Enhanced DDPG algorithm for latency and energy-efficient task scheduling in MEC systems
Abstract This paper presents an improved Deep Deterministic Policy Gradient (DDPG) algorithm for task scheduling in Mobile Edge Computing (MEC) systems, focusing on reducing latency and energy consumption. The enhanced DDPG incorporates a soft update mechanism for target network parameters and an ex...
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| Format: | Article |
| Language: | English |
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Springer
2025-04-01
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| Series: | Discover Internet of Things |
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| Online Access: | https://doi.org/10.1007/s43926-025-00134-4 |
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| author | Fei Zhou Lihong Zhao Xiaomei Ding Shuqin Wang |
| author_facet | Fei Zhou Lihong Zhao Xiaomei Ding Shuqin Wang |
| author_sort | Fei Zhou |
| collection | DOAJ |
| description | Abstract This paper presents an improved Deep Deterministic Policy Gradient (DDPG) algorithm for task scheduling in Mobile Edge Computing (MEC) systems, focusing on reducing latency and energy consumption. The enhanced DDPG incorporates a soft update mechanism for target network parameters and an experience replay buffer to improve stability and training efficiency. A composite reward function, considering both latency and energy consumption, is designed and compared in linear and nonlinear forms. Experimental results demonstrate that the nonlinear reward function achieves faster convergence and better system performance. The improved DDPG outperforms traditional algorithms like Shortest Job First (SJF), Round Robin (RR), and Earliest Deadline First (EDF) in terms of latency and energy consumption across different task loads. At a task load of 10, the improved DDPG achieved a latency of 250 ms and energy consumption of 965 J, significantly lower than other algorithms. Ablation experiments further confirm the positive impact of each enhancement, showing that the combination of soft updates and experience replay buffers significantly reduces both latency and energy consumption. The study concludes that the improved DDPG algorithm provides an effective solution for task scheduling in MEC systems, offering robustness and adaptability in complex network environments. Future research will focus on real-world deployment and further optimization of the algorithm for diverse MEC scenarios. |
| format | Article |
| id | doaj-art-ebfc4de03ee040b1a71578c73f1de6e1 |
| institution | OA Journals |
| issn | 2730-7239 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Internet of Things |
| spelling | doaj-art-ebfc4de03ee040b1a71578c73f1de6e12025-08-20T02:28:10ZengSpringerDiscover Internet of Things2730-72392025-04-015111310.1007/s43926-025-00134-4Enhanced DDPG algorithm for latency and energy-efficient task scheduling in MEC systemsFei Zhou0Lihong Zhao1Xiaomei Ding2Shuqin Wang3Anhui Wenda University of Information EngineeringAnhui Wenda University of Information EngineeringAnhui Wenda University of Information EngineeringAnhui Wenda University of Information EngineeringAbstract This paper presents an improved Deep Deterministic Policy Gradient (DDPG) algorithm for task scheduling in Mobile Edge Computing (MEC) systems, focusing on reducing latency and energy consumption. The enhanced DDPG incorporates a soft update mechanism for target network parameters and an experience replay buffer to improve stability and training efficiency. A composite reward function, considering both latency and energy consumption, is designed and compared in linear and nonlinear forms. Experimental results demonstrate that the nonlinear reward function achieves faster convergence and better system performance. The improved DDPG outperforms traditional algorithms like Shortest Job First (SJF), Round Robin (RR), and Earliest Deadline First (EDF) in terms of latency and energy consumption across different task loads. At a task load of 10, the improved DDPG achieved a latency of 250 ms and energy consumption of 965 J, significantly lower than other algorithms. Ablation experiments further confirm the positive impact of each enhancement, showing that the combination of soft updates and experience replay buffers significantly reduces both latency and energy consumption. The study concludes that the improved DDPG algorithm provides an effective solution for task scheduling in MEC systems, offering robustness and adaptability in complex network environments. Future research will focus on real-world deployment and further optimization of the algorithm for diverse MEC scenarios.https://doi.org/10.1007/s43926-025-00134-4Mobile edge computingTask schedulingLow latencyEnergy consumptionDeep deterministic policy gradient |
| spellingShingle | Fei Zhou Lihong Zhao Xiaomei Ding Shuqin Wang Enhanced DDPG algorithm for latency and energy-efficient task scheduling in MEC systems Discover Internet of Things Mobile edge computing Task scheduling Low latency Energy consumption Deep deterministic policy gradient |
| title | Enhanced DDPG algorithm for latency and energy-efficient task scheduling in MEC systems |
| title_full | Enhanced DDPG algorithm for latency and energy-efficient task scheduling in MEC systems |
| title_fullStr | Enhanced DDPG algorithm for latency and energy-efficient task scheduling in MEC systems |
| title_full_unstemmed | Enhanced DDPG algorithm for latency and energy-efficient task scheduling in MEC systems |
| title_short | Enhanced DDPG algorithm for latency and energy-efficient task scheduling in MEC systems |
| title_sort | enhanced ddpg algorithm for latency and energy efficient task scheduling in mec systems |
| topic | Mobile edge computing Task scheduling Low latency Energy consumption Deep deterministic policy gradient |
| url | https://doi.org/10.1007/s43926-025-00134-4 |
| work_keys_str_mv | AT feizhou enhancedddpgalgorithmforlatencyandenergyefficienttaskschedulinginmecsystems AT lihongzhao enhancedddpgalgorithmforlatencyandenergyefficienttaskschedulinginmecsystems AT xiaomeiding enhancedddpgalgorithmforlatencyandenergyefficienttaskschedulinginmecsystems AT shuqinwang enhancedddpgalgorithmforlatencyandenergyefficienttaskschedulinginmecsystems |