HINC – Harmonizing Diverse Resource Information Across IoT, Network Functions and Clouds
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Transcript of HINC – Harmonizing Diverse Resource Information Across IoT, Network Functions and Clouds
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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
http://dsg.tuwien.ac.at/staff/truong
FiCloud 2016, Vienna, 24th August 2016 1
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Outline
Background and motivation
HINC framework
Distributed resource information model
Architecture and implementation
Testbed and experiments
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Background - Systems
3
Analysis &
managementHot deploy
Control
Re-route
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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 4https://arrayofthings.github.io/
http://www.sktelecom.com/en/press/detail.do?idx=1172
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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, Sweden Indian Overfly collapses
figure source: http://timesofindia.indiatimes.com
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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
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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
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Examples of existing
providers/modelsProvider 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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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.
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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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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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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
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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.
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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.
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Prototype
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http://sincconcept.github.io/HINC/
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Testbed
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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.
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Testbed
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Distributed testbed
- Edge: physical/virtual machines on different cities.
- Communication: CloudAMQP.
- Cloud: AmazonEC.
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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
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Query time by number of gateways
Distributed sites
testbed
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In-lab testbed
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Gateway’s response time variability
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Distributed sites
testbed
In-lab testbed
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Number of sensors and locations
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Query time from
distributed sites
Query time by
number of sensors,
distributed sites
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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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Thanks for your
attention!
Questions?
Hong-Linh Truong
Distributed Systems GroupTU Wien
dsg.tuwien.ac.at/staff/truong
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