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CLC number: TP393

On-line Access: 2024-06-04

Received: 2023-02-28

Revision Accepted: 2024-06-04

Crosschecked: 2023-08-06

Cited: 0

Clicked: 475

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Shunfu JIN

https://orcid.org/0000-0002-5845-5601

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Frontiers of Information Technology & Electronic Engineering  2024 Vol.25 No.5 P.664-684

http://doi.org/10.1631/FITEE.2300128


A cloud-edge-device collaborative offloading scheme with heterogeneous tasks and its performance evaluation


Author(s):  Xiaojun BAI, Yang ZHANG, Haixing WU, Yuting WANG, Shunfu JIN

Affiliation(s):  School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China; more

Corresponding email(s):   jsf@ysu.edu.cn

Key Words:  Edge computing, Offloading scheme, Cloud-edge-device collaboration, Markov chain, Cost function


Xiaojun BAI, Yang ZHANG, Haixing WU, Yuting WANG, Shunfu JIN. A cloud-edge-device collaborative offloading scheme with heterogeneous tasks and its performance evaluation[J]. Frontiers of Information Technology & Electronic Engineering, 2024, 25(5): 664-684.

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Abstract: 
How to collaboratively offload tasks between user devices, edge networks (ENs), and cloud data centers is an interesting and challenging research topic. In this paper, we investigate the offloading decision, analytical modeling, and system parameter optimization problem in a collaborative cloud-edge-device environment, aiming to trade off different performance measures. According to the differentiated delay requirements of tasks, we classify the tasks into delay-sensitive and delay-tolerant tasks. To meet the delay requirements of delay-sensitive tasks and process as many delay-tolerant tasks as possible, we propose a cloud-edge-device collaborative task offloading scheme, in which delay-sensitive and delay-tolerant tasks follow the access threshold policy and the loss policy, respectively. We establish a four-dimensional continuous-time markov chain as the system model. By using the Gauss-Seidel method, we derive the stationary probability distribution of the system model. Accordingly, we present the blocking rate of delay-sensitive tasks and the average delay of these two types of tasks. Numerical experiments are conducted and analyzed to evaluate the system performance, and numerical simulations are presented to evaluate and validate the effectiveness of the proposed task offloading scheme. Finally, we optimize the access threshold in the EN buffer to obtain the minimum system cost with different proportions of delay-sensitive tasks.

一种面向多类任务的云-边-端协同卸载策略及其性能评估

白小军1,2,张扬1,2,武海星1,2,王宇廷1,2,金顺福1,2
1燕山大学信息科学与工程学院,中国秦皇岛市,066004
2河北省计算机虚拟技术与系统集成重点实验室,中国秦皇岛市,066004
摘要:如何在用户设备、边缘网络和云数据中心之间协同卸载任务是一个非常有趣且具有挑战性的研究课题。本文研究了云-边-端协同环境下的任务卸载决策、建模解析和系统参数优化问题,旨在权衡不同的性能指标。根据任务的不同延迟要求,将任务分类为延迟敏感型任务和延迟容忍型任务。为了在满足延迟敏感型任务的延迟需求的同时尽可能处理更多的延迟容忍型任务,提出一种云-边-端协同任务卸载策略,其中,延迟敏感型任务和延迟容忍型任务分别遵循访问阈值控制策略和损失策略。建立一个四维连续时间马尔可夫链作为系统机理模型,利用高斯-赛德尔方法,求解系统模型的平稳概率分布。在此基础上,给出延迟敏感型任务的阻塞率和两类任务的平均延迟等性能指标。通过数值实验评估了系统性能,并通过仿真实验验证了所提任务卸载策略的有效性。最后,针对不同延迟敏感型任务比例,优化了边缘网络缓冲区中的访问阈值,实现了系统开销的最小化。

关键词:边缘计算;卸载策略;云-边-端协同;马尔可夫链;成本函数

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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