Advertisement

LW-CoEdge: a lightweight virtualization model and collaboration process for edge computing

  • Marcelo Pitanga AlvesEmail author
  • Flavia C. Delicato
  • Igor L. Santos
  • Paulo F. Pires
Article
  • 26 Downloads
Part of the following topical collections:
  1. Special Issue on Smart Computing and Cyber Technology for Cyberization

Abstract

Edge Computing is a novel paradigm that extends Cloud Computing by moving the computation closer to the end users and/or data sources. When considering Edge Computing, it is possible to design a three-tier architecture (comprising tiers for the cloud devices, edge devices, and end devices) which is useful to meet emerging IoT applications that demand low latency, geo-localization, and energy efficiency. Like the Cloud, the Edge Computing paradigm relies on virtualization. An Edge Computing virtualization model provides a set of virtual nodes (VNs) built on top of the physical devices that make up the three-tier architecture. It also provides the processes of provisioning and allocating VNs to IoT applications at the edge of the network. Performing these processes efficiently and cost-effectively raises a resource management challenge. Applying the traditional cloud virtualization models (typically centralized) to virtualize the edge tier, are unsuitable to handle emerging IoT application scenarios due to the specific features of the edge nodes, such as geographical distribution, heterogeneity and, resource constraints. Therefore, we propose a novel distributed and lightweight virtualization model targeting the edge tier, encompassing the specific requirements of IoT applications. We designed heuristic algorithms along with a P2P collaboration process to operate in our virtualization model. The algorithms perform (i) a distributed resource management process, and (ii) data sharing among neighboring VNs. The distributed resource management process provides each edge node with decision-making capability, engaging neighboring edge nodes to allocate or provision on-demand VNs. Thus, the distributed resource management improves system performance, serving more requests and handling edge node geographical distribution. Meanwhile, data sharing reduces the data transmissions between end devices and edge nodes, saving energy and reducing data traffic for IoT-edge infrastructures.

Keywords

collaboration data sharing edge computing lightweight virtualization P2P resource management 

Notes

Acknowledgements

This work is partially funded by FAPESP (grant 2015/24144-7). Professors Flavia C. Delicato and Paulo F. Pires are CNPq Fellows.

References

  1. 1.
    Aazam, M., Huh, E.N.: Fog computing: the cloud-iot/ioe middleware paradigm. IEEE Potentials. 35(3), 40–44 (2016)CrossRefGoogle Scholar
  2. 2.
    Aazam, M., Khan, I., Alsaffar, A.A., Huh, E.N.: Cloud of things: integrating internet of things and cloud computing and the issues involved. In: Applied Sciences and Technology (IBCAST), 2014 11th International Bhurban Conference on, pp. 414–419. IEEE (2014)Google Scholar
  3. 3.
    Alam, M.G.R., Hassan, M.M., Uddin, M.Z., Almogren, A., Fortino, G.: Autonomic computation offloading in mobile edge for IoT applications. Futur. Gener. Comput. Syst. 90, 149–157 (2019)CrossRefGoogle Scholar
  4. 4.
    Aloi, G., Caliciuri, G., Fortino, G., Gravina, R., Pace, P., Russo, W., Savaglio, C.: Enabling IoT interoperability through opportunistic smartphone-based mobile gateways. J. Netw. Comput. Appl. 81, 74–84 (2017)CrossRefGoogle Scholar
  5. 5.
    Armbrust, M., Fox, A., Griffith, R., Joseph, A.D., Katz, R., Konwinski, A., et al.: A view of cloud computing. Commun. ACM. 53(4), 50–58 (2010)CrossRefGoogle Scholar
  6. 6.
    Atzori, L., Iera, A., Morabito, G.: The internet of things: a survey. Comput. Netw. 54(15), 2787–2805 (2010)CrossRefGoogle Scholar
  7. 7.
    Basili, R.B., (1992). Software Modeling and Measurement: The Goal/Question/Metric Paradigm. Technical Report. University of Maryland at College Park, College Park, MD, USA.Google Scholar
  8. 8.
    Bonomi, F.: Connected vehicles, the internet of things, and fog computing. In: The Eighth ACM International Workshop on Vehicular Inter-Networking (VANET), Las Vegas, USA, pp. 13–15 (2011)Google Scholar
  9. 9.
    Bonomi, F., Milito, R., Zhu, J., Addepalli, S.: Fog computing and its role in the internet of things. In: Proceedings of the First Edition of the MCC Workshop on Mobile Cloud Computing, pp. 13–16. ACM (2012)Google Scholar
  10. 10.
    Bonomi, F., Milito, R., Natarajan, P., Zhu, J.: Fog computing: a platform for internet of things and analytics. In: Big Data and Internet of Things: a Roadmap for Smart Environments, pp. 169–186. Springer International Publishing (2014)Google Scholar
  11. 11.
    Botta, A., De Donato, W., Persico, V., Pescapé, A.: On the integration of cloud computing and internet of things. In: Future Internet of Things and Cloud (FiCloud), 2014 International Conference on, pp. 23–30. IEEE (2014)Google Scholar
  12. 12.
    Bouzeghoub, M.: A framework for analysis of data freshness. In: Proceedings of the 2004 International Workshop on Information Quality in Information Systems, pp. 59–67. ACM (2004)Google Scholar
  13. 13.
    Byers, C. C., & Wetterwald, P. (2015). Fog computing distributing data and intelligence for resiliency and scale necessary for IoT: the Internet Of Things (ubiquity symposium). Ubiquity, 2015 (November), 4Google Scholar
  14. 14.
    Carrol, J.M.: Five reasons for scenario-based design. In: Proceedings of the 32nd Annual Hawaii International Conference on Systems Sciences. 1999. HICSS-32. Abstracts and CD-ROM of Full Papers. 11-pp, IEEE (1999)Google Scholar
  15. 15.
    Casadei, R., Fortino, G., Pianini, D., Russo, W., Savaglio, C., Viroli, M.: Modelling and simulation of opportunistic IoT services with aggregate computing. Futur. Gener. Comput. Syst. 91, 252–262 (2019)CrossRefGoogle Scholar
  16. 16.
    Catarinucci, L., De Donno, D., Mainetti, L., Palano, L., Patrono, L., Stefanizzi, M.L., Tarricone, L.: An IoT-aware architecture for smart healthcare systems. IEEE Internet Things J. 2(6), 515–526 (2015)CrossRefGoogle Scholar
  17. 17.
    Cavalcante, E., Pereira, J., Alves, M.P., Maia, P., Moura, R., Batista, T., et al.: On the interplay of internet of things and cloud computing: a systematic mapping study. Comput. Commun. 89, 17–33 (2016)CrossRefGoogle Scholar
  18. 18.
  19. 19.
    CEP: Complex Event Processing. Available in: https://en.wikipedia.org/wiki/Complex_event_processing (2017). Last accessed: 11/07/2017
  20. 20.
    Cisco IOx: Available in: https://www.cisco.com/c/en/us/products/cloud-systems-management/iox/index.html. Last accessed: 11/07/2017
  21. 21.
    Delicato, F.C., Pires, P.F., Pirmez, L., Batista, T.: Wireless sensor networks as a service. In: Engineering of Computer Based Systems (ECBS), 2010 17th IEEE International Conference and Workshops on, pp. 410–417. IEEE (2010)Google Scholar
  22. 22.
    Delicato, F.C., Pires, P.F., Batista, T.: The resource management challenge in IoT. In: Resource Management for Internet of Things, pp. 7–18. Springer International Publishing (2017)Google Scholar
  23. 23.
    Distefano, S., Merlino, G., Puliafito, A.: Sensing and actuation as a service: a new development for clouds. In: Network Computing and Applications (NCA), 2012 11th IEEE International Symposium on, pp. 272–275. IEEE (2012)Google Scholar
  24. 24.
  25. 25.
    EdgeX Foundry: The Open Platform for the IoT Edge. Available in: https://www.edgexfoundry.org
  26. 26.
    Endo, P.T., de Almeida Palhares, A.V., Pereira, N.N., Goncalves, G.E., Sadok, D., Kelner, J., et al.: Resource allocation for distributed cloud: concepts and research challenges. IEEE Netw. 25(4), (2011)CrossRefGoogle Scholar
  27. 27.
  28. 28.
    FIWARE IoT Agent for Ultralight 2.0 protocol: https://github.com/telefonicaid/iotagent-ul
  29. 29.
    FIWARE Orion Context Broker, Release 4: http://fiware-orion.readthedocs.io/en/master/index.html
  30. 30.
    Fortino, G., Russo, W., Savaglio, C., Shen, W., Zhou, M.: Agent-oriented cooperative smart objects: from IoT system design to implementation. IEEE Transactions on Systems, Man, and Cybernetics: Systems. 99, 1–18 (2017)Google Scholar
  31. 31.
    Garcia Lopez, P., Montresor, A., Epema, D., Datta, A., Higashino, T., Iamnitchi, A., et al.: Edge-centric computing: vision and challenges. ACM SIGCOMM Computer Communication Review. 45(5), 37–42 (2015)CrossRefGoogle Scholar
  32. 32.
    Giang, N.K., Blackstock, M., Lea, R., Leung, V.C.: Developing iot applications in the fog: a distributed dataflow approach. In: Internet of Things (IOT), 2015 5th International Conference on the, pp. 155–162. IEEE (2015)Google Scholar
  33. 33.
    Gubbi, J., Buyya, R., Marusic, S., Palaniswami, M.: Internet of things (IoT): a vision, architectural elements, and future directions. Futur. Gener. Comput. Syst. 29(7), 1645–1660 (2013)CrossRefGoogle Scholar
  34. 34.
  35. 35.
    Hong, K., Lillethun, D., Ramachandran, U., Ottenwälder, B., Koldehofe, B.: Mobile fog: a programming model for large-scale applications on the internet of things. In: Proceedings of the Second ACM SIGCOMM Workshop on Mobile Cloud Computing, pp. 15–20. ACM (2013)Google Scholar
  36. 36.
    Inaba, M., Katoh, N., Imai, H.: Applications of weighted Voronoi diagrams and randomization to variance-based k-clustering. In: Proceedings of the Tenth Annual Symposium on Computational Geometry, pp. 332–339. ACM (1994)Google Scholar
  37. 37.
  38. 38.
    Johnston, W.M., Hanna, J.R., Millar, R.J.: Advances in dataflow programming languages. ACM Computing Surveys (CSUR). 36(1), 1–34 (2004)CrossRefGoogle Scholar
  39. 39.
  40. 40.
    Khan, I., Belqasmi, F., Glitho, R., Crespi, N., Morrow, M., Polakos, P.: Wireless sensor network virtualization: a survey. IEEE Communications Surveys & Tutorials. 18(1), 553–576 (2016)CrossRefGoogle Scholar
  41. 41.
    Kokash, N.: An introduction to heuristic algorithms, pp. 1–8. Department of Informatics and Telecommunications (2005)Google Scholar
  42. 42.
    Lewis, J., & Fowler, M. (2014). Microservices. Available in: http://martinfowler.com/articles/microservices.html. Last accessed: 27/09/2017
  43. 43.
    Luan, T.H., Gao, L., Li, Z., Xiang, Y., Wei, G., Sun, L.: Fog computing: Focusing on mobile users at the edge. arXiv preprint. arXiv, 1502.01815 (2015)Google Scholar
  44. 44.
    Maaroju, N., & Garg, D. G. (2009). Choosing the best heuristic for a NP-Problem. Masters of Engineering, Patiala, Thapar University, Faculty of Computer Science and Engineering. June 2009Google Scholar
  45. 45.
    Madria, S., Kumar, V., Dalvi, R.: Sensor cloud: a cloud of virtual sensors. IEEE Softw. 31(2), 70–77 (2014)CrossRefGoogle Scholar
  46. 46.
    Mahmud, R., Kotagiri, R., Buyya, R.: Fog computing: a taxonomy, survey and future directions. In: Internet of Everything, pp. 103–130. Springer, Singapore (2018)Google Scholar
  47. 47.
    Morabito, R.: Virtualization on internet of things edge devices with container technologies: a performance evaluation. IEEE Access. 5, 8835–8850 (2017)CrossRefGoogle Scholar
  48. 48.
    Morabito, R., Cozzolino, V., Ding, A.Y., Beijar, N., Ott, J.: Consolidate IoT edge computing with lightweight virtualization. IEEE Netw. 32(1), 102–111 (2018)CrossRefGoogle Scholar
  49. 49.
    Morvaj, B., Lugaric, L., Krajcar, S.: Demonstrating smart buildings and smart grid features in a smart energy city. In: Proceedings of the 2011 3rd International Youth Conference on Energetics (IYCE), pp. 1–8. IEEE (2011)Google Scholar
  50. 50.
    Munir, A., Kansakar, P., Khan, S.U.: IFCIoT: integrated fog cloud IoT architectural paradigm for future internet of things. arXiv preprint. arXiv, 1701.08474 (2017)Google Scholar
  51. 51.
    Mutlag, A.A., Ghani, M.K.A., Arunkumar, N., Mohamed, M.A., Mohd, O.: Enabling technologies for fog computing in healthcare IoT systems. Futur. Gener. Comput. Syst. 90, 62–78 (2019)CrossRefGoogle Scholar
  52. 52.
    Naha, R.K., Garg, S., Georgakopoulos, D., Jayaraman, P.P., Gao, L., Xiang, Y., Ranjan, R.: Fog computing: survey of trends, architectures, requirements, and research directions. IEEE access. 6, 47980–48009 (2018)CrossRefGoogle Scholar
  53. 53.
    Nan, Y., Li, W., Bao, W., Delicato, F.C., Pires, P.F., Zomaya, A.Y.: Cost-effective processing for delay-sensitive applications in cloud of things systems. In: Network Computing and Applications (NCA), 2016 IEEE 15th International Symposium on, pp. 162–169. IEEE (2016)Google Scholar
  54. 54.
    Nishant, K., Sharma, P., Krishna, V., Gupta, C., Singh, K.P., Rastogi, R.: Load balancing of nodes in cloud using ant colony optimization. In: Computer Modelling and Simulation (UKSim), 2012 UKSim 14th International Conference on, pp. 3–8. IEEE (2012)Google Scholar
  55. 55.
    Pace, P., Aloi, G., Gravina, R., Caliciuri, G., Fortino, G., Liotta, A.: An edge-based architecture to support efficient applications for healthcare industry 4.0. IEEE Transactions on Industrial Informatics. 15(1), 481–489 (2018)CrossRefGoogle Scholar
  56. 56.
    Pahl, C., Lee, B.: Containers and clusters for edge cloud architectures--a technology review. In: Future Internet of Things and Cloud (FiCloud), 2015 3rd International Conference on, pp. 379, 2015–386. IEEE (2015)Google Scholar
  57. 57.
    Peralta, G., Iglesias-Urkia, M., Barcelo, M., Gomez, R., Moran, A., Bilbao, J.: Fog computing based efficient IoT scheme for the industry 4.0. In: 2017 IEEE International Workshop of Electronics, Control, Measurement, Signals and their Application to Mechatronics (ECMSM), pp. 1–6. IEEE (2017)Google Scholar
  58. 58.
    PubNub Staff. How Fast is Realtime? Human Perception and Technology. On-line publish. February 9, 2015. Available in: https://www.pubnub.com/blog/how-fast-is-realtime-human-perception-and-technology/. Last accessed: 05/09/2019
  59. 59.
    Sahni, Y., Cao, J., Zhang, S., Yang, L.: Edge mesh: a new paradigm to enable distributed intelligence in internet of things. IEEE Access. 5, 16441–16458 (2017)CrossRefGoogle Scholar
  60. 60.
    Santos, I.L., Pirmez, L., Delicato, F.C., Khan, S.U., Zomaya, A.Y.: Olympus: the cloud of sensors. IEEE Cloud Computing. 2(2), 48–56 (2015)CrossRefGoogle Scholar
  61. 61.
    Santos, I.L., Pirmez, L., Delicato, F.C., Oliveira, G.M., Farias, C.M., Khan, S.U., Zomaya, A.Y.: Zeus: a resource allocation algorithm for the cloud of sensors. Futur. Gener. Comput. Syst. 92, 564–581 (2019)CrossRefGoogle Scholar
  62. 62.
    Satyanarayanan, M., Bahl, P., Caceres, R., Davies, N.: The case for vm-based cloudlets in mobile computing. IEEE pervasive Computing. 8(4), (2009)Google Scholar
  63. 63.
    Sheng, X., Tang, J., Xiao, X., Xue, G.: Sensing as a service: challenges, solutions and future directions. IEEE Sensors J. 13(10), 3733–3741 (2013)CrossRefGoogle Scholar
  64. 64.
    Shi, H., Chen, N., Deters, R.: Combining mobile and fog computing: using coap to link mobile device clouds with fog computing. In: Data Science and Data Intensive Systems (DSDIS), 2015 IEEE International Conference on, pp. 564–571. IEEE (2015)Google Scholar
  65. 65.
    Shi, W., Cao, J., Zhang, Q., Li, Y., Xu, L.: Edge computing: vision and challenges. IEEE Internet Things J. 3(5), 637–646 (2016)CrossRefGoogle Scholar
  66. 66.
    Skarlat, O., Schulte, S., Borkowski, M., Leitner, P.: Resource provisioning for iot services in the fog. In: Service-Oriented Computing and Applications (SOCA), 2016 IEEE 9th International Conference on, pp. 32–39. IEEE (2016)Google Scholar
  67. 67.
  68. 68.
    Taleb, T., Dutta, S., Ksentini, A., Iqbal, M., Flinck, H.: Mobile edge computing potential in making cities smarter. IEEE Commun. Mag. 55(3), 38–43 (2017)CrossRefGoogle Scholar
  69. 69.
    Tan, L., Wang, N.: Future internet: the internet of things. In: Advanced Computer Theory and Engineering (ICACTE), 2010 3rd International Conference on(Vol. 5, Pp. V5–376). IEEE (2010)Google Scholar
  70. 70.
    Thönes, J.: Microservices. IEEE Softw. 32(1), 116–116 (2015)CrossRefGoogle Scholar
  71. 71.
  72. 72.
    Wang, N., Varghese, B., Matthaiou, M., Nikolopoulos, D.S.: ENORM: a framework for edge node resource management. IEEE Trans. Serv. Comput. (2017)Google Scholar
  73. 73.
    Weidenhaupt, K., Pohl, K., Jarke, M., Haumer, P.: Scenarios in system development: current practice. IEEE Softw. 15(2), 34–45 (1998)CrossRefGoogle Scholar
  74. 74.
    Xia, C., Li, W., Chang, X., Delicato, F., Yang, T., Zomaya, A.: Edge-based energy Management for Smart Homes. In: 2018 IEEE 16th Intl Conf on Dependable, Autonomic and Secure Computing, 16th Intl Conf on Pervasive Intelligence and Computing, 4th Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress (DASC/PiCom/DataCom/CyberSciTech), pp. 849–856. IEEE (2018)Google Scholar
  75. 75.
    Xu, J., Palanisamy, B., Ludwig, H., Wang, Q.: Zenith: Utility-aware resource allocation for edge computing. In: Edge Computing (EDGE), 2017 IEEE International Conference on, pp. 47–54. IEEE (2017)Google Scholar
  76. 76.
    Yang, L., Li, W., Ghandehari, M., Fortino, G.: People-centric cognitive internet of things for the quantitative analysis of environmental exposure. IEEE Internet Things J. 5(4), 2353–2366 (2017)CrossRefGoogle Scholar
  77. 77.
    Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, vol. 2015. ACM (2015a)Google Scholar
  78. 78.
    Yi, S., Hao, Z., Qin, Z., Li, Q.: Fog computing: Platform and applications. In: Hot Topics in Web Systems and Technologies (HotWeb). 2015 Third IEEE Workshop on, pp. 73–78. IEEE (2015b)Google Scholar
  79. 79.
    Zhang, B., Mor, N., Kolb, J., Chan, D. S., Lutz, K., Allman, E., ... & Kubiatowicz, J. (2015). The Cloud Is Not Enough: Saving IoT from the Cloud. In HotCloudGoogle Scholar

Copyright information

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

Authors and Affiliations

  1. 1.Universidade Federal do Rio de Janeiro (UFRJ)Rio de JaneiroBrazil
  2. 2.Centro Federal de Educação Tecnológica Celso Suckow da Fonseca (CEFET-RJ)Rio de JaneiroBrazil

Personalised recommendations