Effective resource management in IoT systems must represent IoT resources, edge-to-cloud network capabilities, and cloud resources at a high-level, while being able to link to diverse low-level types of IoT devices, network functions, and cloud computing infrastructures. Hence resource management in such a context demands a highly distributed and extensible approach, which allows us to integrate and provision IoT, network functions, and cloud resources from various providers. In this paper, we address this crucial research issue. We first present a high- level information model for virtualized IoT, network functions and cloud resource modeling, which also incorporates software- defined gateways, network slicing and data centers. This model is used to glue various low-level resource models from different types of infrastructures in a distributed manner to capture sets of resources spanning across different sub-networks. We then develop a set of utilities and a middleware to support the integration of information about distributed resources from various sources. We present a proof of concept prototype with various experiments to illustrate how various tasks in IoT cloud systems can be simplified as well as to evaluate the performance of our framework.

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
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3. Background - Systems
3
Analysis &
management
Hot deploy
Control
Re-route
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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)
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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.
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 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
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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
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7. Motivation – End-to-End resource
slice provisioning
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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
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9. Approach
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• 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.
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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.
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12. Examples
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"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
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"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
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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.
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15. Prototype
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http://sincconcept.github.io/HINC/

16. Testbed
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17. Testbed
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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
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Distributed testbed
- Edge: physical/virtual machines on different cities.
- Communication: CloudAMQP.
- Cloud: AmazonEC.

19. Reducing complexity in accessing
and control resources
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1. Query data points
2. Control the
resource
3. Query network
functions and clouds

20. Query time by number of gateways
Distributed sites
testbed
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In-lab testbed

21. Gateway’s response time variability
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Distributed sites
testbed
In-lab testbed

22. Number of sensors and locations
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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
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24. Thanks for your
attention!
Questions?
Hong-Linh Truong
Distributed Systems Group
TU Wien
dsg.tuwien.ac.at/staff/truong
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