fog computing framework with agile programming models. It allows IoT service providers to easily design and implement their services, meanwhile automatically launching dynamic data processing flows over cloud and edges in an optimized manner.

1. FIWARE at the edge:
FogFlow, a new GE for IoT edge computing
Bin Cheng (bin.cheng@neclab.eu),
Ernö Kovacs (ernoe.kovacs@neclab.eu)
NEC Labs Europe

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. Motivation
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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. 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. What Is FogFlow (2): High Level View
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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)
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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. 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. Current Status
 Version 1.0 is released as open source at Github, under BSD-4 license
• https://github.com/smartfog/fogflow,
 Approved as FIWARE GE
 Detailed tutorial is available, with examples
• http://fogflow.readthedocs.io
 Being used in both EU projects and internal NEC use cases
• SMARTIE for “smart building” use case
• CPaaS.io 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
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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
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11. 10
How to Use FogFlow
http://fogflow.readthedocs.io/

12. System Deployment
11

13. FogFlow Dashboard
12

14. Development Process
13

15. Use Case Example
14

16. Designing a IoT Service
15

17. Developing Dockerized Operators
16

18. Defining Your Service Topology
17

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. Using The Results Generated from Your IoT Service
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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. 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. Acknowledgement
 This work has been partially funded by the European Union’s Horizon 2020 research
and innovation programme within the CPaaS.io project under Grant Agreement No.
723076
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23. Thank you!
http://fiware.org
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