Presentation by Bin Cheng Senior Researcher, NEC Laboratories Europe GmbH FIWARE Global Summit 21-22 May 2019 - Genoa, Italy

1. FogFlow GE: Intent-based Edge Programming
Bin Cheng (bin.cheng@neclab.eu)
NEC Labs Europe

2. Agenda
§ Overview of FogFlow GE
• motivation, value proposition, major design, key features, system
architecture
§ Intent-based edge programming model in FogFlow
• Core concepts and Intent model
• Service topology
• Fog function: serverless edge computing
§ Use cases
• anomaly detection, lost child finder, smart parking
§ Next Steps
• Integration with Unikernel-based task provisioning (Unikraft)
1

3. 2
Part I: Overview of FogFlow

4. Motivation of FogFlow GE
3
Lower Complexity
Lower Cost
Higher Efficiency
Smart
Cities
Public
Safety
Smart
Factories
IoT services
difficult to program
Infrastructure
difficult to manage
Edges
IoT devices
not able to be
smart
After using NEC FogFlow
Edge Computing Framework
Lack of model
High complexity from
heterogeneity and
dynamics
No fast, closed control
loop
Development + Operation
Development + Operation
Faster response time, low
bandwidth usage,
better scalability, high reliability

5. Value Proposition of FogFlow
4
IoT Service
providers
Infrastructure
Providers
Device
Providers
Intent-based edge
programming
Fast control loop
Optimized
orchestration
Low
learning cost
Low
OPEX
Fast
time-to-market
Improved
QoS

6. FogFlow GE: Context-driven Cloud-Edge Orchestration
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, data context, and usage context)
Producers
(sensors)
Consumers
(actuators)
cloud
edge edge edge
raw context information
timely results fast actions
FogFlow dynamic
processing flows
Data context
System context Usage context

7. System View of FogFlow
6
Topology
master
IoT
devices
Discovery
Applications/Services
Task
designer
Service developers
System operators
FIWARE NGSI
Non-NGSI
Worker(s)Worker(s)
Docker
Registry
Data Processing Layer
Other
data source(s)
NGSINon-NGSI
Context Management Layer
IoT Device Layer
Broker(s)
Application/Service Layer
Service Orchestration Layer

8. Deployment View of FogFlow
7
Docker
Registry
Core components
IoT Broker
(cloud)
IoT
Discovery
PostgreSQL Topology
Master
RabbitMQ
IoT
Broker
(edge)
Worker
docker engine
edge node 2
(IP-registry)
Task
instances
Task
Designer
back-end part
cloud node
Worker
IoT
Broker
(edge)
Worker
docker engine
Task
instances
edge node 1
data channel
control channel

9. How It Works
8
Template of your IoT Service Execution Plan Deployment Plan
cloud
edge1
edge2
Service topology
Cloud-Edge
Programming model
Cloud-Edge Orchestration
Graphical
editor
Automation &
optimizations
Cloud-Edge
Environment
IoT
Devices
Usage
Context
Data/System
Context

10. Key Features (1): Agile Intent-based Edge Programming
9
Reusable
building blocks
Compose them with declarative hints (input, output, groupby, ..)
Submit
Select
Your IoT service is
ready and deployed
to the cloud and
edges in minutes
Programming Cloud and Edges easily and fast

11. Key Features (2): Automated and Optimized Orchestration for
Cloud-Edge
10
Connected
Device
Backend
Cloud(s)
Other
Edges
Sensing
Notify
Orchestrating
Collaborative
deployment
Closed control loop
Dynamic processing flows
Reacting
Nearby
Edge
Collaborative
deployment
Orchestrating and managing dynamic processing flows over cloud
and edges in an efficient and optimized manner

12. Key Features (3): Standard-based Ecosystem: enabling data-
driven ecosystem with open data model and APIs
11
Domain C
(other FIWARE systems)
Domain B
(Public safety)
Open data model
Open APIs
Vendor-neutral
Marketplace of
data-driven apps
NGSI
NGSI
NGSI
FogFlow
deployment B
Domain A
for smart cities
FogFlow
deployment A
NGSI-LD

13. 12
Part II: Intent-based Edge Programming in FogFlow

14. Core Concepts: operator, task, service topology, intent, fog
function
13
Task A
Operator
(e.g.,
Avg)
A B C
Intent
Task B
Service topology
A special, common case
A task: annotated operator with the configuration
on how to parallelize the task and shuffle their
input data
Operator plus the annotation on how to
parallelize the task and shuffle their input data
Docker images
Implementation of the operator
Intent
Intents
Intent-based programming model
Task: it means annotated task specification at the design phase; it means task instances (running
data processing tasks in a docker container)
Fog function
A Intents

15. Intent-based Edge Programming Model
14

16. Context-driven Orchestration
15
FogFlow
Orchestrator
service
topology
IoT
Discovery
Docker
registr
y
issue
Subscribe
Notify
Actions
launch/terminate instance
add/remove input
Fetch docker images
Orion
Cloud-based
FIWARE Ecosystem 1
2
3
4
5
6
7
7
device layer
Cloud-edge computing
environment
Service orchestration layer
Intent
0 Define a service topology

17. Orchestration Towards User-definable Intents
16
Service Topology
Execution Plan
Deployment Plan
cloud
edge1edge2
q Service topology
q Scope
q SLO: service level
objective
q priority
locality aware deployment
dynamic execution graph
Intent

18. Development Process
17
NGSI
applications
Operators
docker image
Tasks
Service
Topology
Execution plan
Deployment plan
intent
define a service
topology
implement operators
issue an intent
These two steps are combined as a
single step if you use the fog function
programming model

19. 18
Part III: Use Cases

20. Use Case 1: Anomaly Detection in Shops
19
Retail stores at different locations
Cloud
Dashboard
app
PowerPanel 1
Alarm
Anomaly
events
New rules for
anomaly detection
anomaly events
PowerPanel 2
Raspberry PI

21. Screenshot of the designed service topology
20

22. Define an Intent Object to trigger this service topology
21

23. Use Case 2: Lost Child Finder
22
Face
matching
Virtual sensors
(cameras)
groupby “cameraID”
Subscribe
broadcast Picture of the
lost child
Stadium A Stadium B
Cloud
IoT
gateway
Terminal
gateway
IoT
gateway
Picture of
the lost child
Notifications
Terminal
gateway
Terminal
gateway
Terminal
gateway

24. Use Case 3: Smart Parking
23
Connected
car
private
site
Public
site
Real-time
estimation
Prediction
Arrival time
Recommender
Utilizing NGSI API to
access digital twins
Virtual Entities: Public parking sites,
private parking sites, connected cars

25. 24
Part IV: Next Steps

26. Ongoing Activities for FogFlow GE
§ Decentralized orchestration for better scalability and reliability
• IoT Discovery: from centralized to decentralized
• Topology Master: from centralized to decentralized
§ Advanced algorithms to achieve the requested SLO (service level objective)
• Explore advanced algorithms to make better decisions in order to achieved high level
SLO and continuously adjust the deployment plan towards SLO
§ Integration with Unikernel-based task provision (NEC UniKraft)
§ Adaption to NGSI-LD
25

27. Decentralized Orchestration (design)
26

28. Decentralized Orchestration (preliminary results)
27
0
20
40
60
80
100
120
140
160
180
200
1 2 3
numberoftasks/second
throughput of creating tasks
distributed central

29. Motivation to use unikernel
28
Docker images could be too big and take
time to fetch and launch them
Launching a docker
container takes 2~3
seconds
For time-critical IoT services, we have
to launch dockerized operators faster
and more efficiently

30. Unikraft:Unikernels Made Operators Fast and Efficient
29

31. FogFlow + Unikraft
30
FogFlow
programming model
FogFlow
System
FogFlow
EdgeNode
Docker API
FogFlow
Operator
Unikraft
Libraries
Unikraft

32. Thank you!

33. ACKNOWLEDGMENT
This project has received funding from the European Union’s Horizon 2020 research
and innovation programme under grant agreement No 814918
and by Japan’s Ministry of Internal Affairs and Communications (MIC). Responsibility
for the information and views set out in the document lies entirely with the authors.