Combining neural network-based method with heuristic policy for optimal task scheduling in hierarchical edge cloud
Deploying service nodes hierarchically at the edge of the network can effectively improve the service quality of offloaded task requests and increase the utilization of resources. In this paper, we study the task scheduling problem in the hierarchically deployed edge cloud. We first formulate the mi...
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| Format: | Article |
| Language: | English |
| Published: |
KeAi Communications Co., Ltd.
2023-06-01
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| Series: | Digital Communications and Networks |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352864822000724 |
| _version_ | 1827917711312683008 |
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| author | Zhuo Chen Peihong Wei Yan Li |
| author_facet | Zhuo Chen Peihong Wei Yan Li |
| author_sort | Zhuo Chen |
| collection | DOAJ |
| description | Deploying service nodes hierarchically at the edge of the network can effectively improve the service quality of offloaded task requests and increase the utilization of resources. In this paper, we study the task scheduling problem in the hierarchically deployed edge cloud. We first formulate the minimization of the service time of scheduled tasks in edge cloud as a combinatorial optimization problem, blue and then prove the NP-hardness of the problem. Different from the existing work that mostly designs heuristic approximation-based algorithms or policies to make scheduling decision, we propose a newly designed scheduling policy, named Joint Neural Network and Heuristic Scheduling (JNNHSP), which combines a neural network-based method with a heuristic based solution. JNNHSP takes the Sequence-to-Sequence (Seq2Seq) model trained by Reinforcement Learning (RL) as the primary policy and adopts the heuristic algorithm as the auxiliary policy to obtain the scheduling solution, thereby achieving a good balance between the quality and the efficiency of the scheduling solution. In-depth experiments show that compared with a variety of related policies and optimization solvers, JNNHSP can achieve better performance in terms of scheduling error ratio, the degree to which the policy is affected by resources limitations, average service latency, and execution efficiency in a typical hierarchical edge cloud. |
| first_indexed | 2024-03-13T03:32:20Z |
| format | Article |
| id | doaj.art-5657968e0a1c42a59e91b85a6bb45fbf |
| institution | Directory Open Access Journal |
| issn | 2352-8648 |
| language | English |
| last_indexed | 2024-03-13T03:32:20Z |
| publishDate | 2023-06-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Digital Communications and Networks |
| spelling | doaj.art-5657968e0a1c42a59e91b85a6bb45fbf2023-06-24T05:18:01ZengKeAi Communications Co., Ltd.Digital Communications and Networks2352-86482023-06-0193688697Combining neural network-based method with heuristic policy for optimal task scheduling in hierarchical edge cloudZhuo Chen0Peihong Wei1Yan Li2College of Computer Science and Engineering, Chongqing University of Technology, Chongqing, 200433, ChinaSchool of Artificial Intelligence, Chongqing University of Technology, Chongqing, 200433, China; Corresponding author.School of Artificial Intelligence, Chongqing University of Technology, Chongqing, 200433, ChinaDeploying service nodes hierarchically at the edge of the network can effectively improve the service quality of offloaded task requests and increase the utilization of resources. In this paper, we study the task scheduling problem in the hierarchically deployed edge cloud. We first formulate the minimization of the service time of scheduled tasks in edge cloud as a combinatorial optimization problem, blue and then prove the NP-hardness of the problem. Different from the existing work that mostly designs heuristic approximation-based algorithms or policies to make scheduling decision, we propose a newly designed scheduling policy, named Joint Neural Network and Heuristic Scheduling (JNNHSP), which combines a neural network-based method with a heuristic based solution. JNNHSP takes the Sequence-to-Sequence (Seq2Seq) model trained by Reinforcement Learning (RL) as the primary policy and adopts the heuristic algorithm as the auxiliary policy to obtain the scheduling solution, thereby achieving a good balance between the quality and the efficiency of the scheduling solution. In-depth experiments show that compared with a variety of related policies and optimization solvers, JNNHSP can achieve better performance in terms of scheduling error ratio, the degree to which the policy is affected by resources limitations, average service latency, and execution efficiency in a typical hierarchical edge cloud.http://www.sciencedirect.com/science/article/pii/S2352864822000724Edge cloudTask schedulingNeural networkReinforcement learning |
| spellingShingle | Zhuo Chen Peihong Wei Yan Li Combining neural network-based method with heuristic policy for optimal task scheduling in hierarchical edge cloud Digital Communications and Networks Edge cloud Task scheduling Neural network Reinforcement learning |
| title | Combining neural network-based method with heuristic policy for optimal task scheduling in hierarchical edge cloud |
| title_full | Combining neural network-based method with heuristic policy for optimal task scheduling in hierarchical edge cloud |
| title_fullStr | Combining neural network-based method with heuristic policy for optimal task scheduling in hierarchical edge cloud |
| title_full_unstemmed | Combining neural network-based method with heuristic policy for optimal task scheduling in hierarchical edge cloud |
| title_short | Combining neural network-based method with heuristic policy for optimal task scheduling in hierarchical edge cloud |
| title_sort | combining neural network based method with heuristic policy for optimal task scheduling in hierarchical edge cloud |
| topic | Edge cloud Task scheduling Neural network Reinforcement learning |
| url | http://www.sciencedirect.com/science/article/pii/S2352864822000724 |
| work_keys_str_mv | AT zhuochen combiningneuralnetworkbasedmethodwithheuristicpolicyforoptimaltaskschedulinginhierarchicaledgecloud AT peihongwei combiningneuralnetworkbasedmethodwithheuristicpolicyforoptimaltaskschedulinginhierarchicaledgecloud AT yanli combiningneuralnetworkbasedmethodwithheuristicpolicyforoptimaltaskschedulinginhierarchicaledgecloud |