12412_1. High Speed Network

8/12/2019 12412_1. High Speed Network

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High Speed Network

Introduction


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Course OverviewMain focus: Internet technologies, protocols, and

applicationsSecondary focus: Performance issues

Textbook: William Stallings, High Speed Networksand Internet, Pearson Education, 4th, 2011 +( Research Papers literature)

Goal: Understanding the current trend in high speednetworking research field


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Course Objectives :

To introduce principles and current technologiesof High Speed NetworksTo develop an in-depth understanding, in terms of

protocols and applications of major high-speednetworking technologiesPerform network design using the technologies to

meet a given set of requirements

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Objectives of the Chapter

Network basics and network evolution

High Speed network- why and what?

Bottleneck and future Scope ( Advance TCP/IP

and ATM Network)

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We know

What is network ?

Network DevicesHUB

Switch

BridgeRouter

Gateway

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Introduction

Layer Models ??

Type of Network ?

Protocol ??

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Internet Protocol Stack

Application: supporting networkapplications and end-user services

FTP, SMTP, HTTP, DNS, NTP

Transport: end to end data transferTCP, UDP

Network: routing of datagrams fromsource to destination

IPv4, IPv6, BGP, RIP, routing protocols

Data Link: hop by hop frames,channel access, flow/error control

PPP, Ethernet, IEEE 802.11b

Physical: raw transmission of bits8

Application

Transport

Network

Data Link

Physical

001101011...


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CERN Project

The European Organization for Nuclear Research

What is CERN?

an international organization whose purpose is to operatethe world's largest particle physics laboratory

Famous for :Study of interactions between particles

Large Hadron Collider (LHC)Higgs boson

Large computer centre containing very powerful data-processingfacilities primarily for experimental data analysis

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Physicists were excited about the near discovery of the elusive God particle or Higgs boson .Dozens of Indian scientists have been involved over the years searching for this missing cornerstone of particle physics in laboratories in Europe, India and the US.Researchers from Hyderabad played a crucial role in this near discovery by patientlysearching a wide range of giga-electron volts (GeV) to find the Higgs boson, named afterBritish scientist Peter Higgs and Indian physicist Satyendra Nath Bose.

Dr Bose, who taught at Dhaka and Calcutta universities, did pioneering research inmathematical physics and quantum mechanics. Although he did not win the Nobel, at leasttwo scientists who carried forward his work, won the prize.

Even the Large Hadron Collider (LHC) at the European Organisation for Nuclear Research(CERN) has a huge contribution from India. The 8,000-tonne magnet at LHC was made inIndia. Indian teams also contributed to LHC hardware in the form of circuits and software inanalysing computer-generated data. Incidentally, Indians have been associated with CERNfor more than half a century, much before the LHC was fired up.

CERN and INDIA


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CERN Cont..

The Large Hadron Collider will produceroughly 15 petabytes (15 million gigabytes) of data annually

enough to fill more than 1.7 million dual-layer DVDs a year

Around the world scientists want to access and analyse this

dataMajor wide area networking hub .

CERN is collaborating with institutions in 34 differentcountries to operate a distributed computing and data storage

infrastructure: the Worldwide LHC Computing Grid

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CERN Cont..

Outcome ?

birthplace of the World Wide Web (www)

Higher network :

Grid computingHiggs boson

CERN has become a centre for the development of gridcomputing, hosting, among others, the Enabling Grids for

E-sciencE (EGEE) and LHC Computing Grid projects.

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CERN Cont..

VideoCERN Grid computing

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http://localhost/var/www/apps/conversion/tmp/scratch_3/1.3%20What_s%20new%20CERN%20%20GRID%20computing.mp4http://localhost/var/www/apps/conversion/tmp/scratch_3/1.3%20What_s%20new%20CERN%20%20GRID%20computing.mp4


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Motivation for this course :

Need to carry large volumes of traffic with differentquality of service requirements over networkoperating at very high data rate

Area of ProtocolCongestion controlTraffic characterization and management

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High Speed Networking:

A Layered ViewVideo, Web, FTP

TCP/IP, UDP

UnixVAX, Alpha

Adapters, NICs

FDDI, GigE, ATMFiber, SONET, WiFi

Application Designers

Protocol Architects

O.S. Architects

CPU, Memory,Disk

LAN Interfaces

Media Access

Optical Devices


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High Speed Networking: A Layered View

Faster media does not necessarily imply faster

network applicationsInterdependence between layersInteractions between protocols

Need to consider trends of all layers

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Networking

Growth of number & power of computers is driving need forinter connection

Rapid integration of voice , data , image & video

technologie stwo broad categories of communications networks:

Local Area Network (LAN)

Wide Area Network (WAN)

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What is an/the Internet?Connected computingdevices: hosts, end-

systemsPCs, workstations, servers PDAs, phones, toasters,cars

running networkapplications

Communication linksfiber, copper, radio, satellite

Routers/switches: forward packets (chunks) of datathrough network

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router workstation server

mobile

local ISP

company

network

regional ISP


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As of January 2005 > 300 million computers in 209 countries


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Internet Evolution

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High-Speed Networks: Analog Network to ATM

Analog Network ( Analog switching + circuitswitching )

Public telephone network analog based switching

IDN (Integrated Digital Network)A network that uses both digital transmission and digitalswitching.

Need to provide economic voice communicationearly 60s, answer to growth of digital, computer -controlled,circuit-switched networkingWestern Electic 4ESS introduced in 1976, 1 st large scalecommercial time-division switch 29


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Voice and Data Communicationover an Analog Telephone Network


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Analog and Digital Servicesover the Telephone Network


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IDN


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Cont ISDN (Integrated Services Digital Network)

integrated voice and data on the same digital transmissionlinks/exchangesdesigned to allow digital transmission of voiceand data over ordinary telephone copper wiresresulting in potentially better voice quality than an analog

phone can provide.It offers circuit-switched connections (for either voice ordata), and packet-switched connections (for data), in

increments of 64 kilobit/s.

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ISDN


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The Need for Speed!Scale

growing number of hosts -- growing demands on

bandwidthnew technologies result innew paradigms for deviceand connection types

e.g. ??

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Application demand for large to huge file

transfers increasing critical nature of

Internet use demand for real -time

performance characteristics demand for guarantees of

service levels

Its all about User Expectations


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High-Speed Networks: LANs

High-speed LANs driven by explosive growth in speed and computing

power of PCs in 1990semergence of client-server computing architecture in

business environment .use of centralized server farmsemergence of power workgroups and workgroupapplicationsneed for local high-speed LAN backbones

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Traffic typeElastic traffic:

adjust its throughput and delay between end hosts in responseto network condition.

Generally TCP-based application (HTTP,STMP,FTP)

Principle form of feedback: packet loss caused by network

load/congestion, causing TCP to implements its congestionavoidance algorithm and reduce the rate at which packets aresent over the network

TCP traffic is considered to be "network friendly

Inelastic traffic: - does not easily adapt /adjust its throughputand delay in response to network conditions- generally real-time multimedia (audio streaming,video,VoIP)

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Traffic type

How to handle Inelastic Traffic ?????Requirement

Preferential treatment to application wit more demanding

resourceState requirement in advance

Using service request functionOn fly

IP packet header field

Should support elastic traffic as well

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Qos on InternetRequirements for inelastic traffic includes :

Throughput: average rate of successful message deliveryover a communication channel.

Delay: The delay of a network specifies how long it takes for abit of data to travel across the network from one node orendpoint to another. It is typically measured in multiples or

fractions of seconds.

Delay variation : allowable delay

Packet loss: Packet loss is the failure of one or more

transmitted packets to arrive at their destination. 41


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Delays in Packet Switched (e.g. IP)Networks

End-to-end delay (simplified) =(d prop + d trans + d queue + d proc ) x Q

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B A Where:

Propagation delay (d prop )Transmission delay (d trans )Queuing delay (d queue )Processing delay (d proc )

Number of links (Q)

Processing delay - time routers take toprocess the packet headerQueuing delay - time the packet spends inrouting queuesTransmission delay - time it takes to pushthe packet's bits onto the linkPropagation delay - time for a signal toreach its destination


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The Need for Improved (better)Levels of Service

Inter net Best-Effort Service

all packets treated equallydesigned for elastic trafficno guarantees of

bandwidth or throughputno guarantees of delayno guarantee of jitter(delay variation)

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Applications often create inelastic traffic often sensitive to delay often sensitive to jitter often critical in nature

generate elastic traffic as well

User Requirem ents!


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Semi-Supervised Network Traffic Classification


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Jeffrey Erman , Anirban Mahanti , Martin Arlitt , Ira Cohen , Carey Williamson Department of Computer Science, University of Calgary

Department of Computer Science and Engineering, Indian Institute of Technology (Delhi)Enterprise Systems & Software Labs, HP Labs

Introduction

Conclusions

References

Classification Framework

Real-Time Classification

Semi-Supervised Results

Acknowledgements

A fundamental challenge in the design of the real-time classificationsystem is the need to classify a flow as soon as possible. Unlike offlineclassification where all discriminating flow statistics are availablea priori ,in the real-time context we only have partial information on the flowstatistics.

Our solution uses a layered classification system based on the idea ofpacket milestones.

A packet milestone is reached when the count of the totalnumber of packets a flow has sent or received reaches a specificvalue.

Each layer has an independent classifier. Flow statistics are monitored in real-time. As a flow reaches a packet milestone it is classified/reclassified

by the appropriate layer.

This layered approach allows us to revise and potentially improve theclassification of flows.

Figures 3 & 4 present example results by using the April 13, 9 am tracewe collected from the UofC. We see that the classier performs well, withbyte accuracies typically in the 70% to 90% range.

CampusRouter

Internet

Web

Streaming

P2P

U of Calgary

Identifying and categorizing network traffic byapplication type is challenging because of thecontinued evolution of applications, especially of thosewith a desire to be undetectable. The diminishedeffectiveness of port-based identification and theoverheads of deep packet inspection approachesmotivate us to propose a traffic classificationmethodology that relies on using only flowstatistics to classify traffic.

Full Paper available at: http://pages cpsc ucalgary ca/~erman/

Our proposed technique is a flexible mathematicalframework that leverages both labeled and unlabeledflows. Thissemi-supervised approach to learning anetwork traffic classifier is a key contribution of thiswork. Fast and accurate classifiers can be obtained by

training with a small number of labelled flows mixedwith a large number of unlabelled flows.

High flow and byte accuracy can be achieved foroffline and real-time classification

Robust classifiers can be built that are immune totransient changes in network conditions.

Our approach can be integrated with solutionsthat collect flow statistics. We developed a prototype

real-time classifier using Bro [4].

Clustering

AlgorithmClassifier

LabelledTraining Data

LabelledClusters

ClassifiedFlows

UnclassifiedFlows

Step 1: Model Building Step 2: Classification A clustering algorithm partitionsthe training flows into disjointgroups called clusters based onsimilarity. The advantages are:

Builds natural clusters.

The number of training flowsneeded is small (e.g., 8000)

Classifier assigns each newunclassified flow to the nearest clusterusing Euclidean distance. This is themaximum likelihood cluster assignment.

Label of t he ass igned clusterbecomes the classification of the flow.

A cluster label is obtained using thelabelled flows available in each cluster.

These can be obtained through avariety of means: (automated)payload analysis, port numbers,expert knowledge.

Cl usters with no labels can be leftas unknown.

Training Data: Training data can be amix oflabelled and unlabelled flows.Features include: Average Packet Size,Number of Packets, Payload Bytes,Header Bytes, etc.

Typical byteaccuracies in the 70%to 90% range.

Figure 3: Performance of Real-time Classifier

This work was supported by the Natural Sciences and Engineering ResearchCouncil (NSERC) of Canada and Informatics Circle of Research Excellence(iCORE) of the province of A lberta, Canada.

Labelling of training feature vectors is one of the most timeconsuming steps of the classification process.

Figure 1: Selective Labelling of Flows

Figure 4: Byte Accuracy of Real-time Classifier

In Figure 1 we test the hypothesis that if a few flows arelabelled in each cluster then we have a reasonablebasis for creating the cluster to application mapping.With as few as two labels per cluster, we attain 94%flow accuracy.

The results in Figure 2 show the effect on theclassifiers precision when we used a fixed number of labelled flowsand a varying numbers of unlabelled flows in the trainingdata set.

Our results show that for a fixed number of labelledtraining flows, increasing the number of unlabelled flowsincreases the classifiers precision.

[1] O. Chapelle, B. Scholkopf, and A. Zien, editors. Semi-Supervised Learning. MIT Press,Cambridge, MA, 2006.

[2] J. Erman, A. Mahanti, M. Arlitt, I. Cohen, and C. Williamson. Offline/Online TrafficClassification Using Semi-Supervised Learning. To Appear in Proc. of IFIP Performance 2007

[3] J. Erman, A. Mahanti, M. Arlitt, and C. Williamson. Identifying and Discriminating BetweenWeb and Peer-to-Peer Traffic in the Network Core. InWWW 07, Banff, Canada, May 2007.

[4] V. Paxson. Bro: A System for Detecting Nework Intruders in Real-time. Computer Networks,31(23-24):2435-2463, 1999.

Figure 2: Training with (Un)labelled Flows

Retraining Detection

UnlabelledTraining Data

Although we found that our classifiers remai ned robustfor extended periods of time, a mechanism fordetermining when the classifier needs updating is stillrequired.

We propose using the average distance of new

flows to the centroid of the nearest cluster; asignificant increase in the average distance indicatesthe need for an update.

Figure 5: Correlation Between Average Distance andFlow Accuracy


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More TopicsP2P systems modeling and analysisWireless Internet measurement/modeling

WiMax (IEEE 802.16)

QoS in CDMA2000 EV-DO

Wireless mesh networks?Sensor networks?Grid computing?

Network security?

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Exercise

Take case study on Worldwide LHC Computing Grid on needof high speed network and discuss .

12412_1. High Speed Network

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