Advertisement

Hierarchical resource scheduling method using improved cuckoo search algorithm for internet of things

  • Chunguang ZhangEmail author
  • Guangping Zeng
  • Hongbo Wang
  • Xuyan Tu
Article
  • 3 Downloads
Part of the following topical collections:
  1. Special Issue on Fog/Edge Networking for Multimedia Applications

Abstract

Current researches for Internet of Things (IoT) QoS mainly focuses on the formulation of service level protocols, which improves some performance of resource scheduling, but there are still many shortcomings in resolving the real-time and personalized requirements of IoT. Aiming at the hierarchical resource scheduling algorithm of the IoT, the key issues of hierarchical resource scheduling are analyzed in detail. The hierarchical resource scheduling of the Internet of Things based on improved heuristic algorithm is deeply studied and explored. A cuckoo search algorithm based on adaptive Cauchy mutation is proposed. Because the algorithm is prone to premature, easy to fall into the local optimal solution, and unable to find the global optimal solution, by introducing mutation operator, the improved algorithm has a certain ability of local random search, while accelerating convergence to the optimal solution in the later period, maintaining the diversity of solutions. The simulation results show that the average service success rate of the proposed resource scheduling algorithm is close to 99%, which can effectively guarantee the relative fairness of user requests, meet the real-time and personalized needs of different users, and improve the utilization rate of resources.

Keywords

Hierarchical scheduling Internet of things Resource scheduling Improved cuckoo search algorithm Global optimal solution Mutation operator 

Notes

Acknowledgements

This work was supported by the Natural Science Foundation of China (No. 61572074).

References

  1. 1.
    Boucetta C, Idoudi H, Saidane L A. (2016) Hierarchical cuckoo search-based routing in wireless sensor networks[C]// Computers & Communication.Google Scholar
  2. 2.
    Hui L, Zhou Q, Fei Z et al (2018) Scheduling based on interruption analysis and PSO for strictly periodic and preemptive partitions in integrated modular avionics [J]. IEEE Access 6(99):13523–13540Google Scholar
  3. 3.
    Jasper V (2018) Walraevens Joris, Maertens tom, et al. calculation of the performance region of an easy-to-optimize alternative for generalized processor sharing[J]. Eur J Oper Res 270(2):625–635Google Scholar
  4. 4.
    Zhu C, Hui Z, Han G, et al. (2015) BTDGS: binary-tree based data gathering scheme with Mobile sink for wireless multimedia sensor networks [J]. Mobile Networks & Applications, 20(5):604–622Google Scholar
  5. 5.
    Zhao Y, Zeng H (2017) An efficient schedulability analysis for optimizing systems with adaptive mixed-criticality scheduling [J]. Real-Time Systems 53(4):1–59zbMATHGoogle Scholar
  6. 6.
    Olaniyan R , Maheswaran M (2018) Synchronous scheduling algorithms for edge coordinated internet of things[C]// 2018 IEEE 2nd international conference on fog and edge computing (ICFEC). IEEE.Google Scholar
  7. 7.
    Yue C, Long M, Wang J, et al. (2016) Correlation autoencoder hashing for supervised cross-modal search[C]// Acm on International Conference on Multimedia Retrieval.Google Scholar
  8. 8.
    Carlos Mera-Gomez, Francisco Ramirez, Rami Bahsoon, et al. (2018) A multi-agent elasticity management based on multi-tenant debt exchanges[C]// 2018 IEEE 12th International Conference on Self-Adaptive and Self-Organizing Systems (SASO).Google Scholar
  9. 9.
    Tan S, Hu L, Wang-Xu A et al (2016) Kernelized cross-modal hashing for multimedia retrieval[C]//. Intell Control AutomGoogle Scholar
  10. 10.
    Peng Y, Qi J, Xin H et al (2018) CCL: cross-modal correlation learning with multigrained fusion by hierarchical network[J]. IEEE Trans Multimedia 20(2):405–420Google Scholar
  11. 11.
    Nazir S, Shafiq S, Iqbal Z, et al. (2018) Cuckoo optimization algorithm based job scheduling using cloud and fog computing in smart grid[C]// the 10th International Conference on Intelligent Networking and Collaborative Systems (INCoS-2018).Google Scholar
  12. 12.
    Zhou F, Luo M, Tong Y, et al. (2017) Mitigating multi-tenant interference on mobile offloading servers: poster abstract[C]// Symposium.Google Scholar
  13. 13.
    Xing X, Shimada A, Taniguchi R I, et al. (2015) Coupled dictionary learning and feature mapping for cross-modal retrieval[C]// IEEE International Conference on Multimedia & Expo.Google Scholar
  14. 14.
    Chen W C, Chen C W, Hu M C. (2018) Syncgan: synchronize the latent spaces of cross-modal generative adversarial networks[C]// 2018 IEEE International Conference on Multimedia and Expo (ICME).Google Scholar
  15. 15.
    Nishimaki J, Hosokawa T, Fujiwara H. (2016) A scheduling method for hierarchical testability based on test environment generation results[C]// Workshop on design automation for understanding hardware designs.Google Scholar
  16. 16.
    Liang X, Peng P, Lu Y, et al. (2015) Cross-modal self-taught learning for image retrieval[C]// International Conference on Multimedia Modeling.Google Scholar
  17. 17.
    El-Ela AAA, El-Sehiemy RA, Abbas AS (2017) Optimal Placement and Sizing of Distributed Generation and Capacitor Banks in Distribution Systems Using Water Cycle Algorithm[J]. IEEE Syst J (99):1–8Google Scholar
  18. 18.
    Cao G, Iosifidis A, Member S et al (2018) Generalized multi-view embedding for visual recognition and cross-modal retrieval[J]. IEEE Transactions on Cybernetics 48(9):2542–2555Google Scholar
  19. 19.
    Tang J, Wang K, Shao L (2016) Supervised matrix factorization hashing for cross-modal retrieval[J]. IEEE Trans Image Process 25(7):3157–3166MathSciNetzbMATHGoogle Scholar
  20. 20.
    Wu Y, Wang S, Zhang W, et al. (2017) Online low-rank similarity function learning with adaptive relative margin for cross-modal retrieval[C]// IEEE International Conference on Multimedia & Expo.Google Scholar
  21. 21.
    Fei H, Cheng J, Zhang Y, et al. (2017) Towards sketch-based image retrieval with deep cross-modal correlation learning[C]// IEEE International Conference on Multimedia & Expo.Google Scholar
  22. 22.
    Haibo YU, Chao Z, Zuqiang D et al (2018) Economic optimization for configuration and sizing of micro integrated energy systems[J]. Journal of Modern Power Systems and Clean Energy 6(02):144–155Google Scholar
  23. 23.
    Wang J, Li G, Peng P et al (2017) Semi-supervised semantic factorization hashing for fast cross-modal retrieval[J]. Multimed Tools Appl 76(3):1–19Google Scholar
  24. 24.
    Yao L, Yuan Y, Huang Q, et al. (2016) Hashing for cross-modal similarity retrieval[C]// International Conference on Semantics.Google Scholar
  25. 25.
    Tan S, Hu L, Wang-Xu A, et al. (2016) Kernelized cross-modal hashing for multimedia retrieval[C]// Intelligent Control & Automation.Google Scholar
  26. 26.
    Yoosefi A, Naji HR. (2016) A hierarchical cluster-based model with run-time reconfigurable resource allocation on FPGAs[C]// International Conference on Vlsi Systems.Google Scholar
  27. 27.
    Khamis S, Fanello S, Rhemann C, et al. (2018) StereoNet: guided hierarchical refinement for real-time edge-aware depth prediction[C]// Europen Conference on Computer Vision (ECCV).Google Scholar
  28. 28.
    Gasmi K, Rebaya A, Amari I, et al. (2016) Workflow for multi-core architecture: from MATLAB/Simulink models to hardware mapping/scheduling[C]// International Conference on Sciences of Electronics.Google Scholar
  29. 29.
    George N, Chandrasekaran K, Binu A. (2016) Optimization-aware scheduling in cloud computing[C]// International Conference on Informatics & Analytics.Google Scholar
  30. 30.
    Hascoët J, Desnos K, Nezan J F, et al. (2017) Hierarchical dataflow model for efficient programming of clustered manycore processors[C]// IEEE International Conference on Application-specific systems.Google Scholar
  31. 31.
    Guo L, Chen H, Liu Q et al (2018) A computationally efficient and hierarchical control strategy for velocity optimization of on-road vehicles[J]. IEEE Trans Syst Man Cybern Syst 49(1):1–11Google Scholar
  32. 32.
    Sovannarith H, Chakchai S-I, Gia NT (2017) Distributed image compression architecture over wireless multimedia sensor networks[J]. Wirel Commun Mob Comput 2017(3):1–21Google Scholar
  33. 33.
    Pendleton M, Sebra R, Pang AWC, Ummat A, Franzen O, Rausch T, Stütz AM, Stedman W, Anantharaman T, Hastie A, Dai H, Fritz MHY, Cao H, Cohain A, Deikus G, Durrett RE, Blanchard SC, Altman R, Chin CS, Guo Y, Paxinos EE, Korbel JO, Darnell RB, McCombie WR, Kwok PY, Mason CE, Schadt EE, Bashir A (2015) Assembly and diploid architecture of an individual human genome via single-molecule technologies[J]. Nat Methods 12(8):780–786Google Scholar
  34. 34.
    Zhu C, Hui Z, Han G et al (2015) BTDGS: binary-tree based data gathering scheme with Mobile sink for wireless multimedia sensor networks[J]. Mobile Networks & Applications 20(5):604–622Google Scholar
  35. 35.
    Li Z, Zang C, Zeng P et al (2018) Fully distributed hierarchical control of parallel grid-supporting inverters in islanded AC microgrids[J]. IEEE Trans Ind Inf 14(2):679–690Google Scholar
  36. 36.
    Realy J, Sáezz S, Crespoy A (2016) Combined Scheduling of Time-Triggered Plans and Priority Scheduled Task Sets[J]. ACM SIGAda Ada Letters 36(1):68–76Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Chunguang Zhang
    • 1
    Email author
  • Guangping Zeng
    • 1
  • Hongbo Wang
    • 1
  • Xuyan Tu
    • 1
  1. 1.School of Computing & Communication EngineeringUniversity of Technology Science BeijingBeijingChina

Personalised recommendations