{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T02:03:24Z","timestamp":1780020204125,"version":"3.53.1"},"reference-count":38,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62271036"],"award-info":[{"award-number":["62271036"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62301021"],"award-info":[{"award-number":["62301021"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62571022"],"award-info":[{"award-number":["62571022"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Future Generation Computer Systems"],"published-print":{"date-parts":[[2026,9]]},"DOI":"10.1016\/j.future.2026.108507","type":"journal-article","created":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T06:15:05Z","timestamp":1775196905000},"page":"108507","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["AM-A2C: Cloud computing task scheduling algorithm based on attention mechanism and action mutation"],"prefix":"10.1016","volume":"182","author":[{"given":"Yuzhang","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8639-2476","authenticated-orcid":false,"given":"Le","family":"Tian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maozu","family":"Guo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.future.2026.108507_bib0001","doi-asserted-by":"crossref","first-page":"448","DOI":"10.1016\/j.jclepro.2017.12.239","article-title":"Assessing ICT global emissions footprint: trends to 2040 & recommendations","volume":"177","author":"Belkhir","year":"2018","journal-title":"J. Clean. Prod."},{"key":"10.1016\/j.future.2026.108507_bib0002","unstructured":"I. E. Agency, Data Centres and Data Transmission Networks, 2022, (https:\/\/www.iea.org\/energy-system\/buildings\/data-centres-and-data-transmission-networks)."},{"issue":"11","key":"10.1016\/j.future.2026.108507_bib0003","first-page":"3003","article-title":"Deep reinforcement learning enhanced greedy optimization for online scheduling of batched tasks in cloud HPC systems","volume":"33","author":"Yang","year":"2021","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"10.1016\/j.future.2026.108507_bib0004","doi-asserted-by":"crossref","first-page":"1595","DOI":"10.1007\/s10586-015-0484-2","article-title":"Random task scheduling scheme based on reinforcement learning in cloud computing","volume":"18","author":"Peng","year":"2015","journal-title":"Cluster Comput."},{"issue":"3","key":"10.1016\/j.future.2026.108507_bib0005","doi-asserted-by":"crossref","first-page":"844","DOI":"10.1016\/j.ejor.2021.06.002","article-title":"A first fit type algorithm for the coupled task scheduling problem with unit execution time and two exact delays","volume":"297","author":"B\u00e9k\u00e9si","year":"2022","journal-title":"Eur. J. Oper. Res."},{"issue":"12","key":"10.1016\/j.future.2026.108507_bib0006","article-title":"Improved max-min algorithm in cloud computing","volume":"50","author":"Elzeki","year":"2012","journal-title":"Int. J. Comput. Appl."},{"key":"10.1016\/j.future.2026.108507_bib0007","doi-asserted-by":"crossref","first-page":"4591","DOI":"10.1007\/s10462-021-10006-2","article-title":"Application of an improved discrete crow search algorithm with local search and elitism on a humanitarian relief case","volume":"54","author":"Elig\u00fczel","year":"2021","journal-title":"Artif. Intell. Rev."},{"key":"10.1016\/j.future.2026.108507_bib0008","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2023.101955","article-title":"Prediction of TBM operation parameters using machine learning models based on BPSO","volume":"56","author":"Wang","year":"2023","journal-title":"Adv. Eng. Inf."},{"issue":"4","key":"10.1016\/j.future.2026.108507_bib0009","doi-asserted-by":"crossref","first-page":"780","DOI":"10.1109\/TCC.2015.2440257","article-title":"The value of cooperation: minimizing user costs in multi-broker mobile cloud computing networks","volume":"5","author":"Guan","year":"2015","journal-title":"IEEE Trans. Cloud Comput."},{"issue":"2","key":"10.1016\/j.future.2026.108507_bib0010","doi-asserted-by":"crossref","first-page":"766","DOI":"10.1109\/TSC.2019.2961082","article-title":"An on-line virtual machine consolidation strategy for dual improvement in performance and energy conservation of server clusters in cloud data centers","volume":"15","author":"Lin","year":"2019","journal-title":"IEEE Trans. Serv. Comput."},{"key":"10.1016\/j.future.2026.108507_bib0011","doi-asserted-by":"crossref","first-page":"1075","DOI":"10.1007\/s10586-020-03177-0","article-title":"Autonomic task scheduling algorithm for dynamic workloads through a load balancing technique for the cloud-computing environment","volume":"24","author":"Ebadifard","year":"2021","journal-title":"Cluster Comput."},{"key":"10.1016\/j.future.2026.108507_bib0012","article-title":"Parallel enhanced whale optimization algorithm for independent tasks scheduling on cloud computing","author":"Khan","year":"2024","journal-title":"IEEE Access"},{"key":"10.1016\/j.future.2026.108507_bib0013","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.knosys.2019.01.023","article-title":"Task scheduling in cloud computing based on hybrid moth search algorithm and differential evolution","volume":"169","author":"Abd Elaziz","year":"2019","journal-title":"Knowl. Based Syst."},{"issue":"6","key":"10.1016\/j.future.2026.108507_bib0014","doi-asserted-by":"crossref","first-page":"4415","DOI":"10.1007\/s42835-023-01502-2","article-title":"An energy and deadline-Aware scheduler with hybrid optimization in virtualized clouds","volume":"18","author":"Senthil Kumar","year":"2023","journal-title":"J. Electric. Eng. Technol."},{"issue":"2","key":"10.1016\/j.future.2026.108507_bib0015","doi-asserted-by":"crossref","first-page":"2127","DOI":"10.1109\/TCC.2022.3188672","article-title":"Energy and reliability-aware task scheduling for cost optimization of DVFS-enabled cloud workflows","volume":"11","author":"Cao","year":"2022","journal-title":"IEEE Trans. Cloud Comput."},{"key":"10.1016\/j.future.2026.108507_bib0016","article-title":"Damping-assisted evolutionary swarm intelligence for industrial iot task scheduling in cloud computing","author":"Gad","year":"2023","journal-title":"IEEE Internet Things J."},{"issue":"3","key":"10.1016\/j.future.2026.108507_bib0017","doi-asserted-by":"crossref","first-page":"519","DOI":"10.3390\/pr12030519","article-title":"Dynamic load balancing in cloud computing: optimized RL-Based clustering with multi-Objective optimized task scheduling","volume":"12","author":"Khan","year":"2024","journal-title":"Processes"},{"key":"10.1016\/j.future.2026.108507_bib0018","article-title":"Lsia3CS: deep reinforcement learning-Based cloud-Edge collaborative task scheduling in large-Scale IIot","author":"Zhang","year":"2024","journal-title":"IEEE Internet Things J."},{"issue":"10","key":"10.1016\/j.future.2026.108507_bib0019","doi-asserted-by":"crossref","first-page":"14799","DOI":"10.1007\/s11227-024-06035-7","article-title":"Energy-efficient DAG scheduling with DVFS for cloud data centers","volume":"80","author":"Yang","year":"2024","journal-title":"J. Supercomput."},{"issue":"2","key":"10.1016\/j.future.2026.108507_bib0020","first-page":"310","article-title":"Task scheduling strategy based on improved A2C algorithm for cloud data center","volume":"52","author":"Xu","year":"2025","journal-title":"Comput. Sci."},{"key":"10.1016\/j.future.2026.108507_bib0021","article-title":"DRL-Enabled Computation offloading for AIGC services in ioIT-Assisted edge computing networks","author":"Zhang","year":"2024","journal-title":"IEEE Internet Things J."},{"key":"10.1016\/j.future.2026.108507_bib0022","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2022.105710","article-title":"Multi-agent deep reinforcement learning for task offloading in group distributed manufacturing systems","volume":"118","author":"Xiong","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.future.2026.108507_bib0023","article-title":"EC-TRL: Evolutionary-Weighted clustering and transformer-Augmented reinforcement learning for dynamic resource scheduling in edge cloud environments","author":"Zhou","year":"2024","journal-title":"IEEE Internet Things J."},{"key":"10.1016\/j.future.2026.108507_bib0024","article-title":"Deep reinforcement learning task scheduling method for real-Time performance awareness","author":"Wang","year":"2025","journal-title":"IEEE Access"},{"issue":"6","key":"10.1016\/j.future.2026.108507_bib0025","doi-asserted-by":"crossref","first-page":"4158","DOI":"10.1109\/TII.2021.3113875","article-title":"Cloud\u2013edge collaborative SFC mapping for industrial IoT using deep reinforcement learning","volume":"18","author":"Xu","year":"2021","journal-title":"IEEE Trans. Ind. Inf."},{"key":"10.1016\/j.future.2026.108507_bib0026","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.ins.2018.08.032","article-title":"A cloud server energy consumption measurement system for heterogeneous cloud environments","volume":"468","author":"Lin","year":"2018","journal-title":"Inf. Sci. (Ny)"},{"key":"10.1016\/j.future.2026.108507_bib0027","series-title":"International Conference on Autonomous Unmanned Systems","first-page":"1160","article-title":"A HPEDF-Based reinforcement learning radar scheduling method","author":"Ye","year":"2022"},{"issue":"3","key":"10.1016\/j.future.2026.108507_bib0028","doi-asserted-by":"crossref","first-page":"4791","DOI":"10.1109\/JIOT.2018.2869226","article-title":"Joint optimization of energy consumption and latency in mobile edge computing for internet of things","volume":"6","author":"Cui","year":"2018","journal-title":"IEEE Internet Things J."},{"key":"10.1016\/j.future.2026.108507_bib0029","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.procs.2021.03.016","article-title":"Task scheduling in cloud using deep reinforcement learning","volume":"184","author":"Swarup","year":"2021","journal-title":"Procedia Comput. Sci."},{"key":"10.1016\/j.future.2026.108507_bib0030","doi-asserted-by":"crossref","DOI":"10.1007\/s11227-014-1177-y","article-title":"A queuing theory model for cloud computing","author":"Vilaplana Mayoral","year":"2014","journal-title":"J. Supercomput."},{"issue":"3","key":"10.1016\/j.future.2026.108507_bib0031","doi-asserted-by":"crossref","first-page":"1681","DOI":"10.1007\/s10586-018-2718-6","article-title":"Robust optimization for energy-efficient virtual machine consolidation in modern datacenters","volume":"21","author":"Nasim","year":"2018","journal-title":"Cluster Comput."},{"issue":"1","key":"10.1016\/j.future.2026.108507_bib0032","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1186\/s13677-020-00219-1","article-title":"Multi-objective workflow optimization strategy (MOWOS) for cloud computing","volume":"10","author":"Konjaang","year":"2021","journal-title":"J. Cloud Comput."},{"key":"10.1016\/j.future.2026.108507_bib0033","article-title":"Attention is all you need","author":"Vaswani","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.future.2026.108507_bib0034","unstructured":"S. Ioffe, Batch normalization: Accelerating deep network training by reducing internal covariate shift, arXiv preprint arXiv: 1502.03167(2015)."},{"key":"10.1016\/j.future.2026.108507_bib0035","series-title":"2019 16Th International Computer Conference on Wavelet Active Media Technology and Information Processing","first-page":"417","article-title":"Dialogue policy optimization based on KL-GAN-A2C model","author":"Liu","year":"2019"},{"issue":"3","key":"10.1016\/j.future.2026.108507_bib0036","doi-asserted-by":"crossref","first-page":"400","DOI":"10.1214\/aoms\/1177729586","article-title":"A stochastic approximation method","volume":"22","author":"Robbins","year":"1951","journal-title":"Annal. Math. Stat."},{"key":"10.1016\/j.future.2026.108507_bib0037","unstructured":"Alibaba Group, Alibaba Cluster Trace Program cluster-trace-v2018, 2018, (Online dataset). Accessed: 2026\/04\/06 11:17:40, https:\/\/github.com\/alibaba\/clusterdata\/tree\/master\/cluster-trace-v2018."},{"issue":"1","key":"10.1016\/j.future.2026.108507_bib0038","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1109\/TPDS.2021.3087349","article-title":"COSCO: Container orchestration using co-simulation and gradient based optimization for fog computing environments","volume":"33","author":"Tuli","year":"2021","journal-title":"IEEE Trans. Parallel Distrib. Syst."}],"container-title":["Future Generation Computer Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0167739X2600141X?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0167739X2600141X?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T01:34:34Z","timestamp":1780018474000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0167739X2600141X"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9]]},"references-count":38,"alternative-id":["S0167739X2600141X"],"URL":"https:\/\/doi.org\/10.1016\/j.future.2026.108507","relation":{},"ISSN":["0167-739X"],"issn-type":[{"value":"0167-739X","type":"print"}],"subject":[],"published":{"date-parts":[[2026,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"AM-A2C: Cloud computing task scheduling algorithm based on attention mechanism and action mutation","name":"articletitle","label":"Article Title"},{"value":"Future Generation Computer Systems","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.future.2026.108507","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"108507"}}