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HINC – Harmonizing Diverse Resource
Information Across
IoT, Network Functions and Clouds
Duc-HungLe, Nanjangud Narendra, Hong-Linh Truong
Distributed Systems Group, TU Wien
truong@dsg.tuwien.ac.at
http://dsg.tuwien.ac.at/staff/truong
FiCloud 2016, Vienna, 24th August 2016 1
Outline
 Background and motivation
 HINC framework
 Distributed resource information model
 Architecture and implementation
 Testbed and experiments
FiCloud 2016, Vienna, 24th August 2016 2
Background - Systems
3
Analysis &
management
Hot deploy
Control
Re-route
FiCloud 2016, Vienna, 24th August 2016
Background – elastic service models
 Cloud service models
 Networks
 Network function
virtualization
 Pay-per-use IoT
communication
 IoT
 Fixed IoT infrastructures
 On-demand IoT
 Human participation
(sensing and analytics)
FiCloud 2016, Vienna, 24th August 2016 4
https://arrayofthings.github.io/
http://www.sktelecom.com/en/press/detail.do?idx=1172
Background - application scenarios
 Emergency responses, on-demand crowd sensing, Geo
Sports monitoring, cyber-physical systems testing, etc.
FiCloud 2016, Vienna, 24th August 2016 5
 Need to have an end-to-end provisioning of resources
 E.g., sensors, network function services, storage, virtual machines
 Short, crucial and heavily workload; elasticity and uncertainties.
Geo Sports: Picture courtesy
Future Position X, SwedenIndian Overfly collapses
figure source: http://timesofindia.indiatimes.com
Motivation – End-to-End resource
slice provision
6
Emergency
response
Hospital &
traffic
Emergency
response
service
Early
treatment
protocol
Best route
to the
hospital
Most
suitable
hospital
- Wearables
- Mobie medical
equipment
- First aid info.
- Vehicle capability
- Location
- Hospital capability
- Traffic status
Victims
Distributed
resource
management
IoT resource
provisioning
Dedicate
sub-network
Coordinate
operations
FiCloud 2016, Vienna, 24th August 2016
Motivation – End-to-End resource
slice provisioning
FiCloud 2016, Vienna, 24th August 2016 7
End-to end
Resource slice
CPS Applications/Virtual
infrastructures
http://sincconcept.github.io/
This paper: harmonize resource information from
IoT, network functions and cloud providers for
resource slice provisioning
Examples of existing
providers/models
Provider Category APIs Information models
FIWare Orion IoT RESTful (NGSI10), one-time
query or subscription
High level attributes on
data and context
FIWare IDAS IoT RESTful for read/write custom
models and assets
Low level resource
model catalogs
IoTivity IoT REST-like OIC protocol, support
C++, Java and JavaScript
Multiple OIC model
OpenHAB IoT RESTful for query and control
IoT resources
Low level resource
model catalogs
OpenDayLight Network Dynamic REST generated from
Yang model (model-driven)
Low level resource
model catalogs
OpenBaton Network RESTful for network service
description
ETSI MANO v1.1.1 data
model
OpenStack Cloud RESTful, multiple language via
SDK, OCCI, CIMI
OpenStack model,
OCCI, CIMI
8
FiCloud 2016, Vienna, 24th August 2016
Approach
9
FiCloud 2016, Vienna, 24th August 2016
• Avoid top-down
• Design a “super” model to manage the world.
• Focus and suitable for single-purpose solution.
• Bottom-up
• Let providers use their own models.
• Integration and link diverse types of information.
• Adaptor: to interface with providers’ APIs.
• Transformer: integrate our distributed resource model.
• Focus on resource relationships across IoT, Network
functions and clouds.
Information model
Physical: Sensor/actualtor/devices in providers’ models
Virtual IoT: SD-Gateway and capabilities.
Network functions: edge-to-edge, edge-to-cloud network.
Clouds: VM, data services, data analytics.
10
FiCloud 2016, Vienna, 24th August 2016
Resource information integration
• The model aims to be extensible to cater
multiple underlying devices and services.
• To cope with the rapidly increasing of systems.
• A process to interface with resource providers.
11
FiCloud 2016, Vienna, 24th August 2016
Examples
FiCloud 2016, Vienna, 24th August 2016 12
"data": {
"DeviceProps": {
"commandURL": "http://...OpenIoT/..",
"lastIP": "195.97.103.225",
"commands": true },
"asset": {
"name": "00:3b:B6:BodyTemperature",
"description": "asset model protocol" },
"model": "SENSOR_TEMP",
"registrationTime": "2015-04-16T15:39:58Z",
"status": "Active",
"sensorMetaData": [
{"ms": {
"dataType": "BodyTemperature",
"unit": "Celsius",
"rate": "10" }
}]}
{
"type": "LocationItem",
"link": "http://..../rest/items/DemoLocation"
}
Resource from OpenHAB
- Simple data format.
- A link for more information.
- Information is static.
- Complex data format.
- Have control capability.
- More meta data.
A resource from OpenIOT
Examples
FiCloud 2016, Vienna, 24th August 2016 13
"SoftwareDefinedGateway":{
"uuid": "5a60...",
"name": "gateway1",
“datapoints": [
{ “name": “Temp1",
"datatype": "BodyTemerature",
"measurementUnit": "Celsius",
"resourceID":
"00:3b:B6:BodyTemperature",
"extra": [
<imported List 1 and List 2> ...}
], },
“controlpoints”: [
{ "name": "changeRate",
"resourceID": "00:3b:B6:BodyTemperature",
"description": "change sensor rate",
"reference":"http://.../OpenIoT/assets/..",
} ], }
Virtual IoT resource information
- Software-Defined Gateway
wraps a set of capabilities.
- Data Point extracts a set of
interesting attributes for
higher level management.
- Control Point contains a
reference to the provider
API for controlling
resources.
Architecture
14
Global management
service
- Run on users’ site.
- Coordinate Local
Management Service.
- Manage relationships.
Local management
service
- Deployed on gateway or
network station.
- Interface with provider.
- Transform information.
FiCloud 2016, Vienna, 24th August 2016
Prototype
15
FiCloud 2016, Vienna, 24th August 2016
http://sincconcept.github.io/HINC/
Testbed
16
FiCloud 2016, Vienna, 24th August 2016
Testbed
17
FiCloud 2016, Vienna, 24th August 2016
In-lab testbed:
- Server: 8 CPU-i7, 3.60GHz, 32GB RAM
- Edge: 100 dockers with emulated sensors + gateways.
- Network: virtual routers (https://www.weave.works/)
- Cloud: event processing.
Testbed
18
FiCloud 2016, Vienna, 24th August 2016
Distributed testbed
- Edge: physical/virtual machines on different cities.
- Communication: CloudAMQP.
- Cloud: AmazonEC.
Reducing complexity in accessing
and control resources
19
FiCloud 2016, Vienna, 24th August 2016
1. Query data points
2. Control the
resource
3. Query network
functions and clouds
Query time by number of gateways
Distributed sites
testbed
FiCloud 2016, Vienna, 24th August 2016 20
In-lab testbed
Gateway’s response time variability
FiCloud 2016, Vienna, 24th August 2016 21
Distributed sites
testbed
In-lab testbed
Number of sensors and locations
FiCloud 2016, Vienna, 24th August 2016 22
Query time from
distributed sites
Query time by
number of sensors,
distributed sites
Conclusion and future work
 Harmonizing information in 3 dimensions:
 High-level view of low level resources
 End-to-end view of IoT, network functions and clouds
 Large-scale view of highly distributed sites
 Future work:
 Information-centric resource provisioning
 Dynamic IoT infrastructure configuration
 End-to-end resource optimization
23
FiCloud 2016, Vienna, 24th August 2016
Thanks for your
attention!
Questions?
Hong-Linh Truong
Distributed Systems Group
TU Wien
dsg.tuwien.ac.at/staff/truong
FiCloud 2016, Vienna, 24th August 2016 24

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HINC – Harmonizing Diverse Resource Information Across IoT, Network Functions and Clouds

  • 1. HINC – Harmonizing Diverse Resource Information Across IoT, Network Functions and Clouds Duc-HungLe, Nanjangud Narendra, Hong-Linh Truong Distributed Systems Group, TU Wien truong@dsg.tuwien.ac.at http://dsg.tuwien.ac.at/staff/truong FiCloud 2016, Vienna, 24th August 2016 1
  • 2. Outline  Background and motivation  HINC framework  Distributed resource information model  Architecture and implementation  Testbed and experiments FiCloud 2016, Vienna, 24th August 2016 2
  • 3. Background - Systems 3 Analysis & management Hot deploy Control Re-route FiCloud 2016, Vienna, 24th August 2016
  • 4. Background – elastic service models  Cloud service models  Networks  Network function virtualization  Pay-per-use IoT communication  IoT  Fixed IoT infrastructures  On-demand IoT  Human participation (sensing and analytics) FiCloud 2016, Vienna, 24th August 2016 4 https://arrayofthings.github.io/ http://www.sktelecom.com/en/press/detail.do?idx=1172
  • 5. Background - application scenarios  Emergency responses, on-demand crowd sensing, Geo Sports monitoring, cyber-physical systems testing, etc. FiCloud 2016, Vienna, 24th August 2016 5  Need to have an end-to-end provisioning of resources  E.g., sensors, network function services, storage, virtual machines  Short, crucial and heavily workload; elasticity and uncertainties. Geo Sports: Picture courtesy Future Position X, SwedenIndian Overfly collapses figure source: http://timesofindia.indiatimes.com
  • 6. Motivation – End-to-End resource slice provision 6 Emergency response Hospital & traffic Emergency response service Early treatment protocol Best route to the hospital Most suitable hospital - Wearables - Mobie medical equipment - First aid info. - Vehicle capability - Location - Hospital capability - Traffic status Victims Distributed resource management IoT resource provisioning Dedicate sub-network Coordinate operations FiCloud 2016, Vienna, 24th August 2016
  • 7. Motivation – End-to-End resource slice provisioning FiCloud 2016, Vienna, 24th August 2016 7 End-to end Resource slice CPS Applications/Virtual infrastructures http://sincconcept.github.io/ This paper: harmonize resource information from IoT, network functions and cloud providers for resource slice provisioning
  • 8. Examples of existing providers/models Provider Category APIs Information models FIWare Orion IoT RESTful (NGSI10), one-time query or subscription High level attributes on data and context FIWare IDAS IoT RESTful for read/write custom models and assets Low level resource model catalogs IoTivity IoT REST-like OIC protocol, support C++, Java and JavaScript Multiple OIC model OpenHAB IoT RESTful for query and control IoT resources Low level resource model catalogs OpenDayLight Network Dynamic REST generated from Yang model (model-driven) Low level resource model catalogs OpenBaton Network RESTful for network service description ETSI MANO v1.1.1 data model OpenStack Cloud RESTful, multiple language via SDK, OCCI, CIMI OpenStack model, OCCI, CIMI 8 FiCloud 2016, Vienna, 24th August 2016
  • 9. Approach 9 FiCloud 2016, Vienna, 24th August 2016 • Avoid top-down • Design a “super” model to manage the world. • Focus and suitable for single-purpose solution. • Bottom-up • Let providers use their own models. • Integration and link diverse types of information. • Adaptor: to interface with providers’ APIs. • Transformer: integrate our distributed resource model. • Focus on resource relationships across IoT, Network functions and clouds.
  • 10. Information model Physical: Sensor/actualtor/devices in providers’ models Virtual IoT: SD-Gateway and capabilities. Network functions: edge-to-edge, edge-to-cloud network. Clouds: VM, data services, data analytics. 10 FiCloud 2016, Vienna, 24th August 2016
  • 11. Resource information integration • The model aims to be extensible to cater multiple underlying devices and services. • To cope with the rapidly increasing of systems. • A process to interface with resource providers. 11 FiCloud 2016, Vienna, 24th August 2016
  • 12. Examples FiCloud 2016, Vienna, 24th August 2016 12 "data": { "DeviceProps": { "commandURL": "http://...OpenIoT/..", "lastIP": "195.97.103.225", "commands": true }, "asset": { "name": "00:3b:B6:BodyTemperature", "description": "asset model protocol" }, "model": "SENSOR_TEMP", "registrationTime": "2015-04-16T15:39:58Z", "status": "Active", "sensorMetaData": [ {"ms": { "dataType": "BodyTemperature", "unit": "Celsius", "rate": "10" } }]} { "type": "LocationItem", "link": "http://..../rest/items/DemoLocation" } Resource from OpenHAB - Simple data format. - A link for more information. - Information is static. - Complex data format. - Have control capability. - More meta data. A resource from OpenIOT
  • 13. Examples FiCloud 2016, Vienna, 24th August 2016 13 "SoftwareDefinedGateway":{ "uuid": "5a60...", "name": "gateway1", “datapoints": [ { “name": “Temp1", "datatype": "BodyTemerature", "measurementUnit": "Celsius", "resourceID": "00:3b:B6:BodyTemperature", "extra": [ <imported List 1 and List 2> ...} ], }, “controlpoints”: [ { "name": "changeRate", "resourceID": "00:3b:B6:BodyTemperature", "description": "change sensor rate", "reference":"http://.../OpenIoT/assets/..", } ], } Virtual IoT resource information - Software-Defined Gateway wraps a set of capabilities. - Data Point extracts a set of interesting attributes for higher level management. - Control Point contains a reference to the provider API for controlling resources.
  • 14. Architecture 14 Global management service - Run on users’ site. - Coordinate Local Management Service. - Manage relationships. Local management service - Deployed on gateway or network station. - Interface with provider. - Transform information. FiCloud 2016, Vienna, 24th August 2016
  • 15. Prototype 15 FiCloud 2016, Vienna, 24th August 2016 http://sincconcept.github.io/HINC/
  • 17. Testbed 17 FiCloud 2016, Vienna, 24th August 2016 In-lab testbed: - Server: 8 CPU-i7, 3.60GHz, 32GB RAM - Edge: 100 dockers with emulated sensors + gateways. - Network: virtual routers (https://www.weave.works/) - Cloud: event processing.
  • 18. Testbed 18 FiCloud 2016, Vienna, 24th August 2016 Distributed testbed - Edge: physical/virtual machines on different cities. - Communication: CloudAMQP. - Cloud: AmazonEC.
  • 19. Reducing complexity in accessing and control resources 19 FiCloud 2016, Vienna, 24th August 2016 1. Query data points 2. Control the resource 3. Query network functions and clouds
  • 20. Query time by number of gateways Distributed sites testbed FiCloud 2016, Vienna, 24th August 2016 20 In-lab testbed
  • 21. Gateway’s response time variability FiCloud 2016, Vienna, 24th August 2016 21 Distributed sites testbed In-lab testbed
  • 22. Number of sensors and locations FiCloud 2016, Vienna, 24th August 2016 22 Query time from distributed sites Query time by number of sensors, distributed sites
  • 23. Conclusion and future work  Harmonizing information in 3 dimensions:  High-level view of low level resources  End-to-end view of IoT, network functions and clouds  Large-scale view of highly distributed sites  Future work:  Information-centric resource provisioning  Dynamic IoT infrastructure configuration  End-to-end resource optimization 23 FiCloud 2016, Vienna, 24th August 2016
  • 24. Thanks for your attention! Questions? Hong-Linh Truong Distributed Systems Group TU Wien dsg.tuwien.ac.at/staff/truong FiCloud 2016, Vienna, 24th August 2016 24