Citation: | LONG Saiqin, WANG Cong, LONG Weifan, et al., “An Efficient Task Scheduling Algorithm in the Cloud and Edge Collaborative Environment,” Chinese Journal of Electronics, in press, doi: 10.23919/cje.2022.00.223, 2022. |
[1] |
P. Lai, Q. He, J. Grundy, et al., “Cost-effective app user allocation in an edge computing environment,” IEEE Transactions on Cloud Computing, vol.10, no.3, pp.1701–1713, 2022. doi: 10.1109/TCC.2020.3001570
|
[2] |
I. M. Ibrahim, S. R. M. Zeebaree, M. A. M. Sadeeq, et al., “Task scheduling algorithms in cloud computing: A review,” Turkish Journal of Computer and Mathematics Education (TURCOMAT), vol.12, no.4, pp.1041–1053, 2021. doi: 10.17762/turcomat.v12i4.612
|
[3] |
F. Y. Hu, L. L. Lv, T. L. Zhang, et al., “Vehicular task scheduling strategy with resource matching computing in cloud-edge collaboration,” IET Collaborative Intelligent Manufacturing, vol.3, no.4, pp.334–344, 2021. doi: 10.1049/cim2.12023
|
[4] |
J. J. Guo, C. L. Li, Y. Chen, et al., “On-demand resource provision based on load estimation and service expenditure in edge cloud environment,” Journal of Network and Computer Applications, vol.151, article no.102506, 2020. doi: 10.1016/j.jnca.2019.102506
|
[5] |
J. Schmitt, J. Bönig, T. Borggräfe, et al., “Predictive model-based quality inspection using machine learning and edge cloud computing,” Advanced Engineering Informatics, vol.45, article no.101101, 2020. doi: 10.1016/j.aei.2020.101101
|
[6] |
P. F. Hu, S. Dhelim, H. S. Ning, et al., “Survey on fog computing: Architecture, key technologies, applications and open issues,” Journal of Network and Computer Applications, vol.98, pp.27–42, 2017. doi: 10.1016/j.jnca.2017.09.002
|
[7] |
S. Z. Bi and Y. J. Zhang, “Computation rate maximization for wireless powered mobile-edge computing with binary computation offloading,” IEEE Transactions on Wireless Communications, vol.17, no.6, pp.4177–4190, 2018. doi: 10.1109/TWC.2018.2821664
|
[8] |
X. F. Cao, G. M. Tang, D. K. Guo, et al., “Edge federation: Towards an integrated service provisioning model,” IEEE/ACM Transactions on Networking, vol.28, no.3, pp.1116–1129, 2020. doi: 10.1109/TNET.2020.2979361
|
[9] |
Y. M. Miao, G. X. Wu, M. Li, et al., “Intelligent task prediction and computation offloading based on mobile-edge cloud computing,” Future Generation Computer Systems, vol.102, pp.925–931, 2020. doi: 10.1016/j.future.2019.09.035
|
[10] |
H. Yuan, G. M. Tang, X. Y. Li, et al., “Online dispatching and fair scheduling of edge computing tasks: A learning-based approach,” IEEE Internet of Things Journal, vol.8, no.19, pp.14985–14998, 2021. doi: 10.1109/JIOT.2021.3073034
|
[11] |
J. H. Zhao, Q. P. Li, Y. Gong, et al., “Computation offloading and resource allocation for cloud assisted mobile edge computing in vehicular networks,” IEEE Transactions on Vehicular Technology, vol.68, no.8, pp.7944–7956, 2019. doi: 10.1109/TVT.2019.2917890
|
[12] |
X. L. Xu, X. Zhang, X. H. Liu, et al., “Adaptive computation offloading with edge for 5g-envisioned internet of connected vehicles,” IEEE Transactions on Intelligent Transportation Systems, vol.22, no.8, pp.5213–5222, 2021. doi: 10.1109/TITS.2020.2982186
|
[13] |
X. Zheng, M. C. Li, and J. Guo, “Task scheduling using edge computing system in smart city,” International Journal of Communication Systems, vol.34, no.6, article no.e4422, 2021. doi: 10.1002/dac.4422
|
[14] |
J. Edinger, M. Breitbach, N. Gabrisch, et al., “Decentralized low-latency task scheduling for Ad-Hoc computing,” in Proceedings of the 2021 IEEE International Parallel and Distributed Processing Symposium (IPDPS), Portland, OR, USA, pp.776–785, 2021.
|
[15] |
Y. X. Sun, S. Zhou, and J. Xu, “EMM: Energy-aware mobility management for mobile edge computing in ultra dense networks,” IEEE Journal on Selected Areas in Communications, vol.35, no.11, pp.2637–2646, 2017. doi: 10.1109/JSAC.2017.2760160
|
[16] |
T. Q. Dinh, Q. D. La, T. Q. S. Quek, et al., “Learning for computation offloading in mobile edge computing,” IEEE Transactions on Communications, vol.66, no.12, pp.6353–6367, 2018. doi: 10.1109/TCOMM.2018.2866572
|
[17] |
X. L. Xu, Y. C. Li, T. Huang, et al., “An energy-aware computation offloading method for smart edge computing in wireless metropolitan area networks,” Journal of Network and Computer Applications, vol.133, pp.75–85, 2019. doi: 10.1016/j.jnca.2019.02.008
|
[18] |
H. L. Zhang, J. Guo, L. C. Yang, et al., “Computation offloading considering fronthaul and backhaul in small-cell networks integrated with MEC,” in Proceedings of the 2017 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), Atlanta, GA, USA, pp.115–120, 2017.
|
[19] |
L. C. Yang, H. L. Zhang, M. Li, et al., “Mobile edge computing empowered energy efficient task offloading in 5G,” IEEE Transactions on Vehicular Technology, vol.67, no.7, pp.6398–6409, 2018. doi: 10.1109/TVT.2018.2799620
|
[20] |
S. Q. Long, W. F. Long, Z. T. Li, et al., “A game-based approach for cost-aware task assignment with QoS constraint in collaborative edge and cloud environments,” IEEE Transactions on Parallel and Distributed Systems, vol.32, no.7, pp.1629–1640, 2021. doi: 10.1109/TPDS.2020.3041029
|
[21] |
H. S. Tan, Z. H. Han, X. Y. Li, et al., “Online job dispatching and scheduling in edge-clouds,” in Proceedings of the IEEE INFOCOM 2017-IEEE Conference on Computer Communications, Atlanta, GA, USA, pp.1–9, 2017.
|
[22] |
X. Long, J. G. Wu, and L. Chen, “Energy-efficient offloading in mobile edge computing with edge-cloud collaboration,” in Proceedings of the 18th International Conference on Algorithms and Architectures for Parallel Processing, Guangzhou, China, pp.460–475, 2018.
|
[23] |
M. Z. Du, Y. Wang, K. J. Ye, et al., “Algorithmics of cost-driven computation offloading in the edge-cloud environment,” IEEE Transactions on Computers, vol.69, no.10, pp.1519–1532, 2020. doi: 10.1109/TC.2020.2976996
|
[24] |
K. Y. Liu, J. Peng, H. Li, et al., “Multi-device task offloading with time-constraints for energy efficiency in mobile cloud computing,” Future Generation Computer Systems, vol.64, pp.1–14, 2016. doi: 10.1016/j.future.2016.04.013
|
[25] |
H. C. Duan, C. Chen, G. Y. Min, et al., “Energy-aware scheduling of virtual machines in heterogeneous cloud computing systems,” Future Generation Computer Systems, vol.74, pp.142–150, 2017. doi: 10.1016/j.future.2016.02.016
|
[26] |
K. K. Gai, M. K. Qiu, and H. Zhao, “Energy-aware task assignment for mobile cyber-enabled applications in heterogeneous cloud computing,” Journal of Parallel and Distributed Computing, vol.111, pp.126–135, 2018. doi: 10.1016/j.jpdc.2017.08.001
|
[27] |
Z. H. Li, X. R. Yu, L. Yu, et al., “Energy-efficient and quality-aware VM consolidation method,” Future Generation Computer Systems, vol.102, pp.789–809, 2020. doi: 10.1016/j.future.2019.08.004
|
[28] |
G. Peng, H. M. Wu, H. Wu, et al., “Constrained multiobjective optimization for IoT-enabled computation offloading in collaborative edge and cloud computing,” IEEE Internet of Things Journal, vol.8, no.17, pp.13723–13736, 2021. doi: 10.1109/JIOT.2021.3067732
|
[29] |
S. D. Wang, Y. Q. Li, S. C. Pang, et al., “A task scheduling strategy in edge-cloud collaborative scenario based on deadline,” Scientific Programming, vol.2020, article no.3967847, 2020. doi: 10.1155/2020/3967847
|
[30] |
C. S. You, K. B. Huang, and H. Chae, “Energy efficient mobile cloud computing powered by wireless energy transfer,” IEEE Journal on Selected Areas in Communications, vol.34, no.5, pp.1757–1771, 2016. doi: 10.1109/JSAC.2016.2545382
|
[31] |
J. K. Ren, G. D. Yu, Y. H. He, et al., “Collaborative cloud and edge computing for latency minimization,” IEEE Transactions on Vehicular Technology, vol.68, no.5, pp.5031–5044, 2019. doi: 10.1109/TVT.2019.2904244
|
[32] |
G. Z. Zhang, T. Q. S. Quek, M. Kountouris, et al., “Fundamentals of heterogeneous backhaul design—analysis and optimization,” IEEE Transactions on Communications, vol.64, no.2, pp.876–889, 2016. doi: 10.1109/TCOMM.2016.2515596
|
[33] |
A. Al-Shuwaili, O. Simeone, A. Bagheri, et al., “Joint uplink/downlink optimization for backhaul-limited mobile cloud computing with user scheduling,” IEEE Transactions on Signal and Information Processing over Networks, vol.3, no.4, pp.787–802, 2017. doi: 10.1109/TSIPN.2017.2668142
|
[34] |
M. Dawande, J. Kalagnanam, P. Keskinocak, et al., “Approximation algorithms for the multiple knapsack problem with assignment restrictions,” Journal of Combinatorial Optimization, vol.4, no.2, pp.171–186, 2000. doi: 10.1023/A:1009894503716
|
[35] |
S. Martello and P. Toth, Knapsack Problems: Algorithms and Computer Implementations. John Wiley & Sons, Inc., New York, NY, USA, 1990.
|