Skip to main content
SpringerLink
Log in

A Map-Reduce Approach for the Dijkstra Algorithm in SDN Over Osmotic Computing Systems

  • Published:
International Journal of Parallel Programming Aims and scope Submit manuscript

Abstract

Osmotic Computing represents a glue solution able to manage the deployment and orchestration of interconnected microelements across heterogeneous physical and virtual infrastructures (e.g., IoT, Edge and Cloud nodes) according to the behavior of hardware and software components during the time. The adoption of Osmotic Computing is challenging, but addressing networking issues is a key research topic due to the emergence of new problems in terms of QoS requirements. In this paper, we analyze how to exploit well-known networking solutions, such as the Dijkstra’s algorithm, and Big Data oriented technologies, such as the Hadoop and MapReduce, to provide efficient newtorking functionalities in Osmotic Computing. In particular, our objective is to minimize the routing path computation time in the software defined network (SDN) at the basis of microelement networking, as well as to ensure a global view and a high level of dynamism of our network topology. To accomplish this task, we process routing tables through a MapReduce based implementation of the Dijkstra’s algorithm whenever a topology change occurs, and we export routing results into the SDN. Our experimental results show that our networking strategy drastically reduces the best path computation time whenever the network of microelements is very large.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  1. Gartner Says 8.4 Billion Connected “Things” Will Be in Use in 2017, Up 31 Percent From 2016, 2017. https://www.gartner.com/newsroom/id/3598917

  2. Villari, M., Fazio, M., Dustdar, S., Rana, O., Ranjan, R.: Osmotic computing: a new paradigm for edge/cloud integration. IEEE Cloud Comput. 3, 76–83 (2016)

    Article  Google Scholar 

  3. Villari, M., Celesti, A., Fazio, M.: Towards osmotic computing: looking at basic principles and technologies. In: Advances in Intelligent Systems and Computing, pp. 906–915. Springer, Cham (2017)

  4. Datta, S.K., Bonnet, C.: Next-generation, data centric and end-to-end IoT architecture based on microservices. In: 2018 IEEE International Conference on Consumer Electronics—Asia (ICCE-Asia), pp. 206–212 (2018)

  5. Apache Hadoop. https://hadoop.apache.org/. Accessed June 2019

  6. Apache Spark. https://spark.apache.org/. Accessed June 2019

  7. Hazarika, A.V., Ram, G.J.S.R., Jain, E.: Performance comparision of Hadoop and spark engine. In: 2017 International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), pp. 671–674 (2017)

  8. Wakde, A., Shende, P., Waydande, S., Uttarwar, S., Deshmukh, G.: Comparative analysis of hadoop tools and spark technology. In: 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA), pp. 1–4 (2018)

  9. Chebbi, I., Boulila, W., Mellouli, N., Lamolle, M., Farah, I.R.: A comparison of big remote sensing data processing with hadoop mapreduce and spark. In: 2018 4th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP), pp. 1–4 (2018)

  10. Zheng, X., Tian, J., Xiao, X., Cui, X., Yu, X.: A heuristic survivable virtual network mapping algorithm. Soft Comput. 1–11 (2018)

  11. Zhou, W., Jia, J.: Lightweight web3d visualization framework using Dijkstra-based mesh segmentation. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10345 LNCS, pp. 138–151 (2017)

  12. Zhang, S.M., Sangaiah, A.: Reliable design for virtual network requests with location constraints in edge-of-things computing. EURASIP J. Wirel. Commun. Netw. 2018 (2018)

  13. Muñoz, R., Vilalta, R., Yoshikane, N., Casellas, R., Martínez, R., Tsuritani, T., Morita, I.: Integration of IoT, transport SDN, and edge/cloud computing for dynamic distribution of IoT analytics and efficient use of network resources. J. Lightwave Technol. 36, 1420–1428 (2018)

    Article  Google Scholar 

  14. Kaur, K., Garg, S., Aujla, G., Kumar, N., Rodrigues, J., Guizani, M.: Edge computing in the industrial internet of things environment: software-defined-networks-based edge-cloud interplay. IEEE Commun. Mag. 56, 44–51 (2018)

    Article  Google Scholar 

  15. Guo, H., Liu, J.: Collaborative computation offloading for multi-access edge computing over fiber-wireless networks. IEEE Trans. Veh. Technol. (2018)

  16. Liu, M., Mao, Y., Leng, S., Mao, S.: Full-duplex aided user virtualization for mobile edge computing in 5g networks. IEEE Access 6, 2996–3007 (2017)

    Article  Google Scholar 

  17. He, Y., Yu, F.R., Zhao, N., Leung, V., Yin, H.: Software-defined networks with mobile edge computing and caching for smart cities: a big data deep reinforcement learning approach. IEEE Commun. Mag. 55, 31–37 (2017)

    Article  Google Scholar 

  18. Huang, C., Chiang, M., Dao, D., Su, W., Xu, S., Zhou, H.: V2v data offloading for cellular network based on the software defined network (SDN) inside mobile edge computing (MEC) architecture. IEEE Access (2018)

  19. Wang, K., Yin, H., Quan, W., Min, G.: Enabling collaborative edge computing for software defined vehicular networks. IEEE Netw. (2018)

  20. Lin, C., Xue, C., Hu, J., Li, W.Z.: Hierarchical architecture design of computer system. Jisuanji Xuebao Chin. J. Comput. 40, 1996–2017 (2017)

    MathSciNet  Google Scholar 

  21. Liu, Z., Yang, H., Kou, S.: Shared protection algorithm based on virtual network embedding framework in fiber-wireless access network (2017)

  22. Wang, M., Liu, J., Mao, J., Cheng, H., Chen, J., Qi, C.: Routeguardian: constructing. Tsinghua Sci. Technol. 22, 400–412 (2017)

    Article  Google Scholar 

  23. Wang, C., Yan, S.: Scaling SDN network with self-adjusting architecture. In: IEEE International Conference on Electronic Information and Communication Technology (ICEICT), Harbin, pp. 116–120 (2016)

  24. Vig, A., Kushwah, R.S., Tomar, R.S., Kushwah, S.S.: Autonomous agent based shortest path load balancing in cloud. In: 8th International Conference on Computational Intelligence and Communication Networks (CICN), Tehri, pp. 33–37 (2016)

  25. Rattanawadee, P., Ruengsakulrach, N., Saivichit, C.: The transmission time analysis of IPTV multicast service in SDN/openflow environments (2015)

  26. Liu, F., Chen, X., An, W., Peng, Y., Cao, J., Zhang, Y.: Minimizing transmission cost for multiple service function chains in SDN/NFV networks, vol. 2017, pp. 1–6 (2018)

  27. Li, Y., Orgerie, A.C., Rodero, I., Amersho, B.L., Parashar, M., Menaud, J.M.: End-to-end energy models for edge cloud-based IoT platforms: application to data stream analysis in IoT. Future Gener. Comput. Syst. 87, 667–678 (2018)

    Article  Google Scholar 

  28. Dong, S., Gao, Z., Pirbhulal, S., Bian, G.B., Zhang, H., Wu, W., Li, S.: IoT-based 3d convolution for video salient object detection. Neural Comput. Appl. (2019)

  29. Chen, M., Xiong, J., Ma, C., Zhao, Q., Feng, S.: Real-time buffering, parallel processing and high-speed transmission system for multi-channel video images based on ADER. In: 2019 International Conference on Computer, Network, Communication and Information Systems (CNCI 2019). Atlantis Press (2019)

  30. What is OpenFlow? Definition and How it Relates to SDN. (https://www.sdxcentral.com/networking/sdn/definitions/what-is-openflow/) Last access: (June 2019)

  31. The Network Simulator - ns-2. https://www.isi.edu/nsnam/ns/. Accessed June 2019

  32. Opnet Network Simulator. http://opnetprojects.com/opnet-network-simulator/. Accessed June 2019

  33. Mininet An Instant Virtual Network on your Laptop (or other PC). http://mininet.org/. Accessed June 2019

  34. What is a Floodlight Controller? https://www.sdxcentral.com/networking/sdn/definitions/what-is-floodlight-controller/. Accessed June 2019

  35. Opendaylight. https://www.opendaylight.org/. Accessed June 2019

  36. What is FlowVisor? https://searchnetworking.techtarget.com/definition/FlowVisor. Accessed June 2019

  37. Ashouri, M., Setayesh, S.: Enhancing the performance and stability of SDN architecture with a fat-tree based algorithm (2018)

  38. Duque, J.P., Beltrán, D.D., Leguizamón, G.P.: Opendaylight vs. floodlight: comparative analysis of a load balancing algorithm for software defined networking. Int. J. Commun. Netw. Inf. Secur. 10, 348–357 (2018)

    Google Scholar 

  39. Alrashedy, K., Kimmett, B., Gulliver, T.A.: Performance of software-defined networking controllers for different network topologies. In: IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM) (2017)

  40. Ganglia Monitoring System. http://ganglia.sourceforge.net/. Accessed June 2019

Download references

Acknowledgements

This work has been supported by C4E.

Author information

Authors and Affiliations

  1. MIFT, University of Messina, Messina, Italy

    Maria Fazio, Alina Buzachis, Antonino Galletta, Antonio Celesti & Massimo Villari

  2. School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, China

    Jiafu Wan

  3. Istituto Nazionale di Alta Matematica (INdAM) “F. Severi”, Gruppo Nazionale per il Calcolo Scientifico (GNCS), Rome, Italy

    Maria Fazio & Antonio Celesti

  4. Dipartimento di Ingegneria dell’Innovazione, University of Salento, Lecce, Italy

    Antonella Longo

Authors
  1. Maria Fazio
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alina Buzachis
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Antonino Galletta
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Antonio Celesti
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jiafu Wan
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Antonella Longo
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Massimo Villari
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Maria Fazio.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fazio, M., Buzachis, A., Galletta, A. et al. A Map-Reduce Approach for the Dijkstra Algorithm in SDN Over Osmotic Computing Systems. Int J Parallel Prog 49, 347–375 (2021). https://doi.org/10.1007/s10766-021-00693-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10766-021-00693-3

Keywords

Search

Navigation