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FIWARE Tech Summit - FogFlow - New GE for IoT Edge Computing


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Presentation by Bin Cheng
Senior Researcher, NEC Laboratories Europe

FIWARE Tech Summit
28-29 November, 2017
Malaga, Spain

Published in: Technology
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FIWARE Tech Summit - FogFlow - New GE for IoT Edge Computing

  1. 1. FIWARE at the edge: FogFlow, a new GE for IoT edge computing Bin Cheng (, Ernö Kovacs ( NEC Labs Europe
  2. 2. Background: edge computing  Cloud computing • Centralized, elastic and powerful resources, good transparency  Internet of things • Contextual data are constantly generated and to be used at edges • Many IoT services required the closed loop of sensing, analyzing, decision-making, and reacting; fast response time; automated workload management • Cloud-only based architecture is not enough to meet service requirements for IoT, due to inefficient bandwidth consumption, latency limit, privacy concerns  New trend: IoT edge computing • Outsourcing more processing close to data • Utilize both cloud computing and edge computing in a transparent manner • Low latency, reduced bandwidth consumption, better privacy preserving
  3. 3. Motivation 2 edge computing has great potential to reduce bandwidth consumption and end-to-end latency, but it raises much more complexity than cloud computing since the cloud-edge environment is more open, heterogeneous, and dynamic Can we program applications over cloud-edges easily, like programming them in the cloud? Can we let the cloud-edge platform to automatically manage and optimize its own resources under such dynamics? Complicate to realize services due to lack of programming model and poor interoperability: spend months for each service service/application providers No approach of dealing with dynamics like device mobility, instant service usage, temporary failure: applications have to face those issues service realization during the development phase resource management during the deployment phase new services New requirements come frequently
  4. 4. What Is FogFlow (1): Cloud-Edge Orchestrator  FogFlow is a cloud-edge orchestrator to orchestrate dynamic NGSI-based data processing flows on-demand between producers and consumers for providing timely results to make fast actions, based on context (system context and data context) Producers (sensors) Consumers (actuators) cloud edge edge edge raw context information timely results fast actions FogFlow dynamic processing flows Data context System context
  5. 5. What Is FogFlow (2): High Level View 4
  6. 6. Benefits from FogFlow  For IoT service developers • Fast time to market, less development effort  For IoT service operators • Easy management with fast deployment and upgrade  For platform providers • Efficient usage of infrastructure resources • Low operation cost (reduced bandwidth consumption)  For service users (devices or application users) • Better QoS (low latency, fast response time) 5
  7. 7. Key Feature: Context Aware Cloud-Edge Orchestration 6 Data Context and availability (metadata, availability) System Context (locality, mobility, capacity, security, …) Programming model with graphical editor
  8. 8. Relation with Other GEs 7 Distributed Context Management System OrionProcessing tasks FogFlow subscription APPS Other GE(s) sensors actuators FogFlow Dashboard notify e.g. Cygnus non-NGSI devices Adapter(s) (e.g., openMTC, IoT Agent)
  9. 9. Current Status  Version 1.0 is released as open source at Github, under BSD-4 license •,  Approved as FIWARE GE  Detailed tutorial is available, with examples •  Being used in both EU projects and internal NEC use cases • SMARTIE for “smart building” use case • for “smart parking” use case • Internal use cases: anomaly detection, lost child finding  Technical paper published at IEEE Internet of Things Journal with open access  Demonstrations at iEXPO in Tokyo & Smart City Expo World Congress in Barcelona 8
  10. 10. Roadmap  Take further actions to • More use case examples • Webinar • Necessary documents as FIWARE GE • Support quality test  Develop new features • Security enhanced processing flows • Mobility aware task migration • Autonomous management  Apply FogFlow for the FIWARE implementation of Industrial Data Space 9
  11. 11. 10 How to Use FogFlow
  12. 12. System Deployment 11
  13. 13. FogFlow Dashboard 12
  14. 14. Development Process 13
  15. 15. Use Case Example 14
  16. 16. Designing a IoT Service 15
  17. 17. Developing Dockerized Operators 16
  18. 18. Defining Your Service Topology 17
  19. 19. Triggering Your IoT Service 18 Service Topology Execution Plan Deployment Plan cloud edge1edge2  Expected output  Scope  scheduler locality aware deployment dynamic execution graph Orchestration requirement
  20. 20. Using The Results Generated from Your IoT Service 19 Consumer (Alarm2) Consumer (City Operation Center) Consumer (Alarm1) subscribe notify subscribe notify Broker (edge) Broker (cloud) Broker (edge) FogFlow Context Management System IoT Discovery subscribe
  21. 21. References 20 FogFlow paper published by IoT-J Online tutorial: http://fogflow.readthedocs.ioB. Cheng, G. Solmaz, F. Cirillo, E. Kovacs, K. Terasawa and A. Kitazawa, “FogFlow: Easy Programming of IoT Services Over Cloud and Edges for Smart Cities,” in IEEE Internet of Things Journal
  22. 22. Acknowledgement  This work has been partially funded by the European Union’s Horizon 2020 research and innovation programme within the project under Grant Agreement No. 723076 21
  23. 23. Thank you! Follow @FIWARE on Twitter