Integrating deep reinforcement learning with pointer networks for service request scheduling in edge computing
With the increasing popularity of edge computing, service providers are more likely to deploy services at the edge of the network to reduce the latency of service requests. However, the resources offered by edge servers are extremely limited compared to those in the cloud. Therefore, a challenging i...
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| Vydáno v: | Knowledge-based systems Ročník 258; s. 109983 |
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| Hlavní autoři: | , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier B.V
22.12.2022
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| Témata: | |
| ISSN: | 0950-7051, 1872-7409 |
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| Abstract | With the increasing popularity of edge computing, service providers are more likely to deploy services at the edge of the network to reduce the latency of service requests. However, the resources offered by edge servers are extremely limited compared to those in the cloud. Therefore, a challenging issue in edge computing is how to sufficiently utilize service resources at the edge to satisfy as many service requests as possible. Existing service request scheduling methods mainly use a single optimization objective, e.g., resource utilization or running time. In this paper, the issue of service request scheduling with multiple requests is modeled as a sequential problem, where multiple optimization objectives, including resource utilization, running time, and waiting time, are involved. A reinforcement learning model with pointer networks is proposed to construct scheduling policies. Experiments conducted on three representative real-world datasets show that our proposed approach outperforms several state-of-the-art methods on the three metrics. |
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| AbstractList | With the increasing popularity of edge computing, service providers are more likely to deploy services at the edge of the network to reduce the latency of service requests. However, the resources offered by edge servers are extremely limited compared to those in the cloud. Therefore, a challenging issue in edge computing is how to sufficiently utilize service resources at the edge to satisfy as many service requests as possible. Existing service request scheduling methods mainly use a single optimization objective, e.g., resource utilization or running time. In this paper, the issue of service request scheduling with multiple requests is modeled as a sequential problem, where multiple optimization objectives, including resource utilization, running time, and waiting time, are involved. A reinforcement learning model with pointer networks is proposed to construct scheduling policies. Experiments conducted on three representative real-world datasets show that our proposed approach outperforms several state-of-the-art methods on the three metrics. |
| ArticleNumber | 109983 |
| Author | Wang, Jian Zhao, Yuqi Jiang, Delun Li, Bing Li, Duantengchuan |
| Author_xml | – sequence: 1 givenname: Yuqi surname: Zhao fullname: Zhao, Yuqi organization: School of Computer Science, Wuhan University, Wuhan, China – sequence: 2 givenname: Bing surname: Li fullname: Li, Bing email: bingli@whu.edu.cn organization: School of Computer Science, Wuhan University, Wuhan, China – sequence: 3 givenname: Jian orcidid: 0000-0002-1559-9314 surname: Wang fullname: Wang, Jian email: jianwang@whu.edu.cn organization: School of Computer Science, Wuhan University, Wuhan, China – sequence: 4 givenname: Delun surname: Jiang fullname: Jiang, Delun organization: School of Computer Science, Wuhan University, Wuhan, China – sequence: 5 givenname: Duantengchuan surname: Li fullname: Li, Duantengchuan organization: School of Computer Science, Wuhan University, Wuhan, China |
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| Cites_doi | 10.1109/TII.2022.3155162 10.1016/j.future.2021.10.013 10.1109/DASC-PICom-CBDCom-CyberSciTech52372.2021.00112 10.32604/csse.2022.024021 10.1109/JIOT.2021.3104015 10.1186/s13677-021-00276-0 10.1007/BF00941281 10.1023/A:1022672621406 10.1007/978-3-030-64243-3_27 10.1109/WCSP49889.2020.9299801 10.1016/j.knosys.2021.107099 10.1109/TPDS.2021.3131680 10.1007/s10489-021-02549-2 10.1016/j.ins.2022.05.053 10.1016/j.ins.2021.11.052 10.1016/j.sysarc.2020.101799 10.1109/INFOCOM.2017.8057116 10.1109/Cybermatics_2018.2018.00109 10.1109/ICWS53863.2021.00078 10.1109/TMC.2020.3017079 10.1016/j.asoc.2022.108731 10.1109/TC.2020.2987567 10.1145/3487552.3487815 10.1016/j.energy.2017.02.174 10.1109/JIOT.2021.3107431 10.1137/1034115 10.1109/JSAC.2021.3118419 10.1016/j.energy.2013.10.034 10.1145/3500911 10.1007/978-3-030-03596-9_15 |
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| Keywords | Deep reinforcement learning Pointer networks Scheduling algorithm Edge computing |
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| References | Stallings (b13) 2011 J. Deng, B. Li, J. Wang, Y. Zhao, Microservice Pre-Deployment Based on Mobility Prediction and Service Composition in Edge, in: 2021 IEEE International Conference on Web Services, ICWS, 2021, pp. 569–578. Tanenbaum (b12) 2009 Goren, Fogel, Halperin (b17) 2022; 27 Deng, Zhang, Zhou, Liu, Zhou, Chen, Zhao (b19) 2022; 585 Li, Shi, Deng, Hu (b18) 2022; 121 Kong, Duan, Hou, Shen, Wang, Yan, Collotta (b22) 2022; 18 Bello, Pham, Le, Norouzi, Bengio (b32) 2017 X. Zhao, X. Guo, Y. Zhang, W. Li, A Parallel-Batch Multi-Objective Job Scheduling Algorithm in Edge Computing, in: IEEE International Conference on Internet of Things (IThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), 2018, pp. 510–516. Zou, Hao, Yu, Jin (b14) 2021; 70 Zhu, Lin, Li, Wang (b21) 2021; 225 Tuli, Ilager, Ramamohanarao, Buyya (b10) 2022; 21 H. Wang, M. Yurochkin, Y. Sun, D.S. Papailiopoulos, Y. Khazaeni, Federated Learning with Matched Averaging, in: 8th International Conference on Learning Representations, ICLR, 2020. O. Vinyals, M. Fortunato, N. Jaitly, Pointer Networks, in: Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems, 2015, pp. 2692–2700. I. Sutskever, O. Vinyals, Q.V. Le, Sequence to Sequence Learning with Neural Networks, in: Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems, 2014, pp. 3104–3112. Hu, Shaloudegi, Zhang, Yu (b26) 2020 L. Zeng, J. Sun, J. Ma, Q. Liu, Task Scheduling Based on Multi-level Hashing and HRRN in cloud computing, in: IEEE Intl Conf on Dependable, Autonomic and Secure Computing, 2021, pp. 667–672. H. Tan, Z. Han, X. Li, F.C.M. Lau, Online job dispatching and scheduling in edge-clouds, in: 2017 IEEE Conference on Computer Communications, INFOCOM, 2017, pp. 1–9. Hansen (b36) 1992; 34 Wang, Wang, Jia, Pang (b6) 2022; 42 Liao, Li, Guo, Kang, Li (b7) 2022; 9 Cai, Wang, Wang, Lyu, Xu, Zheng, Vasilakos (b11) 2022; 40 Cui, Geng, Zhu, Han (b29) 2017; 125 M. Xu, Z. Fu, X. Ma, L. Zhang, Y. Li, F. Qian, S. Wang, K. Li, J. Yang, X. Liu, From cloud to edge: a first look at public edge platforms, in: IMC ’21: ACM Internet Measurement Conference, Virtual Event, USA, 2021, pp. 37–53. Golub, Heath, Wahba (b37) 2007 R. Chen, L. Cui, Y. Zhang, J. Chen, K. Yao, Y. Yang, C. Yao, H. Han, Delay Optimization with FCFS Queuing Model in Mobile Edge Computing-Assisted UAV Swarms: A Game-Theoretic Learning Approach, in: 2020 International Conference on Wireless Communications and Signal Processing (WCSP), 2020, pp. 245–250. Wang, Li, Huang, Wang, Luo (b20) 2022 Zheng, Wan, Zhang, Jiang (b15) 2022; 11 Engl (b38) 1987; 52 Xia, Qiu, Xu, Zhang (b4) 2022; 606 Song, Xing, Wang, Luo, Dai, Li (b23) 2022; 128 Williams (b33) 1992; 8 Yuan, He, Chen, Zhang, Qi, Xu, Xiang, Yang (b1) 2022; 33 P. Lai, Q. He, M. Abdelrazek, F. Chen, J.G. Hosking, J.C. Grundy, Y. Yang, Optimal Edge User Allocation in Edge Computing with Variable Sized Vector Bin Packing, in: C. Pahl, M. Vukovic, J. Yin, Q. Yu (Eds.), Service-Oriented Computing - 16th International Conference, ICSOC, Vol. 11236, 2018, pp. 230–245. Song, Ma, Zhao, Yang, Zhai (b5) 2022; 52 J. Lu, X. Guo, X. Zhao, H. Zhou, A Parallel Tasks Scheduling Algorithm with Markov Decision Process in Edge Computing, in: Green, Pervasive, and Cloud Computing - 15th International Conference, GPC, Vol. 12398, 2020, pp. 362–375. Huang, Li, Bai, Wang, Bai, Wang (b24) 2020 Sun, Yin, Zou, Zhang, Zhang, Zhou (b8) 2020; 108 J. Schaffer, Multiple Objective Optimization with Vector Evaluated Genetic Algorithms, in: G.J.E. Grefensette, J.J.L. Erlbraum (Eds.), Proceedings of the First Int. Conference on Genetic Algortihms, 1985, pp. 93–100. Peng, Wu, Xia, Ma, Wang, Jiang (b3) 2022; 9 Falsafi, Zakariazadeh, Jadid (b28) 2014; 64 Zou (10.1016/j.knosys.2022.109983_b14) 2021; 70 10.1016/j.knosys.2022.109983_b25 10.1016/j.knosys.2022.109983_b27 Golub (10.1016/j.knosys.2022.109983_b37) 2007 Huang (10.1016/j.knosys.2022.109983_b24) 2020 Song (10.1016/j.knosys.2022.109983_b5) 2022; 52 Wang (10.1016/j.knosys.2022.109983_b6) 2022; 42 Cui (10.1016/j.knosys.2022.109983_b29) 2017; 125 Sun (10.1016/j.knosys.2022.109983_b8) 2020; 108 Hu (10.1016/j.knosys.2022.109983_b26) 2020 Li (10.1016/j.knosys.2022.109983_b18) 2022; 121 Tuli (10.1016/j.knosys.2022.109983_b10) 2022; 21 Williams (10.1016/j.knosys.2022.109983_b33) 1992; 8 Cai (10.1016/j.knosys.2022.109983_b11) 2022; 40 Engl (10.1016/j.knosys.2022.109983_b38) 1987; 52 Deng (10.1016/j.knosys.2022.109983_b19) 2022; 585 Falsafi (10.1016/j.knosys.2022.109983_b28) 2014; 64 10.1016/j.knosys.2022.109983_b30 10.1016/j.knosys.2022.109983_b31 Wang (10.1016/j.knosys.2022.109983_b20) 2022 10.1016/j.knosys.2022.109983_b34 Goren (10.1016/j.knosys.2022.109983_b17) 2022; 27 10.1016/j.knosys.2022.109983_b35 10.1016/j.knosys.2022.109983_b9 10.1016/j.knosys.2022.109983_b16 10.1016/j.knosys.2022.109983_b39 Stallings (10.1016/j.knosys.2022.109983_b13) 2011 10.1016/j.knosys.2022.109983_b2 Tanenbaum (10.1016/j.knosys.2022.109983_b12) 2009 Zhu (10.1016/j.knosys.2022.109983_b21) 2021; 225 Yuan (10.1016/j.knosys.2022.109983_b1) 2022; 33 Xia (10.1016/j.knosys.2022.109983_b4) 2022; 606 Kong (10.1016/j.knosys.2022.109983_b22) 2022; 18 Peng (10.1016/j.knosys.2022.109983_b3) 2022; 9 Zheng (10.1016/j.knosys.2022.109983_b15) 2022; 11 Liao (10.1016/j.knosys.2022.109983_b7) 2022; 9 Song (10.1016/j.knosys.2022.109983_b23) 2022; 128 Hansen (10.1016/j.knosys.2022.109983_b36) 1992; 34 Bello (10.1016/j.knosys.2022.109983_b32) 2017 10.1016/j.knosys.2022.109983_b40 10.1016/j.knosys.2022.109983_b41 |
| References_xml | – year: 2020 ident: b26 article-title: FedMGDA+: Federated learning meets multi-objective optimization – reference: J. Schaffer, Multiple Objective Optimization with Vector Evaluated Genetic Algorithms, in: G.J.E. Grefensette, J.J.L. Erlbraum (Eds.), Proceedings of the First Int. Conference on Genetic Algortihms, 1985, pp. 93–100. – volume: 585 start-page: 441 year: 2022 end-page: 453 ident: b19 article-title: An enhanced fast non-dominated solution sorting genetic algorithm for multi-objective problems publication-title: Inform. Sci. – reference: L. Zeng, J. Sun, J. Ma, Q. Liu, Task Scheduling Based on Multi-level Hashing and HRRN in cloud computing, in: IEEE Intl Conf on Dependable, Autonomic and Secure Computing, 2021, pp. 667–672. – volume: 64 start-page: 853 year: 2014 end-page: 867 ident: b28 article-title: The role of demand response in single and multi-objective wind-thermal generation scheduling: A stochastic programming publication-title: Energy – reference: H. Tan, Z. Han, X. Li, F.C.M. Lau, Online job dispatching and scheduling in edge-clouds, in: 2017 IEEE Conference on Computer Communications, INFOCOM, 2017, pp. 1–9. – reference: J. Deng, B. Li, J. Wang, Y. Zhao, Microservice Pre-Deployment Based on Mobility Prediction and Service Composition in Edge, in: 2021 IEEE International Conference on Web Services, ICWS, 2021, pp. 569–578. – reference: H. Wang, M. Yurochkin, Y. Sun, D.S. Papailiopoulos, Y. Khazaeni, Federated Learning with Matched Averaging, in: 8th International Conference on Learning Representations, ICLR, 2020. – volume: 52 start-page: 4028 year: 2022 end-page: 4040 ident: b5 article-title: Cost-efficient multi-service task offloading scheduling for mobile edge computing publication-title: Appl. Intell. – volume: 9 start-page: 4451 year: 2022 end-page: 4463 ident: b7 article-title: Dependency-aware application assigning and scheduling in edge computing publication-title: IEEE Internet Things J. – reference: R. Chen, L. Cui, Y. Zhang, J. Chen, K. Yao, Y. Yang, C. Yao, H. Han, Delay Optimization with FCFS Queuing Model in Mobile Edge Computing-Assisted UAV Swarms: A Game-Theoretic Learning Approach, in: 2020 International Conference on Wireless Communications and Signal Processing (WCSP), 2020, pp. 245–250. – volume: 9 start-page: 4677 year: 2022 end-page: 4692 ident: b3 article-title: DoSRA: A decentralized approach to online edge task scheduling and resource allocation publication-title: IEEE Internet Things J. – volume: 125 start-page: 681 year: 2017 end-page: 704 ident: b29 article-title: Review: Multi-objective optimization methods and application in energy saving publication-title: Energy – volume: 52 start-page: 209 year: 1987 end-page: 215 ident: b38 article-title: Discrepancy principles for Tikhonov regularization of ill-posed problems leading to optimal convergence rates publication-title: J. Optim. Theory Appl. – volume: 70 start-page: 228 year: 2021 end-page: 239 ident: b14 article-title: A3C-DO: A regional resource scheduling framework based on deep reinforcement learning in edge scenario publication-title: IEEE Trans. Comput. – reference: O. Vinyals, M. Fortunato, N. Jaitly, Pointer Networks, in: Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems, 2015, pp. 2692–2700. – volume: 18 start-page: 6308 year: 2022 end-page: 6316 ident: b22 article-title: Deep reinforcement learning-based energy-efficient edge computing for internet of vehicles publication-title: IEEE Trans. Ind. Inform. – reference: X. Zhao, X. Guo, Y. Zhang, W. Li, A Parallel-Batch Multi-Objective Job Scheduling Algorithm in Edge Computing, in: IEEE International Conference on Internet of Things (IThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), 2018, pp. 510–516. – volume: 34 start-page: 561 year: 1992 end-page: 580 ident: b36 article-title: Analysis of discrete ill-posed problems by means of the L-curve publication-title: SIAM Rev. – reference: M. Xu, Z. Fu, X. Ma, L. Zhang, Y. Li, F. Qian, S. Wang, K. Li, J. Yang, X. Liu, From cloud to edge: a first look at public edge platforms, in: IMC ’21: ACM Internet Measurement Conference, Virtual Event, USA, 2021, pp. 37–53. – volume: 40 start-page: 611 year: 2022 end-page: 625 ident: b11 article-title: Dynacomm: Accelerating distributed CNN training between edges and clouds through dynamic communication scheduling publication-title: IEEE J. Sel. Areas Commun. – reference: J. Lu, X. Guo, X. Zhao, H. Zhou, A Parallel Tasks Scheduling Algorithm with Markov Decision Process in Edge Computing, in: Green, Pervasive, and Cloud Computing - 15th International Conference, GPC, Vol. 12398, 2020, pp. 362–375. – start-page: 202 year: 2007 end-page: 212 ident: b37 article-title: Generalized cross-validation as a method for choosing a good ridge parameter publication-title: Milestones in Matrix Computation - Selected Works of Gene H. Golub, with Commentaries – volume: 121 year: 2022 ident: b18 article-title: Pyramid particle swarm optimization with novel strategies of competition and cooperation publication-title: Appl. Soft Comput. – volume: 128 start-page: 333 year: 2022 end-page: 348 ident: b23 article-title: Offloading dependent tasks in multi-access edge computing: A multi-objective reinforcement learning approach publication-title: Future Gener. Comput. Syst. – reference: I. Sutskever, O. Vinyals, Q.V. Le, Sequence to Sequence Learning with Neural Networks, in: Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems, 2014, pp. 3104–3112. – year: 2022 ident: b20 article-title: Smart contract-based caching and data transaction optimization in mobile edge computing publication-title: Knowl.-Based Syst. – reference: P. Lai, Q. He, M. Abdelrazek, F. Chen, J.G. Hosking, J.C. Grundy, Y. Yang, Optimal Edge User Allocation in Edge Computing with Variable Sized Vector Bin Packing, in: C. Pahl, M. Vukovic, J. Yin, Q. Yu (Eds.), Service-Oriented Computing - 16th International Conference, ICSOC, Vol. 11236, 2018, pp. 230–245. – volume: 606 start-page: 38 year: 2022 end-page: 59 ident: b4 article-title: Multi-objective workflow scheduling based on genetic algorithm in cloud environment publication-title: Inform. Sci. – volume: 27 start-page: 2.3:1 year: 2022 end-page: 2.3:17 ident: b17 article-title: Area optimal polygonization using simulated annealing publication-title: ACM J. Exp. Algorithmics – year: 2009 ident: b12 article-title: Modern Operating Systems – year: 2017 ident: b32 article-title: Neural combinatorial optimization with reinforcement learning – volume: 21 start-page: 940 year: 2022 end-page: 954 ident: b10 article-title: Dynamic scheduling for stochastic edge-cloud computing environments using A3C learning and residual recurrent neural networks publication-title: IEEE Trans. Mob. Comput. – year: 2011 ident: b13 article-title: Operating Systems - Internals and Design Principles – volume: 11 start-page: 3 year: 2022 ident: b15 article-title: Deep reinforcement learning-based workload scheduling for edge computing publication-title: J. Cloud Comput. – year: 2020 ident: b24 article-title: A federated multi-view deep learning framework for privacy-preserving recommendations – volume: 42 start-page: 1241 year: 2022 end-page: 1255 ident: b6 article-title: Flexible task scheduling based on edge computing and cloud collaboration publication-title: Comput. Syst. Sci. Eng. – volume: 33 start-page: 1873 year: 2022 end-page: 1887 ident: b1 article-title: CSEdge: Enabling collaborative edge storage for multi-access edge computing based on blockchain publication-title: IEEE Trans. Parallel Distrib. Syst. – volume: 225 year: 2021 ident: b21 article-title: A decomposition-based multi-objective genetic programming hyper-heuristic approach for the multi-skill resource constrained project scheduling problem publication-title: Knowl.-Based Syst. – volume: 108 year: 2020 ident: b8 article-title: Makespan-minimization workflow scheduling for complex networks with social groups in edge computing publication-title: J. Syst. Archit. – volume: 8 start-page: 229 year: 1992 end-page: 256 ident: b33 article-title: Simple statistical gradient-following algorithms for connectionist reinforcement learning publication-title: Mach. Learn. – volume: 18 start-page: 6308 issue: 9 year: 2022 ident: 10.1016/j.knosys.2022.109983_b22 article-title: Deep reinforcement learning-based energy-efficient edge computing for internet of vehicles publication-title: IEEE Trans. Ind. Inform. doi: 10.1109/TII.2022.3155162 – volume: 128 start-page: 333 year: 2022 ident: 10.1016/j.knosys.2022.109983_b23 article-title: Offloading dependent tasks in multi-access edge computing: A multi-objective reinforcement learning approach publication-title: Future Gener. Comput. Syst. doi: 10.1016/j.future.2021.10.013 – ident: 10.1016/j.knosys.2022.109983_b40 doi: 10.1109/DASC-PICom-CBDCom-CyberSciTech52372.2021.00112 – volume: 42 start-page: 1241 issue: 3 year: 2022 ident: 10.1016/j.knosys.2022.109983_b6 article-title: Flexible task scheduling based on edge computing and cloud collaboration publication-title: Comput. Syst. Sci. Eng. doi: 10.32604/csse.2022.024021 – volume: 9 start-page: 4451 issue: 6 year: 2022 ident: 10.1016/j.knosys.2022.109983_b7 article-title: Dependency-aware application assigning and scheduling in edge computing publication-title: IEEE Internet Things J. doi: 10.1109/JIOT.2021.3104015 – year: 2020 ident: 10.1016/j.knosys.2022.109983_b24 – volume: 11 start-page: 3 year: 2022 ident: 10.1016/j.knosys.2022.109983_b15 article-title: Deep reinforcement learning-based workload scheduling for edge computing publication-title: J. Cloud Comput. doi: 10.1186/s13677-021-00276-0 – ident: 10.1016/j.knosys.2022.109983_b16 – volume: 52 start-page: 209 issue: 2 year: 1987 ident: 10.1016/j.knosys.2022.109983_b38 article-title: Discrepancy principles for Tikhonov regularization of ill-posed problems leading to optimal convergence rates publication-title: J. Optim. Theory Appl. doi: 10.1007/BF00941281 – ident: 10.1016/j.knosys.2022.109983_b31 – volume: 8 start-page: 229 year: 1992 ident: 10.1016/j.knosys.2022.109983_b33 article-title: Simple statistical gradient-following algorithms for connectionist reinforcement learning publication-title: Mach. Learn. doi: 10.1023/A:1022672621406 – ident: 10.1016/j.knosys.2022.109983_b34 doi: 10.1007/978-3-030-64243-3_27 – ident: 10.1016/j.knosys.2022.109983_b39 doi: 10.1109/WCSP49889.2020.9299801 – volume: 225 year: 2021 ident: 10.1016/j.knosys.2022.109983_b21 article-title: A decomposition-based multi-objective genetic programming hyper-heuristic approach for the multi-skill resource constrained project scheduling problem publication-title: Knowl.-Based Syst. doi: 10.1016/j.knosys.2021.107099 – volume: 33 start-page: 1873 issue: 8 year: 2022 ident: 10.1016/j.knosys.2022.109983_b1 article-title: CSEdge: Enabling collaborative edge storage for multi-access edge computing based on blockchain publication-title: IEEE Trans. Parallel Distrib. Syst. doi: 10.1109/TPDS.2021.3131680 – volume: 52 start-page: 4028 issue: 4 year: 2022 ident: 10.1016/j.knosys.2022.109983_b5 article-title: Cost-efficient multi-service task offloading scheduling for mobile edge computing publication-title: Appl. Intell. doi: 10.1007/s10489-021-02549-2 – year: 2022 ident: 10.1016/j.knosys.2022.109983_b20 article-title: Smart contract-based caching and data transaction optimization in mobile edge computing publication-title: Knowl.-Based Syst. – volume: 606 start-page: 38 year: 2022 ident: 10.1016/j.knosys.2022.109983_b4 article-title: Multi-objective workflow scheduling based on genetic algorithm in cloud environment publication-title: Inform. Sci. doi: 10.1016/j.ins.2022.05.053 – volume: 585 start-page: 441 year: 2022 ident: 10.1016/j.knosys.2022.109983_b19 article-title: An enhanced fast non-dominated solution sorting genetic algorithm for multi-objective problems publication-title: Inform. Sci. doi: 10.1016/j.ins.2021.11.052 – volume: 108 year: 2020 ident: 10.1016/j.knosys.2022.109983_b8 article-title: Makespan-minimization workflow scheduling for complex networks with social groups in edge computing publication-title: J. Syst. Archit. doi: 10.1016/j.sysarc.2020.101799 – ident: 10.1016/j.knosys.2022.109983_b41 doi: 10.1109/INFOCOM.2017.8057116 – year: 2017 ident: 10.1016/j.knosys.2022.109983_b32 – ident: 10.1016/j.knosys.2022.109983_b9 doi: 10.1109/Cybermatics_2018.2018.00109 – year: 2009 ident: 10.1016/j.knosys.2022.109983_b12 – ident: 10.1016/j.knosys.2022.109983_b2 doi: 10.1109/ICWS53863.2021.00078 – ident: 10.1016/j.knosys.2022.109983_b25 – volume: 21 start-page: 940 issue: 3 year: 2022 ident: 10.1016/j.knosys.2022.109983_b10 article-title: Dynamic scheduling for stochastic edge-cloud computing environments using A3C learning and residual recurrent neural networks publication-title: IEEE Trans. Mob. Comput. doi: 10.1109/TMC.2020.3017079 – volume: 121 year: 2022 ident: 10.1016/j.knosys.2022.109983_b18 article-title: Pyramid particle swarm optimization with novel strategies of competition and cooperation publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2022.108731 – year: 2020 ident: 10.1016/j.knosys.2022.109983_b26 – ident: 10.1016/j.knosys.2022.109983_b30 – volume: 70 start-page: 228 issue: 2 year: 2021 ident: 10.1016/j.knosys.2022.109983_b14 article-title: A3C-DO: A regional resource scheduling framework based on deep reinforcement learning in edge scenario publication-title: IEEE Trans. Comput. doi: 10.1109/TC.2020.2987567 – ident: 10.1016/j.knosys.2022.109983_b35 doi: 10.1145/3487552.3487815 – volume: 125 start-page: 681 year: 2017 ident: 10.1016/j.knosys.2022.109983_b29 article-title: Review: Multi-objective optimization methods and application in energy saving publication-title: Energy doi: 10.1016/j.energy.2017.02.174 – volume: 9 start-page: 4677 issue: 6 year: 2022 ident: 10.1016/j.knosys.2022.109983_b3 article-title: DoSRA: A decentralized approach to online edge task scheduling and resource allocation publication-title: IEEE Internet Things J. doi: 10.1109/JIOT.2021.3107431 – start-page: 202 year: 2007 ident: 10.1016/j.knosys.2022.109983_b37 article-title: Generalized cross-validation as a method for choosing a good ridge parameter – volume: 34 start-page: 561 issue: 4 year: 1992 ident: 10.1016/j.knosys.2022.109983_b36 article-title: Analysis of discrete ill-posed problems by means of the L-curve publication-title: SIAM Rev. doi: 10.1137/1034115 – volume: 40 start-page: 611 issue: 2 year: 2022 ident: 10.1016/j.knosys.2022.109983_b11 article-title: Dynacomm: Accelerating distributed CNN training between edges and clouds through dynamic communication scheduling publication-title: IEEE J. Sel. Areas Commun. doi: 10.1109/JSAC.2021.3118419 – volume: 64 start-page: 853 year: 2014 ident: 10.1016/j.knosys.2022.109983_b28 article-title: The role of demand response in single and multi-objective wind-thermal generation scheduling: A stochastic programming publication-title: Energy doi: 10.1016/j.energy.2013.10.034 – year: 2011 ident: 10.1016/j.knosys.2022.109983_b13 – volume: 27 start-page: 2.3:1 year: 2022 ident: 10.1016/j.knosys.2022.109983_b17 article-title: Area optimal polygonization using simulated annealing publication-title: ACM J. Exp. Algorithmics doi: 10.1145/3500911 – ident: 10.1016/j.knosys.2022.109983_b27 doi: 10.1007/978-3-030-03596-9_15 |
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