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

[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 31^st 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 8^th IEEE Int Conf on Cyber Security and Cloud Computing/7^th 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 4^th 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 6^th 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 25^th 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 7^th 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.