04/24/23 1

Active Virtual Network Management PredictionActive Virtual Network Management Prediction

DARPA demo performed in collaboration with:

Amit Kulkarni (GE CRD)Virginie Galtier, Yannick Carlinet and Kevin L. Mills (NIST)

TERENA Networking Conference May 14-17, 2001

Stephen F. Bush

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• Faster hardware more fully utilized

• Enables more flexible network

• De-couples protocol from transport

• Minimizes global agreement overhead

• Enables on-the-fly experimentation

• Enables faster deployment of new services

Active Network Benefits

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Active Application (AA)Active network application

AVNMP, AudioApp

Execution Environment (EE)Analogous to a Unix shell for packet execution

Magician, ANTS

Node Operating System (NodeOS)Operating System support for EEs

EE 1

NodeOS

EE 2

Hardware

AA AA AA AA

Active Network Framework

AAActiveAudio

PP

CPU Model

Magician EE

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Options

Payload

ANEP Header Length ANEP Packet Length

Version Flags Type ID

Allows encapsulation of active packets in any transport media

Active Network Encapsulation Protocol (ANEP)

OptionsSource Identifier 1

IPv4 address (32 bits) 1IPv6 address (128 bits) 2802.3 address (48 bits) 3

Destination Identifier 2Same addressing schemes

Integrity Checksum 316 bit one's complement of

the one's complement sum of the entire ANEP packet, starting with the ANEP Version field

N/N Authentication 4Non-Negotiated Authentication

SPKI Self-signed Certificate 1X.509 Self-signed Certificate 2

PayloadAny data or code to be executed by an EE

ANTS codeMagician codeASP codeSmartPacket codePLAN code

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Enables management of more complex systems such as active networks; leading towards self-healing and self-management

Optimal management polling interval is determined based upon predicted rate of change and fault probability

Fault correction will occur before system is impacted

Time to perform dynamic optimization of repair parts, service, and solution entity (such as software agent or human user) co-ordination

Optimal resource allocation and planning

“What-if” scenarios are an integral part of the network

AVNMP-enhanced components protect themselves by taking action, such as migrating to “safe” hardware before disaster occurs

Benefits of Self-Prediction

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Goal: Prediction for Management

Deployment:Optimal use of space and time

Space

Time

Injecting a Model into the Network

L-1 L-3

L-2

L-4

AN-5AN-1

AN-4

Real System

Virtual System

L-1 L-3

L-2

L-4

AN-5AN-1

AN-4DP

LPLP

LP

Distributed model-based prediction capability within systemActive Packet

Network Management Client getnext 1.3.6.1.x.x.x.x.t

getnextresponse 1.3.6.1.x.x.x.x.t+

Managed Object

State Queue (SQ)

SNMP Query

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ABONESending

node

FastestIntermediate

Node

Destinationnode

SlowestIntermediate

Node

AVNMP Architecture

AVNMP AA

Magician AAsPP

LP

Predictor

AA

ActiveAudio

Magician EEMIB

AAActiveAudio

AVNMP updatespredicted MIB values

SNMP

PP

Routing Model

PP

CPU Model

Injected Applications

Injected Models

PP PPPP PP

Other Potential Models

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Prediction ends when preset look ahead is reached

Previous predictions are refined as time progresses

Cyclic Prediction Refinement

Load(packets/second)

Wallclock (minutes)

LVT(minutes)

2040

20

02000400060008000

Load(packets/second)

Wallclock (minutes)

LVT(minutes)

2040

20

02000400060008000

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500000 1 106 1.5 106 2 106 2.5 106 3 106WallclockmS

50

100

150

200

Prediction Error Accuracy

Experiment involved demanding more accuracy over time by reducing the error between predicted and actual values, however...

500000 1 106 1.5 106 2 106 2.5 106 3 106WallclockmS

50000

100000

150000

200000

Expected Lookahead mS Performance

…the tradeoff was loss in Look-ahead...

500000 1 106 1.5 106 2 106 2.5 106 3 106WallclockmS

1

2

3

4

5

6

Speedup Performance

…. and loss in speedup

Accuracy-Performance Tradeoff

Prediction Error

Look-ahead

500000 1 106 1.5 106 2 106 2.5 106 3 106WallclockmS

0.2

0.4

0.6

0.8

1

ProportionOut of Tolerance Performance

… this required more out-of-tolerance messages...

Out of Tolerance Messages

Speedup

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AVNMP Algorithm Prediction performance continuously kept

within tolerance via rollback

Time Warp-like technique used for maximum use of space and time in virtual system

Rollback State Cache holds MIB future values

PP

AVNMP Model

LP

Logical Process

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CPU and Load ApplicationsPredict Resource Use, Including CPU, Throughout an Active Network

Demonstrate predictive power of AVNMP and improvement in predictive power when combining NIST CPU usage models with AVNMP

And so AVNMP can predict CPU usage further into the future

With the NIST CPU usage model integrated, AVNMP requires fewer rollbacks

DPPredictor

DriverPPLP

PPLP

Sendingnode

FastestIntermediate

Node

Destinationnode

SlowestIntermediate

Node

Green Black Red Yellow

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CPU Application ResultsPredict Resource Use, Including CPU, Throughout an Active Network

TTL CPU Prediction

Better CPU prediction model overcomes performance tradeoff limitations

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AccomplishmentsDemonstrated the power of AVNMP to predict resource usage, including CPU, throughout an active network

Showed that AVNMP can predict network-wide resource consumption Compared accuracy of AVNMP CPU usage predictions with and without the NIST CPU usage models Illustrated benefits when AVNMP provides more accurate predictions

Demonstrated the ability to detect and kill malicious or erroneous active packets

Illustrated motivation behind CPU usage modelingShowed improvement of NIST CPU usage models over naive scaling

Demonstrated management of CPU prediction and control of packets on per-application basis by an EE (Magician probably the first of its kind)

Developed MIB for CPU and AVNMP Management of an active node

Integrated SNMP agents and reporting in an EE Provided user-customizable event reporting through multiple mechanisms: Event Logger and SNMP

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Denial-of-Service Attacks Can a combination of AVNMP load prediction and NIST CPU prediction be

used to combat denial of service attacks?

Many small packets

NIST CPU Prediction AVNMP Model

Large CPU packetsAVNMP Load Prediction Model

Attacker

Legitimate Data

TargetLegit User

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DARPA Fault-Tolerant Networks Project

Fault

Portion of Solution

Portion of Solution

Portion of Solution

Portion of Solution

Identify faults within a complex system of management objects

Scale in number of objects andnumber of futures

Robust in the presence of faults Only necessary and sufficient

repair capability should exist in time and space

Network Inherently Forms Fault-Corrective ActionNetwork Inherently Forms Fault-Corrective Action

No Attraction Attraction

Random (Healthy) incompressible Order (Multiple Faults) compressible

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New Theory of Networks Leads to ...

Legacy

Networks

Active

Networks

Shannon

Entropy

Kolmogorov

Complexity

Bits

Legacy

Networks

Active

Networks

Shannon

Entropy

Kolmogorov

Complexity

Bits

Shannon

Entropy

Kolmogorov

Complexity

as active packet

is communication

media

as active packet

is communication

media

Fine-grained model

as active packet

is communication

media

Bits

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References

Book Bush, Stephen F. and Kulkarni, Amit B., ActiveNetworks and Active Virtual Network ManagementPrediction: A Proactive Management Framework,Kluwer Academic/Plenum Publishers, Spring 2001,ISBN 0-306-46560-4.

Source Code http://avnmp.sourceforge.net

GE ActiveNetworkLaboratory

http://www.crd.ge.com/~bushsf/an