Computer Science ›› 2021, Vol. 48 ›› Issue (1): 40-48.doi: 10.11896/jsjkx.200900195
Special Issue: Intelligent Edge Computing
• Intelligent Edge Computing • Previous Articles Next Articles
MA Yu-yin1, ZHENG Wan-bo2, MA Yong3, LIU Hang1, XIA Yun-ni1, GUO Kun-yin1, CHEN Peng4, LIU Cheng-wu5
CLC Number:
[1] CHEN X,LIU Z,CHEN Y,et al.Mobile edge computing based task offloading and resource allocation in 5g ultra-dense networks[J].IEEE Access,2019,7:184172-184182. [2] LYU X,NI W,TIAN H L,et al.Optimal schedule of mobile edge computing for internet of things using partial information[J].IEEE Journal on Selected Areas in Communications,2017,35(11):2606-2615. [3] ZHANG Y,DU P.Delay-driven computation task scheduling in multi-cell cellular edge computing systems[J].IEEE Access,2019,7:149156-149167. [4] CAO H,XU X,LIU Q,et al.Uncertainty-aware resource provisioning for workflow scheduling in edge computing environment[C]//18th IEEE International Conference on Trust,Security And Privacy In Computing And Communications/13th IEEE International Conference on Big Data Science And Engineering,TrustCom/BigDataSE.Rotorua,New Zealand,2019:734-739. [5] DENG Y,CHEN,YAO X,et al.Task scheduling for smart city applications based on multi-server mobile edge computing[J].IEEE Access,2019,7:14410-14421. [6] CHEN J F,CHEN J W,JING P,et al.An improved chaotic bat swarm scheduling learning model on edge computing[J].IEEE Access,2019:58602-58610. [7] MA Y,ZHANG J,WANG S,et al.A Novel Approach to Cost-Efficient Scheduling of Multi-workflows in the Edge Computing Environment with the Proximity Constraint[C]//International Conference on Algorithms and Architectures for Parallel Processing.Springer,Cham,2019:655-668. [8] PENG Q,JIANG H,CHEN M,et al.Reliability-aware andDeadline-constrained workflow scheduling in Mobile Edge Computing[C]//2019 IEEE 16th International Conference on Networking,Sensing and Control (ICNSC).IEEE,2019:236-241. [9] BERNAL J,KUSHIBAR K,ASFAW D S,et al.Deep convolutional neural networks for brain image analysis on magnetic resonance imaging:a review[J].Artificial intelligence in medicine,2019,95:64-81. [10] BOUWMANS T,JAVED S,SULTANA M,et al.Deep neural network concepts for background subtraction:A systematic review and comparative evaluation[J].Neural Networks,2019,117:8-66. [11] GREKOUSIS G.Artificial neural networks and deep learning in urban geography:A systematic review and meta-analysis[J].Computers,Environment and Urban Systems,2019,74:244-256. [12] LAI P,HE Q,ABDELRAZEK M,et al.Optimal edge user allocation in edge computing with variable sized vector bin packing[C]//International Conference on Service-Oriented Computing.Springer,Cham,2018:230-245. [13] LI W,XIA Y,ZHOU M,et al.Fluctuation-aware and predictive workflow scheduling in cost-effective infrastructure-as-a-service clouds[J].IEEE Access,2018,6:61488-61502. [14] KAUR M,KADAM S.A novel multi-objective bacteria foraging optimization algorithm (MOBFOA) for multi-objective scheduling[J].Applied Soft Computing,2018,66:183-195. [15] ZHANG L,LI K,LI C,et al.Bi-objective workflow scheduling of the energy consumption and reliability in heterogeneous computing systems[J].Information Sciences,2017,379:241-256. [16] CASAS I,TAHERI J,RANJAN R,et al.GA-ETI:An enhanced genetic algorithm for the scheduling of scientific workflows in cloud environments[J].Journal of computational science,2018,26:318-331. [17] VERMA A,KAUSHAL S.A hybrid multi-objective particleswarm optimization for scientific workflow scheduling[J].Parallel Computing,2017,62:1-19. [18] ZHOU X,ZHANG G,SUN J,et al.Minimizing cost and makespan for workflow scheduling in cloud using fuzzy dominance sort based HEFT[J].Future Generation Computer Systems,2019,93:278-289. [19] HABAK K,AMMAR M,HARRAS K A,et al.Femto clouds:Leveraging mobile devices to provide cloud service at the edge[C]//2015 IEEE 8th international conference on cloud computing.IEEE,2015:9-16. [20] MAO Y,ZHANG J,SONG S H,et al.Stochastic Joint Radioand Computational Resource Management for Multi-User Mobile-Edge Computing Systems[J].IEEE transactions on wireless communications,2017,16(9):5994-6009. [21] TONG L,LI Y,GAO W.A hierarchical edge cloud architecture for mobile computing[C]//IEEE INFOCOM 2016-The 35th Annual IEEE International Conference on Computer Communications.IEEE,2016:1-9. [22] ZHANG Y,CHEN X,CHEN Y,et al.Cost efficient scheduling for delay-sensitive tasks in edge computing system[C]//2018 IEEE International Conference on Services Computing (SCC).IEEE,2018:73-80. [23] ZHAO T,ZHOU S,GUO X,et al.Tasks scheduling and re-source allocation in heterogeneous cloud for delay-bounded mobile edge computing[C]//2017 IEEE international conference on communications (ICC).IEEE,2017:1-7. [24] HWANG S Y,HSU C C,LEE C H.Service selection for web services with probabilistic QoS[J].IEEE transactions on services computing,2014,8(3):467-480. [25] PAN Y,WANG S,WU L,et al.A Novel Approach to Scheduling Workflows Upon Cloud Resources with Fluctuating Performance[J].Mobile Networks and Applications,2020:1-11. [26] LI W,XIA Y,ZHOU M,et al.Fluctuation-aware and predictive workflow scheduling in cost-effective infrastructure-as-a-service clouds[J].IEEE Access,2018,6:61488-61502. [27] BERTSEKAS D P.Feature-based aggregation and deep rein-forcement learning:A survey and some new implementations[J].IEEE/CAA Journal of Automatica Sinica,2018,6(1):1-31. [28] MAO H,ALIZADEH M,MENACHE I,et al.Resource management with deep reinforcement learning[C]//Proceedings of the 15th ACM Workshop on Hot Topics in Networks.2016:50-56. [29] XUE L,SUN C,WUNSCH D,et al.An adaptive strategy via reinforcement learning for the prisoner's dilemma game[J].IEEE/CAA Journal of Automatica Sinica,2017,5(1):301-310. [30] ZHAN Y,AMMAR H B.Theoretically-grounded policy advice from multiple teachers in reinforcement learning settings with applications to negative transfer[J].arXiv:1604.03986,2016. [31] WANG H,HUANG T,LIAO X,et al.Reinforcement learning for constrained energy trading games with incomplete information[J].IEEE transactions on cybernetics,2016,47(10):3404-3416. [32] ZHENG L,YANG J,CAI H,et al.Magent:A many-agent reinforcement learning platform for artificial collective intelligence[J].arXiv:1712.00600,2017. [33] LOWE R,WU Y I,TAMAR A,et al.Multi-agent actor-critic for mixed cooperative-competitive environments[C]//Advances in Neural Information Processing Systems.2017:6379-6390. [34] DUAN R,PRODAN R,LI X.Multi-objective game theoreticschedulingof bag-of-tasks workflows on hybrid clouds[J].IEEE Transactions on Cloud Computing,2014,2(1):29-42. [35] CUI D,KE W,PENG Z,et al.Multiple DAGs workflow scheduling algorithm based on reinforcement learning in cloud computing[C]//International Symposium on Computational Intelligence and Intelligent Systems.Springer,Singapore,2015:305-311. [36] IRANPOUR E,SHARIFIAN S.A distributed load balancingand admission control algorithm based on Fuzzy type-2 and Game theory for large-scale SaaS cloud architectures[J].Future Generation Computer Systems,2018,86:81-98. [37] WU J H,PENG Z P,CUI D L,et al.A multi-object optimization cloud workflow scheduling algorithm based on reinforcement learning[C]//International Conference on Intelligent Computing.Springer,Cham,2018:550-559. [38] DONG T,XUE F,XIAO C,et al.Task scheduling based on deep reinforcement learning in a cloud manufacturing environment[J].Concurrency and Computation:Practice and Experience,2020,32(11):e5654. [39] PENG Z,CUI D,ZUO J,et al.Random task scheduling scheme based on reinforcement learning in cloud computing[J].Cluster computing,2015,18(4):1595-1607. [40] CHENG M,LI J,NAZARIAN S.DRL-cloud:Deep reinforce-ment learning-based resource provisioning and task scheduling for cloud service providers[C]//2018 23rd Asia and South Pacific Design Automation Conference (ASP-DAC).IEEE,2018:129-134. [41] WANG Y,LIU H,ZHENG W,et al.Multi-objective workflow scheduling with deep-Q-network-based multi-agent reinforcement learning[J].IEEE Access,2019,7:39974-39982. [42] GUO S,LIU J,YANG Y,et al.Energy-efficient dynamic computation offloading and cooperative task scheduling in mobile cloud computing[J].IEEE Transactions on Mobile Computing,2018,18(2):319-333. [43] MNIH V,KAVUKCUOGLU K,SILVER D,et al.Human-level control through deep reinforcement learning[J].nature,2015,518(7540):529-533. [44] MNIH V,KAVUKCUOGLU K,SILVER D,et al.Playing atari with deep reinforcement learning[J].arXiv:1312.5602,2013. [45] BHARATHI S,CHERVENAK A,DEELMAN E,et al.Characterization of scientific workflows[C]//2008 third workshop on workflows in support of large-scale science.IEEE,2008:1-10. [46] ZHANG G M.Gauss-legendre Multi-Repetition Integral formula and its application[J].Journal of Lanzhou University,2000(5):30-34. [47] Tencent Cloud.Tencent Cloud gradually opens up BAT comprehensively competitive cloud platform[DB/OL].[2013-09-10].http://www.qcloud.com/. [48] Aliyun official website.Aliyun official website[DB/OL].2016-9-12.[2016-9-12].https://www.aliyun.com/minisite/goods?userCode=om2mzele [49] Qi Chacha-Huawei Cloud.Qi Chacha[DB/OL].[2020-07-1].https://www.qcc.com/product/c893fb05-edc2-4f3b-9f2d-ed4e0c60-bbcd.html. [50] AJEENA BEEGOM A S,RAJASREE M S.Non-dominated sorting based PSO algorithm for workflow task scheduling in cloud computing systems[J].Journal of Intelligent & Fuzzy Systems,2019,37(5):6801-6813. [51] MOLLAJAFARI M,SHAHHOSEINI H S.Cost-OptimizedGA-Based Heuristic for Scheduling Time-Constrained Workflow Applications in Infrastructure Clouds Using an Innovative Feasibility-Assured Decoding Mechanism[J].Journal Information Science and Engineering,2016,32(6):1541-1560. |
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