ABSTRACT
With the increasing popularity of the Internet-of-Things (IoT), organizations are revisiting their practices as well as adopting new ones so they can deal with an ever-growing amount of sensed and actuated data that IoT-compliant things generate. Some of these practices are about the use of cloud and/or fog computing. The former promotes "anything-as-a-service" and the latter promotes "process data next to where it is located". Generally presented as competing models, this paper discusses how cloud and fog could work hand-in-hand through a seamless coordination of their respective "duties". This coordination stresses out the importance of defining where the data of things should be sent (either cloud, fog, or cloud&fog concurrently) and in what order (either cloud then fog, fog then cloud, or fog&cloud concurrently). Applications' concerns with data such as latency, sensitivity, and freshness dictate both the appropriate recipients and the appropriate orders. For validation purposes, a healthcare-driven IoT application along with an in-house testbed, that features real sensors and fog and cloud platforms, have permitted to carry out different experiments that demonstrate the technical feasibility of the coordination model.
- M. Aazam and E.N. Huh. 2014. Fog Computing and Smart Gateway Based Communication for Cloud of Things. In Proceedings of FiCloud'2014. Barcelona, Spain. Google ScholarDigital Library
- M.R. Abdmeziem, D. Tandjaoui, and I. Romdhani. 2016. Architecting the Internet of Things: State of the Art. In Robots and Sensor Clouds, Anis Koubaa and Elhadi Shakshuki (Eds.). Springer International Publishing.Google Scholar
- P.M. Barnaghi and A.P. Sheth. 2016. On Searching the Internet of Things: Requirements and Challenges. IEEE Intelligent Systems 31, 6 (2016). Google ScholarDigital Library
- F. Bonomi, R. Milito, P. Natarajan, and J. Zhu. 2014. Fog Computing: A Platform for Internet of Things and Analytics. In Big Data and Internet of Things: A Roadmap for Smart Environments, Studies in Computational Intelligence. Cisco, Springer International Publishing.Google Scholar
- D.A. Chekired, L. Khoukhi, and H.T. T. Mouftah. 2018. Industrial IoT Data Scheduling Based on Hierarchical Fog Computing: A Key for Enabling Smart Factory. IEEE Trans. Industrial Informatics 14, 10 (2018).Google ScholarCross Ref
- DZone. https://dzone.com/guides/iot-applications-protocols-and-best-practices, 2017 (visited in May 2017). The Internet of Things, Application, Protocls, and Best Practices. Technical Report.Google Scholar
- H. Gupta, A.V. Dastjerdi, S.K. Ghosh, and R. Buyya. 2016. iFogSim: A Toolkit for Modeling and Simulation of Resource Management Techniques in Internet of Things, Edge and Fog Computing Environments. CoRR abs/1606.02007 (2016).Google Scholar
- G. Lewis, S. Echeverría, S. Simanta, B. Bradshaw, and J. Root. 2014. Tactical Cloudlets: Moving Cloud Computing to the Edge. In Proceedings of MILCOM'2014. Baltimore, USA. Google ScholarDigital Library
- Logicworks. September 2016 (checked out in April 2017). Why Vendor Lock-In Remains a Big Roadblock to Cloud Success. www.cloudcomputing-news.net/news/2016/sep/01/vendor-lock-in-is-big-roadblock-to-cloud-success-survey-finds.Google Scholar
- X. Masip-Bruin, E. Marín-Tordera, G. Tashakor, A. Jukan, and G.J. Ren. October 2016. Foggy Clouds and Cloudy Fogs: A Real Need for Coordinated Management of Fog-to-Cloud Computing Systems. IEEE Wireless Communications 5, 23 (October 2016). Google ScholarDigital Library
- A. Meola. (last checked out October 2017) October 2016. The Critical Role of Infrastructure in the Internet of Things. uk.businessinsider.com/internet-of-things-infrastructure-architecture-management-2016-10.Google Scholar
- I. Petri, J. Diaz-Montes, O. Rana, Y. Rezgui, M. Parashar, and L.F. Bittencourt. 2015. Coordinating Data Analysis & Management in Multi-Layered Clouds. In Proceedings of CN4IoT'2015. Rome, Italy.Google Scholar
- J.W. Rittinghouse and J.F. Ransome. 2009. Cloud Computing: Implementation, Management, and Security. Taylor & Francis. Google ScholarDigital Library
- M. Satyanarayanan, P. Bahl, R. Cáceres, and N. Davies. 2009. The Case for VM-based Cloudlets in Mobile Computing. IEEE Pervasive Computing 8, 4 (2009). Google ScholarDigital Library
- A. Taivalsaari and T. Mikkonen. 2017. A Roadmap to the Programmable World: Software Challenges in the IoT Era. IEEE Software 34, 1 (2017). Google ScholarDigital Library
- B. Varghese, N. Wang, D.S. Nikolopoulos, and R. Buyya. 2017. Feasibility of Fog Computing. arXiv preprint arXiv:1701.05451 (2017).Google Scholar
- M. Weiser. 1999. The Computer for the 21<sup>st</sup> Century. Newsletter ACM SIGMOBILE Mobile Computing and Communications Review 3, 3 (1999). Google ScholarDigital Library
- Z. Wen, R. Yang, P. Garraghan, T. Lin, J. Xu, and M. Rovatsos. March-April 2017. Fog Orchestration for Internet of Things Services. IEEE Internet Computing 21, 2 (March-April 2017). Google ScholarDigital Library
- M. Yannuzzi, R. Milito, R. Serral-Gracià, D. Montero, and M. Nemirovsky. 2014. Key Ingredients in an IoT Recipe: Fog Computing, Cloud Computing, and more Fog Computing. In Proceedings of CAMAD'2014. Athens, Greece.Google Scholar
Index Terms
- Towards a seamless coordination of cloud and fog: illustration through the internet-of-things
Recommendations
A Microservice-Based Industrial Control System Architecture Using Cloud and MEC
Edge Computing – EDGE 2020AbstractCloud computing has been adapted for various application areas. Several research projects are underway to migrate Industrial Control Systems (ICSs) to the public cloud. Some functions of ICSs require real-time processing that is difficult to ...
Is your cloud elastic enough?: performance modelling the elasticity of infrastructure as a service (IaaS) cloud applications
ICPE '12: Proceedings of the 3rd ACM/SPEC International Conference on Performance EngineeringElasticity, the ability to rapidly scale resources up and down on demand, is an essential feature of public cloud platforms. However, it is difficult to understand the elasticity requirements of a given application and workload, and if the elasticity ...
Enabling Workload Engineering in Edge, Fog, and Cloud Computing through OpenStack-based Middleware
Special Issue on Fog, Edge, and Cloud IntegrationTo enable and support smart environments, a recent ICT trend promotes pushing computation from the remote Cloud as close to data sources as possible, resulting in the emergence of the Fog and Edge computing paradigms. Together with Cloud computing, they ...
Comments