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Resource scheduling for delay-sensitive application in three-layer fog-to-cloud architecture

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Abstract

In order to solve the problem of resource constraints of mobile devices, fog computing is proposed to improve WAN delay for delay-sensitive and resource-intensive applications. To utilize the resource of fog-to-cloud efficiently and provide good quality-of-service in terms of delay and service failure probability, an improved three-layer fog-to-cloud architecture and schedule fit algorithm are proposed. Three-layer fog-to-cloud architecture can provide computing resource and transmission delay according to the delay sensitivity of applications, which uses computing resource of fog-to-cloud efficiently and improves service delay. Simulation results validate the effectiveness and efficiency of the proposed schedule fit algorithm and show that schedule fit algorithm outperforms the existing algorithms, i.e., the conventional cloud schedule algorithm, random schedule algorithm and first fit algorithm.

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References

  1. Zhang Y (2018) Resource scheduling and delay analysis for workflow in wireless small cloud. IEEE Trans Mob Comput 17(3):675–687

    Article  Google Scholar 

  2. Satyanarayanan M, Bahl P, Caceres R (2009) The case for VM-based cloudlets in mobile computing. IEEE Pervasive Comput. 8(4):14–23

    Article  Google Scholar 

  3. Bilal K, Khalid O, Erbad A, Khan SU (2018) Potentials, trends, and prospects in edge technologies: fog, cloudlet, mobile edge, and micro data centers. Comput Netw 130:94–120

    Article  Google Scholar 

  4. Velasquez K, Abreu DP, Assis MRM, Senna C, Aranha DF, Bittencourt LF, Laranjeiro N, Curado M, Vieira M, Monteiro E, Madeira E (2018) Fog orchestration for the Internet of everything: state-of-the-art and research challenges. J Internet Services Appl 9(14)

  5. Ahmed E, Chatzimisios P, Gupta BB, Jaraweh Y, Song HB (2018) Recent advances in fog and mobile edge computing Trans Emerg Telecommun Technol 29(7)

  6. Lu T, Chang S, Li W (2018) Fog computing enabling geographic routing for urban area vehicular network. Peer Peer Netw Appl 11(4):749–755

    Article  Google Scholar 

  7. Du JB, Zhao LQ, Feng J, Chu XL (2018) Computation offloading and resource allocation in mixed fog/cloud computing systems with min-max fairness guarantee. IEEE Trans Netw Serv Manag 66(4):1594–1608

    Google Scholar 

  8. Li H, Ota K, Dong M (2018) ECCN: Orchestration of edge-centric computing and content-centric networking in the 5G radio access network. IEEE Wirel Commun 25(3):88–93

    Article  Google Scholar 

  9. Xu J, Ota K, Dong M (2018) Saving energy on the edge: in-memory caching for multi-tier heterogeneous networks. IEEE Commun Mag 56(5):102–107

    Article  Google Scholar 

  10. Josilo S, Dan G (2019) Decentralized algorithm for randomized task allocation in fog computing systems. IEEE-ACM Trans Netw 27(1):85–97

    Article  Google Scholar 

  11. Wu J, Dong M, Ota K, Li J, Yang W, Wang M (2019) Fog-Computing-Enabled Cognitive network function virtualization for an Information-Centric future internet. IEEE Commun Mag 57(7):48–54

    Article  Google Scholar 

  12. Masip-Bruin X, Marin-Tordera E, Tashakor G, Jukan A, Ren GJ (2016) Foggy clouds and cloudy fogs: a real need for coordinated management of fog-to-cloud computing systems. IEEE Wirel Commun Lett 23 (5):120–128

    Article  Google Scholar 

  13. Wang TX, Wei XL, Tang CG, Fan JH (2018) Efficient multi-tasks scheduling algorithm in mobile cloud computing with time constraints. Peer Peer Netw Appl. 11(4):793–807

    Article  Google Scholar 

  14. Bittencourt LF, Diaz-Montes J, Buyya R, Rana OF, Parashar M (2017) Mobility-aware application scheduling in fog computing. IEEE Cloud Comput 4(2):26–35

    Article  Google Scholar 

  15. Liu Y, Yu FR, Li X, Ji H, Leung VCM (2018) Hybrid computation offloading in fog and cloud networks with non-orthogonal multiple access. In: INFOCOM 2018, Honolulu, USA

  16. Li H, Ota K, Dong M (2019) Deep reinforcement scheduling for mobile crowdsensing in fog computing, ACM Trans Internet Technol 19(2)

  17. Zhou Z, Dong M, Ota K, Wang G, Yang L (2016) Energy-efficient resource allocation for D2D communications underlaying cloud-RAN-based LTE-a networks. IEEE Internet Things J 3(3):428–438

    Article  Google Scholar 

  18. Liu Y, Lee MJ, Zheng Y (2016) Adaptive multi-resource allocation for cloudlet-based mobile cloud computing system. IEEE Trans Mob Comput 15(10):2398–2410

    Article  Google Scholar 

  19. Rodrigues TG, Suto K, Nishiyama H, Kato N (2017) Hybrid method for minimizing service delay in edge cloud computing through VM migration and transmission power control. IEEE Trans Comput 66(5):810–819

    Article  MathSciNet  Google Scholar 

  20. Jia MK, Cao JN, Liang WF (2017) Optimal cloudlet placement and user to cloudlet allocation in wireless metropolitan area networks. IEEE Trans Cloud Comput 5(4):725–737

    Article  Google Scholar 

  21. Tiwary M, Puthal D, Sahoo KS, Sahoo B, Yang LT (2018) Response time optimization for cloudlets in mobile edge computing. J Parallel Distrib Comput 119:81–91

    Article  Google Scholar 

  22. Fan Q, Ansari N (2018) Workload allocation in hierarchical cloudlet networks. IEEE Commun Lett 22 (4):820–823

    Article  Google Scholar 

  23. Sonmez C, Ozgovde A, Ersoy C (2017) Performance evaluation of single-tier and two-tier cloudlet assisted applications. In: ICC Workshops, Paris, France

  24. Souza VB, Masip-Bruin X, Marin-Tordera E, Sanchez-Lopez S, Garcia J, Ren GJ, Jukan A, Ferrer AJ (2018) Towards a proper service placement in combined fog-to-cloud (F2C) architectures. Futur Gener Comp Syst 87:1–15

    Article  Google Scholar 

  25. Moreno-Vozmediano R, Montero RS, Huedo E, Llorente IM (2017) Cross-site virtual network in cloud and fog computing. IEEE Trans Mob Comput 4(2):46–53

    Google Scholar 

  26. Peng MG, Yan S, Zhang KC, Wang CG (2016) Fog-computing-based radio access networks: issues and challenges. IEEE Netw 30(4):46–53

    Article  Google Scholar 

  27. Dutta J, Roy S (2017) Iot-fog-cloud based architecture for smart city: prototype of a smart building. In: Confluence 2017, Noida, India

  28. Pham XQ, Huh EN (2016) Towards task scheduling in a cloud-fog computing system. In: APNOMS, Kanazawa, Japan

  29. Deng RL, Lu RX, Lai CZ, Luan TH, Liang H (2016) Optimal workload allocation in fog-cloud computing toward balanced delay and power consumption. IEEE Internet Things J 3(6):1171–1181

    Google Scholar 

  30. Ramirez W, Masip-Bruin X, Marin-Tordera E, Souza VBC, Jukan A, Ren GJ, Gonzalez de Dios O (2017) Optimal workload allocation in fog-cloud computing toward balanced delay and power consumption. IEEE Internet Things J 113:43–52

    Google Scholar 

  31. Rodrigues TG, Suto K, Nishiyama H, Kato N, Temma K (2018) Cloudlets activation scheme for scalable mobile edge computing with transmission power control and virtual machine migration. IEEE Trans Comput 67(9):1287–1300

    Article  MathSciNet  Google Scholar 

  32. Chang S, Zhu H, Dong M, Ota K, Liu X, Shen X (2016) Private and flexible urban message delivery. IEEE Trans Veh Technol 65(7):4900–4910

    Article  Google Scholar 

  33. Melbourne Clouds LAB Cloudsim: a framework for modeling and simulation of cloud computing infrastructures and services. www.cloudbus.org/cloudsim

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Authors and Affiliations

  1. Donghua University, 2999 Renmin Road, Songjiang District, 201620, Shanghai, People’s Republic of China

    Zibin Ren, Ting Lu, Wenjing Guo, Guohua Liu & Shan Chang

  2. Department of Electrical and Computer Engineering, State University of New York at Stony Brook, New York, USA

    Xin Wang

Authors
  1. Zibin Ren
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  2. Ting Lu
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  3. Xin Wang
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  4. Wenjing Guo
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  5. Guohua Liu
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  6. Shan Chang
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Correspondence to Ting Lu.

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This work is supported by National Natural Science Foundation of China (Grant No. 61402101, 61672151), Shanghai Municipal Natural Science Foundation (Grant No. 18ZR1401200).

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Ren, Z., Lu, T., Wang, X. et al. Resource scheduling for delay-sensitive application in three-layer fog-to-cloud architecture. Peer-to-Peer Netw. Appl. 13, 1474–1485 (2020). https://doi.org/10.1007/s12083-020-00900-x

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  • DOI: https://doi.org/10.1007/s12083-020-00900-x

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