article-title":"Machine learning-driven energy-efficient load balancing for real-time heterogeneous systems","volume":"27","author":"Rahmani","year":"2024","journal-title":"Cluster Comput."},{"issue":"5\u20136","key":"10.1016\/j.future.2026.108428_bib0006","first-page":"185","article-title":"Comparison of load balancing strategies on cluster-based web servers","volume":"77","author":"Teo","year":"2001","journal-title":"Simulation"},{"key":"10.1016\/j.future.2026.108428_bib0007","series-title":"2015 3Rd International Conference on Artificial Intelligence, Modelling and Simulation (AIMS)","first-page":"3","article-title":"A machine learning-based approach to estimate the CPU-burst time for processes in the computational grids","author":"Helmy","year":"2015"},{"issue":"1","key":"10.1016\/j.future.2026.108428_bib0008","article-title":"COSCO2: AI-Augmented evolutionary algorithm based workload prediction framework for sustainable cloud data centers","volume":"34","author":"Karthikeyan","year":"2023","journal-title":"Trans. Emerg. Telecommun. Technol."},{"issue":"3","key":"10.1016\/j.future.2026.108428_bib0009","article-title":"Workload prediction in cloud data centers using complex-Valued spatio-Temporal graph convolutional neural network optimized with gazelle optimization algorithm","volume":"36","author":"Karthikeyan","year":"2025","journal-title":"Trans. Emerg. Telecommun. Technol."},{"issue":"1","key":"10.1016\/j.future.2026.108428_bib0010","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13677-023-00453-3","article-title":"Comparative analysis of metaheuristic load balancing algorithms for efficient load balancing in cloud computing","volume":"12","author":"Zhou","year":"2023","journal-title":"J. Cloud Comput."},{"key":"10.1016\/j.future.2026.108428_bib0011","series-title":"Proceedings of the 22Nd International Conference on Parallel Architectures and Compilation Techniques","first-page":"245","article-title":"Transparent CPU-GPU collaboration for data-parallel kernels on heterogeneous systems","author":"Lee","year":"2013"},{"issue":"3","key":"10.1016\/j.future.2026.108428_bib0012","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1109\/MCSE.2010.69","article-title":"OpenCL: a parallel programming standard for heterogeneous computing systems","volume":"12","author":"Stone","year":"2010","journal-title":"Comput. Sci. Eng."},{"key":"10.1016\/j.future.2026.108428_bib0013","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.jpdc.2021.06.003","article-title":"Sigmoid: an auto-tuned load balancing algorithm for heterogeneous systems","volume":"157","author":"P\u00e9rez","year":"2021","journal-title":"J. Parallel Distrib. Comput."},{"issue":"2","key":"10.1016\/j.future.2026.108428_bib0014","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1016\/0743-7315(89)90021-X","article-title":"Dynamic load balancing for distributed memory multiprocessors","volume":"7","author":"Cybenko","year":"1989","journal-title":"J Parallel Distrib. Comput."},{"key":"10.1016\/j.future.2026.108428_bib0015","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.jpdc.2018.05.006","article-title":"A collaborative CPU\u2013GPU approach for principal component analysis on mobile heterogeneous platforms","volume":"120","author":"Valery","year":"2018","journal-title":"J Parallel Distrib. Comput."},{"key":"10.1016\/j.future.2026.108428_bib0016","series-title":"Proceedings of the Second Workshop on Accelerator Programming Using Directives","first-page":"1","article-title":"SSMART: Smart scheduling of multi-architecture tasks on heterogeneous systems","author":"Planas","year":"2015"},{"issue":"1","key":"10.1016\/j.future.2026.108428_bib0017","doi-asserted-by":"crossref","first-page":"330","DOI":"10.1007\/s11227-016-1864-y","article-title":"Energy efficiency of load balancing for data-parallel applications in heterogeneous systems","volume":"73","author":"P\u00e9rez","year":"2017","journal-title":"J. Supercomput."},{"issue":"12","key":"10.1016\/j.future.2026.108428_bib0018","doi-asserted-by":"crossref","first-page":"2943","DOI":"10.1007\/s00607-021-01017-6","article-title":"Optimization of heterogeneous systems with AI planning heuristics and machine learning: a performance and energy aware approach","volume":"103","author":"Memeti","year":"2021","journal-title":"Computing"},{"issue":"1","key":"10.1016\/j.future.2026.108428_bib0019","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1007\/s10766-021-00721-2","article-title":"A profile-based AI-assisted dynamic scheduling approach for heterogeneous architectures","volume":"50","author":"Geng","year":"2022","journal-title":"Int. J. Parallel Program."},{"issue":"21","key":"10.1016\/j.future.2026.108428_bib0020","article-title":"KubeSC-RTP: smart scheduler for kubernetes platform on CPU-GPU heterogeneous systems","volume":"34","author":"Harichane","year":"2022","journal-title":"Concurr. Comput. Practice and Exp."},{"key":"10.1016\/j.future.2026.108428_bib0021","series-title":"2014 21St International Conference on High Performance Computing (HiPC)","first-page":"1","article-title":"Smart multi-task scheduling for openCL programs on CPU\/GPU heterogeneous platforms","author":"Wen","year":"2014"},{"issue":"2","key":"10.1016\/j.future.2026.108428_bib0022","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11227-024-06907-y","article-title":"Intelligent energy pairing scheduler (inEPS) for heterogeneous HPC clusters","volume":"81","author":"L\u00f3pez","year":"2025","journal-title":"J. Supercomput."},{"key":"10.1016\/j.future.2026.108428_bib0023","unstructured":"Z. Bai, D. Wu, P. Dangi, D. Wijerathne, V.P.K. Miriyala, T. Mitra, Data-aware Dynamic Execution of Irregular Workloads on Heterogeneous Systems, arXiv preprint arXiv: 2502.06304(2025)."},{"key":"10.1016\/j.future.2026.108428_bib0024","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.jpdc.2019.05.015","article-title":"Troodon: a machine-learning based load-balancing application scheduler for CPU\u2013GPU system","volume":"132","author":"Khalid","year":"2019","journal-title":"J. Parallel Distrib. Comput."},{"issue":"1","key":"10.1016\/j.future.2026.108428_bib0025","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1007\/s00500-020-05152-8","article-title":"A load balance multi-scheduling model for openCL kernel tasks in an integrated cluster","volume":"25","author":"Ahmed","year":"2021","journal-title":"Soft Comput."},{"key":"10.1016\/j.future.2026.108428_bib0026","series-title":"2023 International Conference on Smart Computing and Application (ICSCA)","first-page":"1","article-title":"RTLB_Sched: real time load balancing scheduler for CPU-GPU heterogeneous systems","author":"Rahmani","year":"2023"},{"key":"10.1016\/j.future.2026.108428_bib0027","series-title":"2022 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)","first-page":"674","article-title":"HBalancer: A machine learning based load balancer in real time CPU-GPU heterogeneous systems","author":"Rahmani","year":"2022"},{"key":"10.1016\/j.future.2026.108428_bib0028","series-title":"2016 13Th IEEE Annual Consumer Communications & Networking Conference (CCNC)","first-page":"224","article-title":"A priority-weighted round robin scheduling strategy for a WBAN based healthcare monitoring system","author":"Manirabona","year":"2016"},{"key":"10.1016\/j.future.2026.108428_bib0029","series-title":"2014 16Th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing","first-page":"393","article-title":"Evaluating weighted round robin load balancing for cloud web services","author":"Wang","year":"2014"},{"issue":"24","key":"10.1016\/j.future.2026.108428_bib0030","doi-asserted-by":"crossref","first-page":"7342","DOI":"10.3390\/s20247342","article-title":"Experimental setup for investigating the efficient load balancing algorithms on virtual cloud","volume":"20","author":"Alankar","year":"2020","journal-title":"Sensors"},{"issue":"3","key":"10.1016\/j.future.2026.108428_bib0031","first-page":"6","article-title":"An improved weighted least connection scheduling algorithm for load balancing in web cluster systems","volume":"5","author":"Singh","year":"2018","journal-title":"International Research Journal of Engineering and Technology (IRJET)"},{"issue":"14","key":"10.1016\/j.future.2026.108428_bib0032","doi-asserted-by":"crossref","DOI":"10.1002\/cpe.5606","article-title":"RALB-HC: A resource-aware load balancer for heterogeneous cluster","volume":"33","author":"Ahmed","year":"2021","journal-title":"Concurr. Comput. Pract. Exp."},{"key":"10.1016\/j.future.2026.108428_bib0033","series-title":"2012 Innovative Parallel Computing (InPar)","first-page":"1","article-title":"Auto-tuning a high-level language targeted to GPU codes","author":"Grauer-Gray","year":"2012"},{"key":"10.1016\/j.future.2026.108428_bib0034","unstructured":"A. Sampson, LLVM for Grad Students, 2015. https:\/\/www.cs.cornell.edu\/~asampson\/blog\/llvm.html."},{"key":"10.1016\/j.future.2026.108428_bib0035","unstructured":"M. Ali, PyCaret: An open source, low-code machine learning library in Python, 2020. PyCaret version 1.0.0, https:\/\/www.pycaret.org."},{"issue":"1","key":"10.1016\/j.future.2026.108428_bib0036","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1186\/s40537-020-00327-4","article-title":"Selecting critical features for data classification based on machine learning methods","volume":"7","author":"Chen","year":"2020","journal-title":"J. Big Data"},{"key":"10.1016\/j.future.2026.108428_bib0037","series-title":"Sixth International Conference on Machine Learning and Applications (ICMLA 2007)","first-page":"429","article-title":"Enhanced recursive feature elimination","author":"Chen","year":"2007"},{"key":"10.1016\/j.future.2026.108428_bib0038","doi-asserted-by":"crossref","DOI":"10.7717\/peerj-cs.623","article-title":"The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation","volume":"7","author":"Chicco","year":"2021","journal-title":"PeerJ Comput. Sci."},{"key":"10.1016\/j.future.2026.108428_bib0039","unstructured":"MIT, Keras Documentation, https:\/\/keras.io\/api\/."},{"key":"10.1016\/j.future.2026.108428_bib0040","unstructured":"Google, Tensorflow framework, https:\/\/www.tensorflow.org\/."},{"key":"10.1016\/j.future.2026.108428_bib0041","unstructured":"Intel Xeon E3-1225, https:\/\/ark.intel.com\/content\/www\/fr\/fr\/ark\/products\/52270\/intel-xeon-processor-e31225-6m-cache-3-10-ghz.html."},{"key":"10.1016\/j.future.2026.108428_bib0042","unstructured":"NVIDIA QUADRO P400, https:\/\/www.nvidia.com\/content\/dam\/en-zz\/Solutions\/design-visualization\/productspage\/quadro\/quadro-desktop\/quadro-pascal-p400-data-sheet-us-nv-704503-r1.pdf."},{"key":"10.1016\/j.future.2026.108428_bib0043","unstructured":"Processeur Intel CoreTMi7-6700, https:\/\/ark.intel.com\/content\/www\/fr\/fr\/ark\/products\/88196\/intel-core-i76700-processor-8m-cache-up-to-4-00-ghz.html."},{"key":"10.1016\/j.future.2026.108428_bib0044","unstructured":"Nvidia, NVIDIA QUADRO K620, https:\/\/www.nvidia.com\/content\/dam\/en-zz\/Solutions\/design-visualization\/documents\/75509_DS_NV_Quadro_K620_US_NV_HR.pdf."},{"key":"10.1016\/j.future.2026.108428_bib0045","doi-asserted-by":"crossref","first-page":"886","DOI":"10.1007\/s11227-013-0870-6","article-title":"An efficient scheduling scheme using estimated execution time for heterogeneous computing systems","volume":"65","author":"Choi","year":"2013","journal-title":"J. Supercomput."},{"key":"10.1016\/j.future.2026.108428_bib0046","unstructured":"Ubuntu, Perf, https:\/\/www.man7.org\/linux\/man-pages\/man1\/perf.1.html."},{"key":"10.1016\/j.future.2026.108428_bib0047","unstructured":"Nvidia-smi,https:\/\/developer.download.nvidia.com\/compute\/DCGM\/docs\/nvidia-smi-367.38.pdf."}],"container-title":["Future Generation Computer Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0167739X26000622?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0167739X26000622?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T14:36:39Z","timestamp":1772721399000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0167739X26000622"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8]]},"references-count":47,"alternative-id":["S0167739X26000622"],"URL":"https:\/\/doi.org\/10.1016\/j.future.2026.108428","relation":{},"ISSN":["0167-739X"],"issn-type":[{"value":"0167-739X","type":"print"}],"subject":[],"published":{"date-parts":[[2026,8]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Real-time AI-powered monitoring for energy-efficient scheduling in multi-node heterogeneous systems","name":"articletitle","label":"Article Title"},{"value":"Future Generation Computer Systems","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.future.2026.108428","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Authors. Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"108428"}}