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

HINC – Harmonizing Diverse Resource

Information Across

IoT, Network Functions and Clouds

Duc-HungLe, Nanjangud Narendra, Hong-Linh Truong

Distributed Systems Group, TU Wien

[email protected]

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


Background - Systems

3

Analysis &

managementHot 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 4https://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.


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

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


Motivation – End-to-End resource

slice provisioning


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/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

8FiCloud 2016, Vienna, 24th August 2016

Approach


• 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.


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.


Examples


"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


"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.


Prototype


http://sincconcept.github.io/HINC/

Testbed


Testbed


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


Distributed testbed

- Edge: physical/virtual machines on different cities.

- Communication: CloudAMQP.

- Cloud: AmazonEC.

Reducing complexity in accessing

and control resources


1. Query data points

2. Control the

resource

3. Query network

functions and clouds

Query time by number of gateways

Distributed sites

testbed


In-lab testbed

Gateway’s response time variability


Distributed sites

testbed

In-lab testbed

Number of sensors and locations


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


Thanks for your

attention!

Questions?

Hong-Linh Truong

Distributed Systems GroupTU Wien

dsg.tuwien.ac.at/staff/truong


Education

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