Data Communications vs. Data Communications vs. Distributed ComputingDistributed Computing

Dr. Craig PartridgeDr. Craig Partridge

Chief Scientist, BBN Chief Scientist, BBN TechnologiesTechnologies

Chair, ACM SIGCOMMChair, ACM SIGCOMM

A Quick HistoryA Quick History

In the 1980s, the data comm In the 1980s, the data comm community largely stopped leading community largely stopped leading in network application developmentin network application development• Overwhelmed by lower layer research Overwhelmed by lower layer research

problemsproblems Other communities stepped in:Other communities stepped in:

• OS and distributed systemsOS and distributed systems• Supercomputing and physicsSupercomputing and physics

An unfortunate side effectAn unfortunate side effect

The two fields most expert in The two fields most expert in networking don’t talk as much as networking don’t talk as much as they shouldthey should

Indeed, I was invited to talk here Indeed, I was invited to talk here because it was considered nice to because it was considered nice to have a networking perspective...have a networking perspective...

What’s new in networkingWhat’s new in networking

So what have those networking So what have those networking guys been up to for the past ten guys been up to for the past ten years or so???years or so???

One person’s perspectiveOne person’s perspective I’ve tried to focus on fun topicsI’ve tried to focus on fun topics

• So nothing on TCP performanceSo nothing on TCP performance– Most problems are configurationalMost problems are configurational

Self SimilaritySelf Similarity

Trouble with queueing theoryTrouble with queueing theory• By late 1980s, clear that classic By late 1980s, clear that classic

models didn’t work for data trafficmodels didn’t work for data traffic• Off by factors of 10 or 100 in queue Off by factors of 10 or 100 in queue

size estimatessize estimates Enter Leland, Taqqu, Willinger & Enter Leland, Taqqu, Willinger &

Wilson (‘93)Wilson (‘93)• Data traffic is self-similar (fractal)Data traffic is self-similar (fractal)

Self Similarity ExampleSelf Similarity Example

More Self SimilarityMore Self Similarity

Self-similarity Self-similarity means traffic means traffic smooths very slowlysmooths very slowly• traffic at 100s traffic at 100s

sample units very sample units very similar to traffic at similar to traffic at 0.01 second samples0.01 second samples

High peak to mean High peak to mean ratiosratios

Self Similarity in practice Self Similarity in practice

Since 1993, we’ve been working to Since 1993, we’ve been working to reduce self similarity to practicereduce self similarity to practice• Confirming it exists on various types Confirming it exists on various types

of networksof networks• Creating generator functions for Creating generator functions for

modelingmodeling• Understanding why it existsUnderstanding why it exists

Quality of ServiceQuality of Service

A term whose definition is evolvingA term whose definition is evolving• Bandwidth guarantee?Bandwidth guarantee?• Loss guarantee?Loss guarantee?• Delay guarantee?Delay guarantee?• All three?All three?

The QoS ChallengeThe QoS Challenge

How to do QoS in a self-similar How to do QoS in a self-similar world?world?• Old style Poisson aggregation doesn’t Old style Poisson aggregation doesn’t

work unless the network loads are very work unless the network loads are very very largevery large

QoS TriumphQoS Triumph• Weighted Fair Queuing (Demers, Weighted Fair Queuing (Demers,

Keshav, Shenker)Keshav, Shenker)• PGPS by ParekhPGPS by Parekh

Weighted Fair QueuingWeighted Fair Queuing

A delightful insightA delightful insight• Transform bit-wise sharing of links Transform bit-wise sharing of links

into packetized sharinginto packetized sharing• Work conserving!Work conserving!

Nicely enough, all other work Nicely enough, all other work conserving schemes have been conserving schemes have been shown to be variants of WFQshown to be variants of WFQ

Fair Queuing DiagramFair Queuing Diagram

Bit by Bit

Fair Q’ing

WFQ DiagramWFQ Diagram

Bit by Bit

WFQ

PGPSPGPS

Packetized General Processor Packetized General Processor SharingSharing• Work by ParekhWork by Parekh

If traffic conforms to a (general) If traffic conforms to a (general) arrival model, we can derive the arrival model, we can derive the upper bound on queuing delayupper bound on queuing delay• At high speeds, bound is nearly At high speeds, bound is nearly

independent of number of queues in the independent of number of queues in the pathpath

What Next for QoS?What Next for QoS?

WFQ is expensive to implementWFQ is expensive to implement• Though good approximations existThough good approximations exist

General feeling that WFQ+PGPS is General feeling that WFQ+PGPS is overkilloverkill• Something simpler should be possibleSomething simpler should be possible• The community is working through The community is working through

various statistical guaranteesvarious statistical guarantees

High PerformanceHigh Performance

Around 1991, the accepted Around 1991, the accepted wisdom was that IP was dead wisdom was that IP was dead because routers couldn’t go fastbecause routers couldn’t go fast

Now, widely accepted that routers Now, widely accepted that routers can achieve petabit speedscan achieve petabit speeds

What Happened?What Happened?

Mostly, good engineeringMostly, good engineering• Router innards re-engineered for Router innards re-engineered for

speedspeed But also some new prefix lookup But also some new prefix lookup

algorithmsalgorithms• Luleå algorithmLuleå algorithm• WashU algorithmWashU algorithm

Ad-Hoc NetworksAd-Hoc Networks

A new and exciting areaA new and exciting area• Imagine thousand or millions of Imagine thousand or millions of

wireless nodes in a roomwireless nodes in a room• They’re movingThey’re moving• They need to discover and federate They need to discover and federate

(securely)(securely)• Managing signal/noise ratio vital for Managing signal/noise ratio vital for

performanceperformance

More on Ad-Hoc NetworksMore on Ad-Hoc Networks

Odd desire to say we’re doneOdd desire to say we’re done• JiniJini• Existing ad-hoc routing protocolsExisting ad-hoc routing protocols

Yet the problems remain hugeYet the problems remain huge• Device location hard (user interface Device location hard (user interface

harder)harder)• Density challenges existing protocolsDensity challenges existing protocols• Clashes over spectrumClashes over spectrum

RobustnessRobustness

To keep the Internet robust we mustTo keep the Internet robust we must• Improve device reliability by factor of 10 Improve device reliability by factor of 10

every two years; ORevery two years; OR• Improve our protocols to be more resilientImprove our protocols to be more resilient

– Assuming something is always going up or Assuming something is always going up or downdown

– How to minimize impactHow to minimize impact– In trafficIn traffic– In performanceIn performance

Can PODC community help here?Can PODC community help here?

Lots of other initiativesLots of other initiatives

SimulationSimulation• How do you simulate something 100 times bigger than anything ever built?How do you simulate something 100 times bigger than anything ever built?

MeasurementMeasurement• How much can you learn just from the edge of the network?How much can you learn just from the edge of the network?

ErrorsErrors• Packets damaged frequently, what to do?Packets damaged frequently, what to do?

AnycastAnycast• Nice idea, how do we make it real?Nice idea, how do we make it real?

The Last SlideThe Last Slide

There’s lots of fun work in There’s lots of fun work in networkingnetworking• A lot has been happeningA lot has been happening• A lot will happenA lot will happen

Some of the problems are also of Some of the problems are also of interest to the PODC communityinterest to the PODC community• I look forward to talking with you I look forward to talking with you

about them.about them.