Transitions for Increased Flexibility in Fog Computing: A Case Study on Complex Event Processing

  • Manisha LuthraEmail author
  • Boris Koldehofe
  • Ralf Steinmetz


Fog computing is envisioned to enable profound applications in the Internet of Things (IoT). A key characteristic of such applications is the need to exchange vital information between distinct IoT devices in the form of event notifications, e. g., traffic conditions when performing traffic monitoring. Complex event processing (CEP) is a powerful paradigm to overcome the information gap from observing primary sensor data by IoT devices to delivering event notifications to the IoT application users. However, to perform CEP in a highly dynamic IoT environment, e. g., involving mobile and heterogeneous devices, require an extremely flexible design of a CEP system to adaptively meet the changing requirements and conditions in which the CEP system is executed.

In this article, we show on the use case of CEP, “how to increase flexibility in a fog-cloud computing environment building on a methodology known as mechanism transitions”. In particular, we state and analyze two exemplary IoT use cases to show the potential of mechanism transitions. We identify and discuss possible promising mechanism transitions in the context of CEP. We perform an experimental study for operator placement and show how transitions help to adapt to conflicting performance objectives.


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  1. 1.
    Ahmad M, Amin MB, Hussain S, Kang BH, Cheong T, Lee S (2016) Health fog: A novel framework for health and wellness applications. J Supercomput 72(10):3677–3695CrossRefGoogle Scholar
  2. 2.
    Alt B, Weckesser M, Becker C, Hollick M, Kar S, Klein A, Klose R, Kluge R, Koeppl H, Koldehofe B, KhudaBukhsh WR, Luthra M, Mousavi M, Muehlhaeuser M, Pfannemueller M, Rizk A, Schuerr A, Steinmetz R (2019) Transitions: A protocol-independent view of the future internet. In: Proceedings of the IEEE, pp 1–12Google Scholar
  3. 3.
    Balazinska M, H Balakrishnan, Madden SR, Stonebraker M (2008) Fault-tolerance in the borealis distributed stream processing system. ACM T Database Syst 33(1):1–44CrossRefGoogle Scholar
  4. 4.
    Bonomi F, Milito R, Zhu J, Addepalli S (2012) 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 13–16Google Scholar
  5. 5.
    Byers CC (2017) Architectural imperatives for fog computing: Use cases, requirements, and architectural techniques for fog-enabled IoT networks. IEEE Commun Mag 55(8):14–20CrossRefGoogle Scholar
  6. 6.
    Castro Fernandez R, Migliavacca M, Kalyvianaki E, Pietzuch P (2013) Integrating scale out and fault tolerance in stream processing using operator state management. In: Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data, pp 725–736Google Scholar
  7. 7.
    Chen X (2015) Decentralized computation offloading game for mobile cloud computing. IEEE T Parall Distr 26(4):974–983CrossRefGoogle Scholar
  8. 8.
    Cisco: Fog Computing and the Internet of Things: Extend the Cloud to Where the Things Are., last access: 22.11.2018.Google Scholar
  9. 9.
    Cugola G, Margara A, Matteucci M, Tamburrelli G (2015) Introducing uncertainty in complex event processing: Model, implementation, and validation. Computing 97(2):103–144CrossRefGoogle Scholar
  10. 10.
    Gramaglia M, Trullols-Cruces O, Naboulsi D, Fiore M, Calderon M (2016) Mobility and connectivity in highway vehicular networks: A case study in Madrid. Comput Commun 78:28–44CrossRefGoogle Scholar
  11. 11.
    Gulisano V, Jiménez-Peris R, Patiño-Martñez M, Soriente C, Valduriez P (2012) Streamcloud: An elastic and scalable data streaming system. IEEE T Parall Distr 23(12):2351–2365CrossRefGoogle Scholar
  12. 12.
    Heinze T, Jerzak Z, Hackenbroich G, Fetzer C (2014) Latency-aware elastic scaling for distributed data stream processing systems. In: Proceedings of the 8th ACM International Conference on Distributed Event-Based Systems, pp 13–22Google Scholar
  13. 13.
    Heinze T, Zia M, Krahn R, Jerzak Z, Fetzer C (2015) An adaptive replication scheme for elastic data stream processing systems. In: Proceedings of the 9th ACM International Conference on Distributed Event-Based Systems, pp 150–161Google Scholar
  14. 14.
    Hou X, Li Y, Chen M, Wu D, Jin D, Chen S (2016) Vehicular fog computing: A viewpoint of vehicles as the infrastructures. IEEE T Veh Technol 65(6):3860–3873CrossRefGoogle Scholar
  15. 15.
    Hu P, Dhelim S, Ning H, Qiu T (2017) Survey on fog computing: Architecture, key technologies, applications and open issues. J Netw Comput Appl 98:27–42CrossRefGoogle Scholar
  16. 16.
    Koldehofe B, Mayer R, Ramachandran U, Rothermel K, Völz M. Rollback-recovery without checkpoints in distributed event processing systems. In: Proceedings of the 7th ACM International Conference on Distributed Event-based Systems, pp 27–38Google Scholar
  17. 17.
    Lakshmanan GT, Li Y, Strom R (2008) Placement strategies for internet-scale data stream systems. IEEE Internet Comput 12(6):50–60CrossRefGoogle Scholar
  18. 18.
    Liu X, Harwood A, Karunasekera S, Rubinstein B, Buyya R (2017) E-storm: Replication-based state management in distributed stream processing systems. In: 46th International Conference on Parallel Processing, pp 571–580Google Scholar
  19. 19.
    Luthra M (2018) Adapting to dynamic user environments in complex event processing system using transitions. In: Proceedings of the 12th ACM International Conference on Distributed and Event-based Systems, pp 274–277Google Scholar
  20. 20.
    Luthra M (2018) Understanding the behavior of operator placement mechanisms on large scale networks. In: Proceedings of the 19th ACM/IFIP/USENIX International Middleware Conference: Posters and DemosGoogle Scholar
  21. 21.
    Luthra M, Koldehofe B, Weisenburger P, Salvaneschi G, Arif R (2018) Tcep: Adapting to dynamic user environments by enabling transitions between operator placement mechanisms. In: Proceedings of the 12th ACM International Conference on Distributed and Event-based Systems, pp 136–147Google Scholar
  22. 22.
    Mathur A, Newe T, Elgenaidi W, Rao M, Dooly G, Toal D (2017) A secure end-to-end iot solution. Sensor Actuat A-Phys 263:291–299CrossRefGoogle Scholar
  23. 23.
    Mayer R, Koldehofe B, Rothermel K (2015) Predictable low-latency event detection with parallel complex event processing. IEEE Internet Things J 2(4):274–286CrossRefGoogle Scholar
  24. 24.
    Munir A, Kansakar P, Khan SU (2017) Ifciot: Integrated fog cloud iot: A novel architectural paradigm for the future internet of things. IEEE Consum Electr Mag 6(3):74–82CrossRefGoogle Scholar
  25. 25.
    Ottenwälder B, Koldehofe B, Rothermel K, Hong K, Lillethun D, Ramachandran U (2014) MCEP: A mobility-aware complex event processing system. ACM T Intern Technol 14(1):1–24CrossRefGoogle Scholar
  26. 26.
    Murthy Palanisamy S, Dürr F, Adnan Tariq M, Rothermel K (2018) Preserving privacy and quality of service in complex event processing through event reordering. In: Proceedings of the 12th ACM International Conference on Distributed and Event-based Systems, pp 40–51Google Scholar
  27. 27.
    Pietzuch P, Ledlie J, Shneidman J, Roussopoulos M, Welsh M, Seltzer M (2006) Network-aware operator placement for stream-processing systems. In: Proceedings of the 22nd International Conference on Data Engineering, pp 49–49Google Scholar
  28. 28.
    Rahmani MA, Preden L-SP, Jantsch A (2018) Fog Computing in the Internet of Things. Springer International PublishingGoogle Scholar
  29. 29.
    Richerzhagen B, Koldehofe B, Steinmetz R (2015) Immense Dynamism. German Res 37:24–27CrossRefGoogle Scholar
  30. 30.
    Samulat P (2017) Die Digitalisierung der Welt: Wie das Industrielle Internet der Dinge aus Produkten Services macht. Springer Fachmedien Wiesbaden, Wiesbaden, pp 103–124CrossRefGoogle Scholar
  31. 31.
    Satzger B, Hummer W, Leitner P, Dustdar S (2011) Esc: Towards an elastic stream computing platform for the cloud. In: IEEE 4th International Conference on Cloud Computing, pp 348–355Google Scholar
  32. 32.
    Simmhan Y (2018) Big Data and Fog Computing. Springer International Publishing, Cham, pp 1–10Google Scholar
  33. 33.
    Starks F, Plagemann TP (2015) Operator placement for efficient distributed complex event processing in manets. In: IEEE 11th International Conference on Wireless and Mobile Computing, Networking and Communications, pp 83–90Google Scholar
  34. 34.
    Starks F, Goebel V, Kristiansen S, Plagemann T (2017) Mobile Distributed Complex Event Processing – Ubi Sumus? Quo Vadimus? Mobile Big Data – A Roadmap from Models to Technologies, vol 1. Springer, pp 1–34Google Scholar
  35. 35.
    Weisenburger P, Luthra M, Koldehofe B, Salvaneschi G (2017) Quality-aware runtime adaptation in complex event processing. In: Proceedings of the 12th International Symposium on Software Engineering for Adaptive and Self-Managing Systems, pp 140–151Google Scholar
  36. 36.
    Xing Y, Hwang J-H, Çetintemel U, Zdonik S (2006) Providing resiliency to load variations in distributed stream processing. In: Proceedings of the 32nd International Conference on Very Large Data Bases. VLDB Endowment, pp 775–786Google Scholar

Copyright information

© Springer-Verlag GmbH Deutschland, ein Teil von Springer Nature 2019

Authors and Affiliations

  • Manisha Luthra
    • 1
    Email author
  • Boris Koldehofe
    • 1
  • Ralf Steinmetz
    • 1
  1. 1.Technical University of DarmstadtDarmstadtGermany

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