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Energy Conservation Solutions for Fog-Edge Computing Paradigms pp 21–47Cite as

DoSP: A Deadline-Aware Dynamic Service Placement Algorithm for Workflow-Oriented IoT Applications in Fog-Cloud Computing Environments

Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT,volume 74)

Abstract

The next generation Internet of Things (IoT) applications are offering multiple services and run in a distributed heterogeneous environment. In such applications, Quality of Service (QoS) requirements are in jeopardy when the computing operations are only outsourced to the public cloud. For IoT applications, a comprehensive framework that supports QoS-aware service placement in a fog computing environment is highly required. It is a challenging task to orchestrate the time critical IoT applications in the fog environment. To alleviate this problem, this paper proposes a novel multitier fog computing architecture called Deadline-oriented Service Placement (DoSP) that provides the services both in fog and cloud nodes. This research work proposed a methodology to utilize low-cost fog resources while ensuring that the response time satisfies a given time constraint. It uses the Genetic Algorithm (GA) to dynamically determine the service placement in the fog environment. In this work, we used the iFogSim simulator to model DoSP and measured the impact of the service placement technique in terms of service deadline. It has been observed that through the proposed solution, there is a reduction in service execution delay, i.e., approximately 10.19% of the overall response time to the EdgeWard and 2.58% to the Cloud Only.

Keywords

  • Fog computing
  • IoT
  • Cloud computing
  • Edge computing
  • Workflow

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References

  1. Kochovski P, Stankovski V (2018) Supporting smart construction with dependable edge computing infrastructures and applications. Autom Constr 85:182–192

    CrossRef  Google Scholar 

  2. Mahmud R, Ramamohanarao K, Buyya R (2018) Latency-aware application module management for fog computing environments. ACM Trans Internet Technol (TOIT) 19(1):1–21

    CrossRef  Google Scholar 

  3. Pham X-Q, Man ND, Tri NDT, Thai NQ, Huh E-N (2017) A cost-and performance-effective approach for task scheduling based on collaboration between cloud and fog computing. Int J Distrib Sens Netw 13(11):1550147717742073

    CrossRef  Google Scholar 

  4. Skarlat O, Nardelli M, Schulte S, Dustdar S (2017) Towards QoS-aware fog service placement. In: 1st IEEE international conference on fog and edge computing (ICFEC) IEEE, Madrid, Spain, pp 89–96

    Google Scholar 

  5. Wu H, Knottenbelt WJ, Wolter K (2019) An efficient application partitioning algorithm in mobile environments. IEEE Trans Parallel Distrib Syst 30(7):1464–1480

    CrossRef  Google Scholar 

  6. Skarlat O, Schulte S, Borkowski M, Leitner P (2016) Resource provisioning for IoT services in the fog. In: 2016 IEEE 9th international conference on service-oriented computing and applications (SOCA). IEEE, pp 32–39

    Google Scholar 

  7. Aazam M, St-Hilaire M, Lung C-H, Lambadaris I (2016) MeFoRE: QoE based resource estimation at fog to enhance QoS in IoT. In: 2016 23rd International conference on telecommunications (ICT). IEEE, pp 1–5

    Google Scholar 

  8. Agarwal S, Yadav S, Yadav AK (2016) An efficient architecture and algorithm for resource provisioning in fog computing. Int J Inf Eng Electron Bus 8(1):48

    Google Scholar 

  9. Verma S, Yadav AK, Motwani D, Raw R, Singh HK (2016) An efficient data replication and load balancing technique for fog computing environment. In: 2016 3rd International conference on computing for sustainable global development (INDIACom). IEEE, pp 2888–2895

    Google Scholar 

  10. Shekhar S, Gokhale A (2017) Dynamic resource management across cloud-edge resources for performance-sensitive applications. In: 2017 17th IEEE/ACM international symposium on cluster, cloud and grid computing (CCGRID). IEEE, pp 707–710

    Google Scholar 

  11. Filip ID, Pop F, Serbanescu C, Choi C (2018) Micro services scheduling model over heterogeneous cloud-edge environments as support for IoT applications. IEEE Internet Things J 5(4):2672–2681

    CrossRef  Google Scholar 

  12. Taneja M, Davy A (2017) Resource aware placement of IoT application modules in fog-cloud computing paradigm. In 2017 IFIP/IEEE symposium on integrated network and service management (IM). IEEE, pp 1222–1228

    Google Scholar 

  13. Souza VBC, Ramírez W, Masip-Bruin X, Marín-Tordera E, Ren G, Tashakor G (2016) Handling service allocation in combined fog-cloud scenarios. In: 2016 IEEE international conference on communications (ICC). IEEE, pp 1–5

    Google Scholar 

  14. Brogi A, Forti S (2017) QoS-aware deployment of IoT applications through the fog. IEEE Internet Things J 4(5):1185–1192

    CrossRef  Google Scholar 

  15. Gill SS, Garraghan P, Buyya R (2019) ROUTER: fog enabled cloud based intelligent resource management approach for smart home IoT devices. J Syst Softw 154:125–138

    CrossRef  Google Scholar 

  16. Wu H, Wolter K (2017) Stochastic analysis of delayed mobile offloading in heterogeneous networks. IEEE Trans Mob Comput 17(2):461–474

    CrossRef  Google Scholar 

  17. Goudarzi M, Wu H, Palaniswami M, Buyya R (2020) An application placement technique for concurrent IoT applications in edge and fog computing environments. IEEE Trans Mob Comput (TMC)

    Google Scholar 

  18. Pham X-Q, Huh E-N (2016) Towards task scheduling in a cloud-fog computing system. In: 2016 18th Asia-Pacific network operations and management symposium (APNOMS). IEEE, , pp 1–4

    Google Scholar 

  19. Giang NK, Blackstock M, Lea R, Leung VC (2015) Developing IoT applications in the fog: a distributed dataflow approach. In: 2015 5th International conference on the internet of things (IOT). IEEE, pp 155–162

    Google Scholar 

  20. Yousefpour A, Patil A, Ishigaki G, Kim I, Wang X, Cankaya HC, Zhang Q, Xie W, Jue JP (2019) FogPlan: a lightweight QoS-aware dynamic fog service provisioning framework. IEEE Internet Things J 6(3):5080–5096

    CrossRef  Google Scholar 

  21. Aazam M, Huh E-N (2015) Dynamic resource provisioning through fog micro datacentre. In: 2015 IEEE international conference on pervasive computing and communication workshops (PerCom workshops). IEEE, pp 105–110

    Google Scholar 

  22. Aazam M, Huh E-N (2015) Fog computing micro datacenter based dynamic resource estimation and pricing model for IoT. In: 2015 IEEE 29th international conference on advanced information networking and applications. IEEE, pp 687–694

    Google Scholar 

  23. Alsaffar AA, Pham HP, Hong C-S, Huh E-N, Aazam M (2016) An architecture of IoT service delegation and resource allocation based on collaboration between fog and cloud computing. Mob Inf Syst 2016

    Google Scholar 

  24. Tao Y, Wang X, Xu X, Chen Y (2017) Dynamic resource allocation algorithm for container-based service computing. In: 2017 IEEE 13th International symposium on autonomous decentralized system (ISADS). IEEE, pp 61–67

    Google Scholar 

  25. Ni L, Zhang J, Jiang C, Yan C, Yu K (2017) Resource allocation strategy in fog computing based on priced timed petri nets. IEEE Internet Things J 4(5):1216–1228

    CrossRef  Google Scholar 

  26. Hoang D, Dang TD (2017) FBRC: optimization of task scheduling in fog-based region and cloud. In: 2017 IEEE Trustcom/BigDataSE/ICESS. IEEE, pp 1109–1114

    Google Scholar 

  27. Zhang H, Xiao Y, Bu S, Niyato D, Yu FR, Han Z (2017) Computing resource allocation in three-tier IoT fog networks: a joint optimization approach combining Stackelberg game and matching. IEEE Internet Things J 4(5):1204–1215

    CrossRef  Google Scholar 

  28. Desikan KS, Srinivasan M, Murthy CSR (2017) A novel distributed latency-aware data processing in fog computing-enabled IoT networks. In: Proceedings of the ACM workshop on distributed information processing in wireless networks, pp 1–6

    Google Scholar 

  29. Skarlat O, Nardelli M, Schulte S, Dustdar S (2017) Towards QoS-aware fog service placement. In: 2017 IEEE 1st international conference on fog and edge computing (ICFEC). IEEE, pp 89–96

    Google Scholar 

  30. Mahmud R, Srirama SN, Ramamohanarao K, Buyya R (2019) Quality of Experience (QoE)-aware placement of applications in fog computing environments. J Parallel Distrib Comput 132:190–203

    CrossRef  Google Scholar 

  31. Brogi A, Forti S, Guerrero C, Lera I (2019) How to place your apps in the fog-state of the art and open challenges. arXiv preprint arXiv:1901.05717

  32. Abbasi M, Pasand EM, Khosravi MR (2020) Workload allocation in IoT-fog-cloud architecture using a multi-objective genetic algorithm. J Grid Comput 18:43–56. https://doi.org/10.1007/s10723-020-09507-1

    CrossRef  Google Scholar 

  33. Gupta H, Dastjerdi AV, Ghosh SK, Buyya R (2017) iFogSim: a toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software Pract Experience 47(9):1275–1296

    CrossRef  Google Scholar 

  34. Zeng D, Gu L, Guo S, Cheng Z, Yu S (2016) Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Trans Comput 65(12):3702–3712

    MathSciNet  CrossRef  Google Scholar 

  35. Lin BY, Shen H (2015) Cloud fog: towards high quality of experience in cloud gaming. In: 2015 44th International conference on parallel processing. IEEE, pp 500–509

    Google Scholar 

  36. Wang N, Varghese B, Matthaiou M, Nikolopoulos DS (2017) ENORM: a framework for edge node resource management. IEEE Trans Serv Comput

    Google Scholar 

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Author information

Authors and Affiliations

  1. School of Computer Science, Lovely Professional University, Phagwara, Punjab, India

    Meeniga Sriraghavendra & Priyanka Chawla

  2. Center for Applied Mathematics, Tianjin University, Tianjin, 300072, PR China

    Huaming Wu

  3. School of Electronic Engineering and Computer Science, Queen Mary University of London, Mile End, UK

    Sukhpal Singh Gill

  4. Cloud Computing and Distributed Systems (CLOUDS), Laboratory, School of Computing and Information Systems, The University of Melbourne, Melbourne, Australia

    Rajkumar Buyya

Authors
  1. Meeniga Sriraghavendra
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  2. Priyanka Chawla
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  3. Huaming Wu
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  4. Sukhpal Singh Gill
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  5. Rajkumar Buyya
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Editor information

Editors and Affiliations

  1. Department of Virtualization, University of Petroleum and Energy Studies, Dehradun, Uttarakhand, India

    Dr. Rajeev Tiwari

  2. Department of Computer Science and Engineering, G. B. Pant Government Engineering College, New Delhi, India

    Dr. Mamta Mittal

  3. Department of Computer Engineering, J. C. Bose University of Science and Technology, Faridabad, Haryana, India

    Dr. Lalit Mohan Goyal

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Sriraghavendra, M., Chawla, P., Wu, H., Gill, S.S., Buyya, R. (2022). DoSP: A Deadline-Aware Dynamic Service Placement Algorithm for Workflow-Oriented IoT Applications in Fog-Cloud Computing Environments. In: Tiwari, R., Mittal, M., Goyal, L.M. (eds) Energy Conservation Solutions for Fog-Edge Computing Paradigms. Lecture Notes on Data Engineering and Communications Technologies, vol 74. Springer, Singapore. https://doi.org/10.1007/978-981-16-3448-2_2

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  • DOI: https://doi.org/10.1007/978-981-16-3448-2_2

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