Q value model.
<div><p>With the increasing popularity of cloud computing services, their large and dynamic load characteristics have rendered task scheduling an NP-complete problem.To address the problem of large-scale task scheduling in a cloud computing environment, this paper proposes a novel cloud...
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2025
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| _version_ | 1852017399090380800 |
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| author | Delong Cui (21730915) |
| author2 | Zhiping Peng (5783078) Kaibin Li (1571551) Qirui Li (6578297) Jieguang He (17100746) Xiangwu Deng (6612770) |
| author2_role | author author author author author |
| author_facet | Delong Cui (21730915) Zhiping Peng (5783078) Kaibin Li (1571551) Qirui Li (6578297) Jieguang He (17100746) Xiangwu Deng (6612770) |
| author_role | author |
| dc.creator.none.fl_str_mv | Delong Cui (21730915) Zhiping Peng (5783078) Kaibin Li (1571551) Qirui Li (6578297) Jieguang He (17100746) Xiangwu Deng (6612770) |
| dc.date.none.fl_str_mv | 2025-08-21T17:40:54Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0329669.g003 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/Q_value_model_/29961876 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Science Policy Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified service level agreement scale task scheduling rendered task scheduling one potential shortcoming employs hierarchical scheduling could impact real classical heuristic algorithms cloud task scheduling experimental results demonstrate skillfully balances cost cost nodes within cloud computing services cloud computing environment updating network parameters performance heavily depends framework &# 8217 allocate tasks first dynamic load characteristics designed using drl cloud computing cloud environments experiments demonstrate dynamic changes load situations load balancing load balance xlink "> virtual machines using low training data still shortcomings resource utilization paper proposes overdue time overall improvement optimizing objectives method used machine learning increasing popularity framework defines continuously learning continuous learning computational overhead |
| dc.title.none.fl_str_mv | Q value model. |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <div><p>With the increasing popularity of cloud computing services, their large and dynamic load characteristics have rendered task scheduling an NP-complete problem.To address the problem of large-scale task scheduling in a cloud computing environment, this paper proposes a novel cloud task scheduling framework using hierarchical deep reinforcement learning (DRL) to address the challenges of large-scale task scheduling in cloud computing. The framework defines a set of virtual machines (VMs) as a VM cluster and employs hierarchical scheduling to allocate tasks first to the cluster and then to individual VMs. The scheduler, designed using DRL, adapts to dynamic changes in the cloud environments by continuously learning and updating network parameters. Experiments demonstrate that it skillfully balances cost and performance. In low-load situations, costs are reduced by using low-cost nodes within the Service Level Agreement (SLA) range; in high-load situations, resource utilization is improved through load balancing. Compared with classical heuristic algorithms, it effectively optimizes load balancing, cost, and overdue time, achieving a 10% overall improvement. The experimental results demonstrate that this approach effectively balances cost and performance, optimizing objectives such as load balance, cost, and overdue time. One potential shortcoming of the proposed hierarchical deep reinforcement learning (DRL) framework for cloud task scheduling is its complexity and computational overhead. Implementing and maintaining a DRL-based scheduler requires significant computational resources and expertise in machine learning. There are still shortcomings in the method used in this study. First, the continuous learning and updating of network parameters might introduce latency, which could impact real-time task scheduling efficiency. Furthermore, the framework’s performance heavily depends on the quality and quantity of training data, which might be challenging to obtain and maintain in a dynamic cloud environment.</p></div> |
| eu_rights_str_mv | openAccess |
| id | Manara_70e88fa80eab00c8adcd293ea9b6d0da |
| identifier_str_mv | 10.1371/journal.pone.0329669.g003 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/29961876 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Q value model.Delong Cui (21730915)Zhiping Peng (5783078)Kaibin Li (1571551)Qirui Li (6578297)Jieguang He (17100746)Xiangwu Deng (6612770)Science PolicySpace ScienceBiological Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedservice level agreementscale task schedulingrendered task schedulingone potential shortcomingemploys hierarchical schedulingcould impact realclassical heuristic algorithmscloud task schedulingexperimental results demonstrateskillfully balances costcost nodes withincloud computing servicescloud computing environmentupdating network parametersperformance heavily dependsframework &# 8217allocate tasks firstdynamic load characteristicsdesigned using drlcloud computingcloud environmentsexperiments demonstratedynamic changesload situationsload balancingload balancexlink ">virtual machinesusing lowtraining datastill shortcomingsresource utilizationpaper proposesoverdue timeoverall improvementoptimizing objectivesmethod usedmachine learningincreasing popularityframework definescontinuously learningcontinuous learningcomputational overhead<div><p>With the increasing popularity of cloud computing services, their large and dynamic load characteristics have rendered task scheduling an NP-complete problem.To address the problem of large-scale task scheduling in a cloud computing environment, this paper proposes a novel cloud task scheduling framework using hierarchical deep reinforcement learning (DRL) to address the challenges of large-scale task scheduling in cloud computing. The framework defines a set of virtual machines (VMs) as a VM cluster and employs hierarchical scheduling to allocate tasks first to the cluster and then to individual VMs. The scheduler, designed using DRL, adapts to dynamic changes in the cloud environments by continuously learning and updating network parameters. Experiments demonstrate that it skillfully balances cost and performance. In low-load situations, costs are reduced by using low-cost nodes within the Service Level Agreement (SLA) range; in high-load situations, resource utilization is improved through load balancing. Compared with classical heuristic algorithms, it effectively optimizes load balancing, cost, and overdue time, achieving a 10% overall improvement. The experimental results demonstrate that this approach effectively balances cost and performance, optimizing objectives such as load balance, cost, and overdue time. One potential shortcoming of the proposed hierarchical deep reinforcement learning (DRL) framework for cloud task scheduling is its complexity and computational overhead. Implementing and maintaining a DRL-based scheduler requires significant computational resources and expertise in machine learning. There are still shortcomings in the method used in this study. First, the continuous learning and updating of network parameters might introduce latency, which could impact real-time task scheduling efficiency. Furthermore, the framework’s performance heavily depends on the quality and quantity of training data, which might be challenging to obtain and maintain in a dynamic cloud environment.</p></div>2025-08-21T17:40:54ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pone.0329669.g003https://figshare.com/articles/figure/Q_value_model_/29961876CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/299618762025-08-21T17:40:54Z |
| spellingShingle | Q value model. Delong Cui (21730915) Science Policy Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified service level agreement scale task scheduling rendered task scheduling one potential shortcoming employs hierarchical scheduling could impact real classical heuristic algorithms cloud task scheduling experimental results demonstrate skillfully balances cost cost nodes within cloud computing services cloud computing environment updating network parameters performance heavily depends framework &# 8217 allocate tasks first dynamic load characteristics designed using drl cloud computing cloud environments experiments demonstrate dynamic changes load situations load balancing load balance xlink "> virtual machines using low training data still shortcomings resource utilization paper proposes overdue time overall improvement optimizing objectives method used machine learning increasing popularity framework defines continuously learning continuous learning computational overhead |
| status_str | publishedVersion |
| title | Q value model. |
| title_full | Q value model. |
| title_fullStr | Q value model. |
| title_full_unstemmed | Q value model. |
| title_short | Q value model. |
| title_sort | Q value model. |
| topic | Science Policy Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified service level agreement scale task scheduling rendered task scheduling one potential shortcoming employs hierarchical scheduling could impact real classical heuristic algorithms cloud task scheduling experimental results demonstrate skillfully balances cost cost nodes within cloud computing services cloud computing environment updating network parameters performance heavily depends framework &# 8217 allocate tasks first dynamic load characteristics designed using drl cloud computing cloud environments experiments demonstrate dynamic changes load situations load balancing load balance xlink "> virtual machines using low training data still shortcomings resource utilization paper proposes overdue time overall improvement optimizing objectives method used machine learning increasing popularity framework defines continuously learning continuous learning computational overhead |