| 摘要: |
| 移动边缘计算场景中任务的不确定性增加了任务卸载及资源分配的复杂性和难度.鉴于此,提出一种移动边缘计算不确定性任务持续卸载及资源分配方法.首先,构建一种移动边缘计算不确定性任务持续卸载模型,通过基于持续时间片划分的任务多批次处理技术应对任务的不确定性,并设计多设备计算资源协同机制提升对计算密集型任务的承载能力.其次,提出一种基于负载均衡的自适应策略选择算法,避免计算资源过度分配导致信道拥堵进而产生额外能耗.最后,基于泊松分布实现了对不确定任务场景模型的仿真,大量实验结果表明时间片长度减小能够降低系统总能耗.此外,所提算法能够更有效地实现任务卸载及资源分配,相较于对比算法,最大可降低能耗11.8%. |
| 关键词: 移动边缘计算 不确定性任务 任务卸载 负载均衡 自适应 |
| DOI：10.13328/j.cnki.jos.006805 |
| 分类号:TP393 |
| 基金项目:国家自然科学基金(62172235);江苏省研究生实践创新计划(SJCX21_0301);中国博士后科学基金(2020M671552) |
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| Continuous Offloading and Resource Allocation Method of Uncertain Tasks in Mobile Edge Computing |
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XU Bin1, ZHAO Yun-Kai2, ZHU Jian-Ming1, LIU Yi-Chuan1, LI Xuan-Tao1, SUN Yan-Fei1, JI Yi-Mu2
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1.School of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing 210003, China;2.School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
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| Abstract: |
| The uncertainty of tasks in mobile edge computing scenarios makes task offloading and resource allocation more complex and difficult. Therefore, a continuous offloading and resource allocation method of uncertain tasks in mobile edge computing is proposed. Firstly, a continuous offloading model of uncertain tasks in mobile edge computing is built, and the multi-batch processing technology based on duration slice partition is employed to address task uncertainty. A multi-device computing resource coordination mechanism is designed to improve the carrying capacity of computation-intensive tasks. Secondly, an adaptive strategy selection algorithm based on load balancing is put forward to avoid channel congestion and additional energy consumption caused by the over-allocation of computing resources. Finally, the uncertain task scenario model is simulated based on Poisson distribution, and experimental results show that the reduction of time slice length can reduce the total energy consumption of the system. In addition, the proposed algorithm can achieve task offloading and resource allocation more effectively and can reduce energy consumption by up to 11.8% compared with comparison algorithms. |
| Key words: mobile edge computing (MEC) uncertain task task offloading load balancing adaptive |
