Task Scheduling for Federated Learning in Edge Cloud Computing Environments by Using Adaptive-Greedy Dingo Optimization Algorithm and Binary Salp Swarm Algorithm
With the development of computationally intensive applications, the demand for edge cloud computing systems has increased, creating significant challenges for edge cloud computing networks. In this paper, we consider a simple three-tier computational model for multiuser mobile edge computing (MEC) a...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Future Internet |
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| Online Access: | https://www.mdpi.com/1999-5903/15/11/357 |
| _version_ | 1797459280201252864 |
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| author | Weihong Cai Fengxi Duan |
| author_facet | Weihong Cai Fengxi Duan |
| author_sort | Weihong Cai |
| collection | DOAJ |
| description | With the development of computationally intensive applications, the demand for edge cloud computing systems has increased, creating significant challenges for edge cloud computing networks. In this paper, we consider a simple three-tier computational model for multiuser mobile edge computing (MEC) and introduce two major problems of task scheduling for federated learning in MEC environments: (1) the transmission power allocation (PA) problem, and (2) the dual decision-making problems of joint request offloading and computational resource scheduling (JRORS). At the same time, we factor in server pricing and task completion, in order to improve the user-friendliness and fairness in scheduling decisions. The solving of these problems simultaneously ensures both scheduling efficiency and system quality of service (QoS), to achieve a balance between efficiency and user satisfaction. Then, we propose an adaptive greedy dingo optimization algorithm (AGDOA) based on greedy policies and parameter adaptation to solve the PA problem and construct a binary salp swarm algorithm (BSSA) that introduces binary coding to solve the discrete JRORS problem. Finally, simulations were conducted to verify the better performance compared to the traditional algorithms. The proposed algorithm improved the convergence speed of the algorithm in terms of scheduling efficiency, improved the system response rate, and found solutions with a lower energy consumption. In addition, the search results had a higher fairness and system welfare in terms of system quality of service. |
| first_indexed | 2024-03-09T16:49:11Z |
| format | Article |
| id | doaj.art-adaf47b5b477421593f32ff23b5c027a |
| institution | Directory Open Access Journal |
| issn | 1999-5903 |
| language | English |
| last_indexed | 2024-03-09T16:49:11Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Future Internet |
| spelling | doaj.art-adaf47b5b477421593f32ff23b5c027a2023-11-24T14:43:10ZengMDPI AGFuture Internet1999-59032023-10-01151135710.3390/fi15110357Task Scheduling for Federated Learning in Edge Cloud Computing Environments by Using Adaptive-Greedy Dingo Optimization Algorithm and Binary Salp Swarm AlgorithmWeihong Cai0Fengxi Duan1Department of Computer, Shantou University, Shantou 515063, ChinaDepartment of Computer, Shantou University, Shantou 515063, ChinaWith the development of computationally intensive applications, the demand for edge cloud computing systems has increased, creating significant challenges for edge cloud computing networks. In this paper, we consider a simple three-tier computational model for multiuser mobile edge computing (MEC) and introduce two major problems of task scheduling for federated learning in MEC environments: (1) the transmission power allocation (PA) problem, and (2) the dual decision-making problems of joint request offloading and computational resource scheduling (JRORS). At the same time, we factor in server pricing and task completion, in order to improve the user-friendliness and fairness in scheduling decisions. The solving of these problems simultaneously ensures both scheduling efficiency and system quality of service (QoS), to achieve a balance between efficiency and user satisfaction. Then, we propose an adaptive greedy dingo optimization algorithm (AGDOA) based on greedy policies and parameter adaptation to solve the PA problem and construct a binary salp swarm algorithm (BSSA) that introduces binary coding to solve the discrete JRORS problem. Finally, simulations were conducted to verify the better performance compared to the traditional algorithms. The proposed algorithm improved the convergence speed of the algorithm in terms of scheduling efficiency, improved the system response rate, and found solutions with a lower energy consumption. In addition, the search results had a higher fairness and system welfare in terms of system quality of service.https://www.mdpi.com/1999-5903/15/11/357edge cloud computingInternet of thingsdingo optimization algorithmsalp swarm algorithmfederated learning |
| spellingShingle | Weihong Cai Fengxi Duan Task Scheduling for Federated Learning in Edge Cloud Computing Environments by Using Adaptive-Greedy Dingo Optimization Algorithm and Binary Salp Swarm Algorithm Future Internet edge cloud computing Internet of things dingo optimization algorithm salp swarm algorithm federated learning |
| title | Task Scheduling for Federated Learning in Edge Cloud Computing Environments by Using Adaptive-Greedy Dingo Optimization Algorithm and Binary Salp Swarm Algorithm |
| title_full | Task Scheduling for Federated Learning in Edge Cloud Computing Environments by Using Adaptive-Greedy Dingo Optimization Algorithm and Binary Salp Swarm Algorithm |
| title_fullStr | Task Scheduling for Federated Learning in Edge Cloud Computing Environments by Using Adaptive-Greedy Dingo Optimization Algorithm and Binary Salp Swarm Algorithm |
| title_full_unstemmed | Task Scheduling for Federated Learning in Edge Cloud Computing Environments by Using Adaptive-Greedy Dingo Optimization Algorithm and Binary Salp Swarm Algorithm |
| title_short | Task Scheduling for Federated Learning in Edge Cloud Computing Environments by Using Adaptive-Greedy Dingo Optimization Algorithm and Binary Salp Swarm Algorithm |
| title_sort | task scheduling for federated learning in edge cloud computing environments by using adaptive greedy dingo optimization algorithm and binary salp swarm algorithm |
| topic | edge cloud computing Internet of things dingo optimization algorithm salp swarm algorithm federated learning |
| url | https://www.mdpi.com/1999-5903/15/11/357 |
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