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CLC number: TP393

On-line Access: 2024-08-27

Received: 2023-10-17

Revision Accepted: 2024-05-08

Crosschecked: 2023-08-06

Cited: 0

Clicked: 1191

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

Reference

[1]Ai LH, Tan B, Zhang JD, et al., 2023. Dynamic offloading strategy for delay-sensitive task in mobile-edge computing networks. IEEE Int Things J, 10(1):526-538.

[2]Akhlaqi MY, Hanapi ZM, 2023. Task offloading paradigm in mobile edge computing—current issues, adopted approaches, and future directions. J Netw Comput Appl, 212:103568.

[3]Bai XJ, Jin SF, 2022. Performance analysis of an energy-saving strategy in cloud data centers based on a MMAP[K]/M[K]/N1+N2 non-preemptive priority queue. Fut Gener Comput Syst, 136:205-220.

[4]Chahoud M, Otoum S, Mourad A, 2023. On the feasibility of federated learning towards on-demand client deployment at the edge. Inform Process Manag, 60(1):103150.

[5]Djigal H, Xu J, Liu LF, et al., 2022. Machine and deep learning for resource allocation in multi-access edge computing: a survey. IEEE Commun Surv Tutor, 24(4):2449-2494.

[6]Feng C, Han PC, Zhang X, et al., 2022. Computation offloading in mobile edge computing networks: a survey. J Netw Comput Appl, 202:103366.

[7]Gholami A, Baras JS, 2021. Collaborative cloud-edge-local computation offloading for multi-component applications. Proc IEEE/ACM Symp on Edge Computing, p.361-365.

[8]Guo M, Wang W, Huang X, et al., 2022. Lyapunov-based partial computation offloading for multiple mobile devices enabled by harvested energy in MEC. IEEE Int Things J, 9(11):9025-9035.

[9]Guo XB, Du ZL, Jin SF, 2022. Nash equilibrium and social optimization of a task offloading strategy with real-time virtual machine repair in an edge computing system. Clust Comput, 25(6):3785-3797.

[10]Hao YX, Jiang YY, Chen T, et al., 2019. iTaskOffloading: intelligent task offloading for a cloud-edge collaborative system. IEEE Netw, 33(5):82-88.

[11]He JY, Zhang D, Zhou YZ, et al., 2020. A truthful online mechanism for collaborative computation offloading in mobile edge computing. IEEE Trans Ind Inform, 16(7):4832-4841.

[12]He XQ, Shen YH, Ren J, et al., 2022. An online auction-based incentive mechanism for soft-deadline tasks in collaborative edge computing. Fut Gener Comput Syst, 137:1-13.

[13]Hossain D, Huynh LNT, Sultana T, et al., 2020. Collaborative task offloading for overloaded mobile edge computing in small-cell networks. Proc Int Conf on Information Networking, p.717-722.

[14]Islam A, Debnath A, Ghose M, et al., 2021. A survey on task offloading in multi-access edge computing. J Syst Archit, 118:102225.

[15]Jayanetti A, Halgamuge S, Buyya R, 2022. Deep reinforcement learning for energy and time optimized scheduling of precedence-constrained tasks in edge-cloud computing environments. Fut Gener Comput Syst, 137:14-30.

[16]Kim C, Dudin A, Dudin S, et al., 2021. Mathematical model of operation of a cell of a mobile communication network with adaptive modulation schemes and handover of mobile users. IEEE Access, 9:106933-106946.

[17]Li W, Jin SF, 2021. Performance evaluation and optimization of a task offloading strategy on the mobile edge computing with edge heterogeneity. J Supercomput, 77(11):12486-12507.

[18]Li YZ, Qi F, Wang ZL, et al., 2020. Distributed edge computing offloading algorithm based on deep reinforcement learning. IEEE Access, 8:85204-85215.

[19]Liao HL, Li XY, Guo DK, et al., 2022. Dependency-aware application assigning and scheduling in edge computing. IEEE Int Things J, 9(6):4451-4463.

[20]Luo ZY, Huang A, 2021. Joint game theory and greedy optimization scheme of computation offloading for UAV-aided network. Proc 31st Int Telecommunication Networks and Applications Conf, p.198-203.

[21]Ma X, Wang SG, Zhang S, et al., 2021. Cost-efficient resource provisioning for dynamic requests in cloud assisted mobile edge computing. IEEE Trans Cloud Comput, 9(3):968-980.

[22]Mao YY, Zhang J, Letaief KB, 2016. Dynamic computation offloading for mobile-edge computing with energy harvesting devices. IEEE J Sel Areas Commun, 34(12):3590-3605.

[23]Mao YY, You CS, Zhang J, et al., 2017. A survey on mobile edge computing: the communication perspective. IEEE Commun Surv Tutor, 19(4):2322-2358.

[24]Muniswamaiah M, Agerwala T, Tappert CC, 2021. A survey on cloudlets, mobile edge, and fog computing. Proc 8th IEEE Int Conf on Cyber Security and Cloud Computing/7th IEEE Int Conf on Edge Computing and Scalable Cloud, p.139-142.

[25]Ranganath S, 2022. Edge computing: types and attributes. Adv Comput, 127:35-62.

[26]Saeik F, Avgeris M, Spatharakis D, et al., 2021. Task offloading in edge and cloud computing: a survey on mathematical, artificial intelligence and control theory solutions. Comput Netw, 195:108177.

[27]Song SN, Fang ZY, Jiang JY, 2022. Fast-DRD: fast decentralized reinforcement distillation for deadline-aware edge computing. Inform Process Manag, 59(2):102850.

[28]Stoyanova M, Nikoloudakis Y, Panagiotakis S, et al., 2020. A survey on the Internet of Things (IoT) forensics: challenges, approaches, and open issues. IEEE Commun Surv Tutor, 22(2):1191-1221.

[29]Su X, An L, Cheng Z, et al., 2023. Cloud-edge collaboration-based bi-level optimal scheduling for intelligent healthcare systems. Fut Gener Comput Syst, 141:28-39.

[30]Tan L, Kuang ZF, Zhao L, et al., 2022. Energy-efficient joint task offloading and resource allocation in OFDMA-based collaborative edge computing. IEEE Trans Wirel Commun, 21(3):1960-1972.

[31]Thai MT, Lin YD, Lai YC, et al., 2020. Workload and capacity optimization for cloud-edge computing systems with vertical and horizontal offloading. IEEE Trans Netw Serv Manag, 17(1):227-238.

[32]Tong Z, Deng XM, Ye F, et al., 2020. Adaptive computation offloading and resource allocation strategy in a mobile edge computing environment. Inform Sci, 537:116-131.

[33]Vhora F, Gandhi J, 2020. A comprehensive survey on mobile edge computing: challenges, tools, applications. Proc 4th Int Conf on Computing Methodologies and Communication, p.49-55.

[34]Wang YZ, Yu JQ, Yu ZB, 2023. Resource scheduling techniques in cloud from a view of coordination: a holistic survey. Front Inform Technol Electron Eng, 24(1):1-40.

[35]Wang ZY, Zhu Q, 2020. Partial task offloading strategy based on deep reinforcement learning. Proc IEEE 6th Int Conf on Computer and Communications, p.1516-1521.

[36]Wu JZ, Cao ZY, Zhang YJ, et al., 2019. Edge-cloud collaborative computation offloading model based on improved partical swarm optimization in MEC. Proc IEEE 25th Int Conf on Parallel and Distributed Systems, p.959-962.

[37]Xia SC, Wen XX, Yao ZX, et al., 2020. Dynamic task offloading and resource allocation for heterogeneous MEC-enable IoT. Proc IEEE/CIC Int Conf on Communications in China, p.847-852.

[38]Yang WY, Liu W, Wei XS, et al., 2021. EdgeKeeper: a trusted edge computing framework for ubiquitous power Internet of Things. Front Inform Technol Electron Eng, 22(3):374-399.

[39]Zhan WH, Luo CB, Min GY, et al., 2020. Mobility-aware multi-user offloading optimization for mobile edge computing. IEEE Trans Veh Technol, 69(3):3341-3356.

[40]Zhang JY, Yu P, Zhou FQ, et al., 2022. Resource and delay aware fine-grained service offloading in collaborative edge computing. Comput Netw, 218:109383.

[41]Zhang MJ, Cao JN, Yang L, et al., 2022. ENTS: an edge-native task scheduling system for collaborative edge computing. Proc IEEE/ACM 7th Symp on Edge Computing, p.149-161.

[42]Zhao H, Geng JW, Jin SF, 2023. Performance research on a task offloading strategy in a two-tier edge structure-based MEC system. J Supercomput, 79(9):10139-10177.

[43]Zheng T, Wan J, Zhang JL, et al., 2020. A survey of computation offloading in edge computing. Proc Int Conf on Computer, Information and Telecommunication Systems, p.1-6.

[44]Zhou WC, Fang WW, Li YY, et al., 2019. Markov approximation for task offloading and computation scaling in mobile edge computing. Mob Inform Syst, 2019:8172698.

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