The CPaaS.io platform allows to make task logic - e.g., for analytics - to be adaptively moved from the cloud to the edge of an IoT network. This presentation given at the first year review meeting in Tokyo on October 5, 2017 explains how. Disclaimer: This document has been produced in the context of the CPaaS.io project which is jointly funded by the European Commission (grant agreement n° 723076) and NICT from Japan (management number 18302). All information provided in this document is provided "as is" and no guarantee or warranty is given that the information is fit for any particular purpose. The user thereof uses the information at its sole risk and liability. For the avoidance of all doubts, the European Commission and NICT have no liability in respect of this document, which is merely representing the view of the project consortium. This document is subject to change without notice.

1. City Platform as a Service – Integrated and Open
WP 4: Cloud & Edge Programming
Toshihiko Yamakami (ACC), Bin Cheng (NEC)
Year 1 Review Meeting, Tokyo
October 5, 2017

2. Overview
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WP 7: Impact Generation
WP 3:
Platform Architecture
WP 4: Cloud & Edge Programming
WP 5: Citizen Empowerment for Data Privacy
WP 6: Holistic Data Management & Governance
WP 8: Project Management
WP 2: Use Cases & Trials
WP 1: Ethics Requirements

3. Involved Participants
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4. Background
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Cloud-edge infrastructure
huge cloud(s)
(public/private cloud)
light edge
(IoT device)
 Low end-to-end latency
 Low bandwidth consumption
 Better privacy-preserving
Technical benefits of edge computing
heavy edge
(IoT Gateway)

5. 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

6. Roadmap in WP4
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EU
focus
Center
Cloud(s)
Heavy edges
Light-weight edges
Japan
focus
Use case analysis
(application requirements)
"Bottom-Up Approach“
"Application"/ "Service" First
Abstracting & Modeling
(programming model)
Mechanism
(framework)
Optimization
(framework)
Use case validation
(applications)

7. Main Results
 Objective 1: task profile specification T4.2
 Cloud-edge programming model for service/application developers
 Objective 2: cloud-edge orchestration mechanism  T4.1
 Hybrid service orchestrator between cloud and edges
 Binary migration between cloud and light-weight edges
 Objective 3: Optimization of task deployment  T4.3
 Locality-aware task deployment
 Objective 4: use case validation
 Implement some application examples for the demo
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NO deliverable in WP4 yet; first one will come in December, 2017

8. Results in Year 1
Part 1: Cloud-edge computing between cloud and heavy edges
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heavy edges: support docker virtualization and run a number of dockerized tasks
The result to be seen in the FogFlow demo

9. Cloud-Edge Programming Model
 Design goal
 Easy sharing & reusing of intermediate data across various
tasks/services/domains
 Capability of linking multiple dockerized micro-services together
 Ease of use
 FogFlow programming model (focus on service orchestration)
 standard-based: better interoperability, easy integration
 scope-based: locality aware, mobility aware
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10. FogFlow Architecture
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service
topology
Global
Orchestrator
(in the Cloud)
Local
Orchestrator
(at edges)
FogFlow
Service
Orchestrator
Context
Producer
Context
Consumer
task
instances
task
instances
requirement
service
operator
service
developer
docker
images
Distributed context management
Network of brokers
(NGSI10)
IoT
Discovery
(NGSI9)

11. Core Concept of FogFlow
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Service Topology Execution Plan Deployment Plan
cloud
edge1
edge2
Task
generation
Task
specification
Task
deployment
Cloud-Edge
Programming model
Mechanism &
optimizations

12. Service Topology
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Graphic Editor for Task Graph

13. Task Specification
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name: AnomalyDetector
operator: anomaly
groupBy: shopID
input_streams:
- type: PowerPanel
shuffling: unicast
scoped: true
- type: Rule
shuffling: broadcast
scoped: false
output_streams:
- type: Anomaly
Task Profile
distributed context management system
notify
subscribe
update

14. Cloud-Edge Service Orchestration
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Service Topology
Execution Plan
Deployment Plan
cloud
edge1edge2
 Expected output
 Scope
 scheduler
locality aware deployment
dynamic execution graph
Orchestration
requirement

15. Distribute Context Management
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Context
Entities
Context Availability
(indexing, discovery)
IoT Devices (producer/consumer)
D D D
B B B B B
updates
D1
Temperature sensor
(context provider)
D2 D2
IoT
Discovery
network of brokers High throughput and low latency
Reliability of message delivery

16. Use Case Example: Anomaly Detection
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17. Dissemination
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FogFlow paper published by IoT-J
To be open source FIWARE GE soon

18. Results in Year 1
Part 2: cloud-edge cooperation between cloud and light-weight edges
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light-weight edges: provide the execution environment to run certain binary tasks migrated from cloud
The result to be seen in the demo of
IoT Aggregator  IoT Engine

19. Basic Architecture of Cloud-Edge Cooperation
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Basically
"Light-weight-
node" approach
TRON is a small device platform
This is the strong point of u2 architecture

20. Basic Architecture of Cloud-Edge Cooperation
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Access Policy
Cloud
Real World
Owner of Device Third Party
without access privileges
Device Real Object
Device Virtual Object
Authorized
device
virtual objects

21. IoT-Engine
21
Standard embedded
computer platform for
IoT devices and sensors

22. 22
Medical Streaming Data may
need latency and privacy
requirements
IoT –Aggregator will provide IoS in the cloud with light-weight IoT engines.
Some use case needs edge-node-level requirements.
Use Case Example (Yokosuka Emergency Medical Care)

23. Issues for Cloud-Edge migration
Load distribution between cloud and edge
Security, Secret, Privacy
 Who owns what data?
 Data cannot be disclosed to other organizations.
Performance
 Throughput (average performance, load balancing)
 Response/realtime performance
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24. Classification of Module Migration
Dynamic
(Realtime, Automatic)
Static
(Planned, Semi-Automatic)
High-Level Module
Migration
(Heavy Edge)
N.A. N.A.
Binary Module
Migration
(Light-weight edge)
N.A. u2
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•"VMs", "dockers", ..., are not suitable for light-weight nodes.
•Source code level compatibility(Re-Compile  planned migration)
•Standardization of light-weight nodes of embedded systems for IoT use
(IoT-Engine + μT-Kernel + ...)

25. IoT Engine
Participating Semiconductor Manufacturers
 Toshiba Microelectronics Corporation
 Renesas Electronics Corporation
 Cypress Semiconductor Corporation
 Imagination Technologies Limited
 Nuvoton Technology Corporation
 NXP Semionductors N.V.
 STMicroelectronics
IoT-Engine Development Kits
 Personal Media Corporation
 Ubiquitous Technologies Corporation
(C) 2017 YRP UNL and TRON Forum, All
Rights Reserved.
25

26. Migration Framework
Task Requirement:
CPU, memory,
bandwidth, latency
Execution
Environment:
CPU, memory,
bandwidth, latency,
Location, mobility
Migration plan
(Scheduling)
Migration and
Distributed
Execution
Protocol
Initiator, Planner
Programming
Model
•Research focus "execution environment“
•Task migration mechanism for diverse light-weight nodes (various CPUs)

27. Plan for Year 2
outlook
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28. WP4 Outlook
 Extended programming model for cloud-edge computing
 Metadata-driven task orchestration
 Algorithms for optimization
 Task migration mechanisms
 Mobility awareness
 System integration
 Use case aspect: smart parking (NEC is working with Odins)
 Platform aspect:
• FIWARE FogFlow  u2 IoT Aggregator;
• security enhancement
 Deliverables and standardization contribution to OpenFog/ETSI MEC
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29. Gracias Mulțumesc 謝謝 Paldies Eskerrik asko Dziękuję Mahalo ‫תודה‬ Go raibh maith agat спасибо Grazzi आभारी
Xin cảm ơn 감사합니다 நன்றி Köszönöm ‫مرسي‬ Ndiyabulela Grazia Tak Благодаря Aitäh Terima kasih Děkuji
Asante Diolch ‫شكرا‬ Takk Ďakujem Gràcies Kiitos Obrigado Teşekkür ederim Ngiyabonga Þakka þér Grazas
Tapadh leibh ขอบคุณ Faleminderit Ačiū Danke Merci Grazie Hvala Ευχαριστώ Dankon Tack Dank je Grazcha
…
Thank You
ありがとう
This document has been produced in the context of the CPaaS.io project which is jointly funded by the European
Commission (grant agreement n° 723076) and NICT from Japan (management number 18302). All information provided
in this document is provided "as is" and no guarantee or warranty is given that the information is fit for any particular
purpose. The user thereof uses the information at its sole risk and liability. For the avoidance of all doubts, the European
Commission and NICT have no liability in respect of this document, which is merely representing the view of the project
consortium. This document is subject to change without notice.
CPaaS.io - Consortium Confidential 29