1TELCORDIA CONFIDENTIAL – RESTRICTED ACCESS

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Traffic Analysis and QoS Provisioning

for e-Health Grid Operations

Telcordia Contact:

Piotr Szczechowiak, PhD

Research Scientist

Telcordia Poland

pszczech@telcordia.com

January 27th 2011

Michał Koziuk

mkoziuk@telcordia.com

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Outline

Telcordia-Poland introduction

TARC-PL research activities

e-Health Grid

e-Health applications

COST project overview

Differentiation Inside Aggregation

QoS measurements

Future work

Summary

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Telcordia-Poland introduction

TARC-PL is a wholly-owned subsidiary of Telcordia oriented towards

European telecommunication and ICT markets leveraging Telcordia

technologies and know-how.

Officialy established in 2008 as Telcordia Poland sp.z o.o.

Offices in Poznań and Warsaw

Prof. Andrzej Dąbrowski, Head and Chief Scientist for TARC-PL

Currently 10 staff members

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Telcordia-Poland research activities

5 EU-FP7 and 2 national projects awarded

Past projects:

EFIPSANS - Exposing the Features in IP version Six protocols that can be exploited/extended for the purposes of designing/building autonomic Networks and Services

COMPAS - Compliance-driven Models, Languages and Architectures for Services

Current projects:

CARMESH - Ubiquitous Mesh Networking for Integrated Telematic Services in Metropolitan Areas

INDENICA – Engineering Virtual Domain-Specific Service Platforms

E-SPONDER – First Responder of the Future

National projects:

COST IC0703 – Traffic Analysis and Dynamic Resource Allocation for e-Health Grid Operations

Context dependant car classification project

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e-Health grid

Grid computing allows researchers, doctors and patients to

share computer power and data storage capacity over the

internet.

e-Health Grid

Healthcare

Professionals

Patients Researchers

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e-Health Grid applications

Main e-Health Grid applications:

Researchers can use grid computing’s processing power to hunt for new viruses, search for new drugs, model disease outbreaks, image the body’s organs and determine treatments for patients

Doctors can gain access to relevant health data and simulation tools regardless of where they are and where the data is stored

Patients can receive a more individualised form of Healthcare and remote diagnosis services

Healthcare workers and doctors can easily collaborate and share large amounts of information

Example grid architectures:

Health Grid - EU, Biomedical Informatics Research Network (BIRN) – USA, Cancer Biomedical Informatics Grid (caBIG) – USA, neuGRID - EU

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Main challenges

End-to-end QoS can be achieved only through both appriopriate Grid performance gurantees and QoS assurances at the network level

QoS measures have been well defined in the domain of communication services but not in the e-Health domain

e-Health service category has a diversified set of traffic patterns with very different bandwidth, delay and reliability requirements

Current network layer QoS solutions are applicable only to an aggregated CoS

In order to guarantee QoS there is a need to identify and differentiate e-Health traffic at the outset of the network

The concept of Differentiation Inside Aggregation (DIA) can help match the chracteristic of e-health traffic and assign particular medical application to a dedicated class of service

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COST project overview

e-Health grid services in the future internet

QOS

(DIA)

MEDIA

COLLABORATION

GRID

RESOURCE

SHARING

INPUT OUTPUTSYSTEM

e-Health

Application

Class & System

Requirements

Traffic Data &

Performance

Parameters

Internet QoS

Constrained Traffic

Supporting e-health

Grid Operations

Seamless e-health

Applications

Provisioning Over

The Network

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Main goals of the project

The primary objective of the project is to design and

develop traffic analysis and network QoS provisioning tools

to support various e-health grid applications

Main tasks:

e-Health traffic characterization

Medical-grade QoS modeling

Available bandwidth and delay estimation

QoS - Differentiation Inside Aggregation (DIA) concept

Simulation and validation of the QoS DIA design

Resource allocation and sharing in the e-Health grid

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e-Health applications requirements

Medical applications have the most stringent QoS

requirements among all Internet services

Collaborative online medical applications need high

bandwidth and are not delay tolerant due to real time data

(voice & video chat)

Medical images and data transmission require high

bandwidth – distorted images may lead to a wrong diagnosis

(ultra high resolution necessary in MRI, fMRI, PET)

Medical simulation tools, image reconstruction programs and

surgery support applications must have minimal packet loss

and require guaranteed data delivery

Appriopriate grid resources with required HPC power must

be available at all times

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Differentiation Inside Aggregation

In order to implement the concept of Differentiation Inside

Aggregation there is a need to calculate traffic statistics

and identify applications at the network level

This task can be performed by using a comibnation of

different tools such as protocol analyzers, remote

monitoring probes and network-based application

recognition services

Definition of a separate e-Health application class is not

sufficient to achieve the necessary QoS for medical

applications

Single medical application may use all available bandwidth

and degrade QoS parameters for other e-Health services

Required QoS assurances can only be achieved by

appriopriate control at the flow level for each application

that belongs to the e-Health class

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QoS measurements baseline algorithm QoS parameters estimation is between two nodes sender-receiver

It takes one-way delay to notify the sender of the delay and bandwidth estimations at the receiver

Estimation can be carried out in both directions (most paths are asymmetric)

Immediate deduction of reverse link estimates across symmetric links

Sender

Sends chains of time stamped

sequenced-numbered small

Probe packets

Receiver

Estimate E2E one-way delay &

Bottleneck link bandwidth

Probe inputs

• Arrival times/inter-packet gaps

• Time stamps

• Sequence Numbers

• Packet Sizes

Throughput & loss rate info for all incoming traffic

Outputs to local/remote terminals

• One way delay

• Chanel BW of bottleneck link

• Available BW across bottleneck

link in the presence of cross traffic

from/to other nodes

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Future work

The project is in its early stage (4 months elapsed) and there is still

much work to be done:

Implementation and evaluation of QoS measurement algorithms

Resource sharing mechanisms in the e-Health grid

Modification of diffserv to include new CoS and DIA concept

(ns-2/ns-3)

Validation and demonstration of results

Emulated grid

environment

background

traffic generation

traffic classification &

DIA application

e-Health

application

running on a

tablete-Health application

running on a smartphone

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Summary

The main objective of the project is to design traffic

analysis tools to enable QoS for e-health grid applications

The above goal will be achieved through validation of the

QoS Differentiation Inside Aggregation concept

The DIA solution will incorporate mechansims from

standard Diffserv and Interserv QoS models

Cooperation with COST action partners and other partners

interested in e-health is possible (joint work/publications)

We are also looking for traces from any kinds of e-Health

networks

Thank you for your attention!