Abstract
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.
Similar content being viewed by others
References
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):3677–3695
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 1–12
Balazinska M, H Balakrishnan, Madden SR, Stonebraker M (2008) Fault-tolerance in the borealis distributed stream processing system. ACM T Database Syst 33(1):1–44
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 13–16
Byers CC (2017) Architectural imperatives for fog computing: Use cases, requirements, and architectural techniques for fog-enabled IoT networks. IEEE Commun Mag 55(8):14–20
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 725–736
Chen X (2015) Decentralized computation offloading game for mobile cloud computing. IEEE T Parall Distr 26(4):974–983
Cisco: Fog Computing and the Internet of Things: Extend the Cloud to Where the Things Are. https://www.cisco.com/c/dam/en_us/solutions/trends/iot/docs/computing-overview.pdf, last access: 22.11.2018.
Cugola G, Margara A, Matteucci M, Tamburrelli G (2015) Introducing uncertainty in complex event processing: Model, implementation, and validation. Computing 97(2):103–144
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 78:28–44
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):2351–2365
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 13–22
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 150–161
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):3860–3873
Hu P, Dhelim S, Ning H, Qiu T (2017) Survey on fog computing: Architecture, key technologies, applications and open issues. J Netw Comput Appl 98:27–42
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 27–38
Lakshmanan GT, Li Y, Strom R (2008) Placement strategies for internet-scale data stream systems. IEEE Internet Comput 12(6):50–60
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 571–580
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 274–277
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 Demos
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 136–147
Mathur A, Newe T, Elgenaidi W, Rao M, Dooly G, Toal D (2017) A secure end-to-end iot solution. Sensor Actuat A-Phys 263:291–299
Mayer R, Koldehofe B, Rothermel K (2015) Predictable low-latency event detection with parallel complex event processing. IEEE Internet Things J 2(4):274–286
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):74–82
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):1–24
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 40–51
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 49–49
Rahmani MA, Preden L-SP, Jantsch A (2018) Fog Computing in the Internet of Things. Springer International Publishing
Richerzhagen B, Koldehofe B, Steinmetz R (2015) Immense Dynamism. German Res 37:24–27
Samulat P (2017) Die Digitalisierung der Welt: Wie das Industrielle Internet der Dinge aus Produkten Services macht. Springer Fachmedien Wiesbaden, Wiesbaden, pp 103–124
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 348–355
Simmhan Y (2018) Big Data and Fog Computing. Springer International Publishing, Cham, pp 1–10
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 83–90
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 1–34
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 140–151
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 775–786
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Luthra, M., Koldehofe, B. & Steinmetz, R. Transitions for Increased Flexibility in Fog Computing: A Case Study on Complex Event Processing. Informatik Spektrum 42, 244–255 (2019). https://doi.org/10.1007/s00287-019-01191-0
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00287-019-01191-0