The Network Flow Language The Network Flow Language (NFL)(NFL)

for Active Sensor Networks (ASNs)for Active Sensor Networks (ASNs)

Danilo Florissi, Yechiam Yemini (YY),

Sushil da Silva, Hao Huang

Columbia University, New York, NY 10027

http://www.cs.columbia.edu/dcc/asn

{df,yy,dasilva,hhuang}@cs.columbia.edu

Toward Programmable Adaptive SNs Key ideas

Active Networks (ANets) enable programming of network transport and processing functions

SNs need to adapt flexibly to limited resources, changing external conditions, and different foci of user interest

Goal: facilitate programmable adaptive SNs through ANet

Scenario

Use

r N

eed

s

Resource Availability

SN

ASN

+ A

Active Networks ANet render networks programmable

Deploy code dynamically to create new node functionality Approaches: code delegation or packet capsules

Virtual Active Net (VAN)

Physical Medium

Active Element

OS Environment

Dataflow model: Active element = packet stream processor engine

Active elements are composed from boxes (computational channels) Boxes encapsulate computations Composition through interconnection Synchronization of streams motions Allocation of underlying resources Simple model of inter-operability

The NetScript Language

NetScript Dataflow Model

Example: IP stack Dynamic deployment of new stack components On-the-fly change/upgrade of functionality

Eth DemuxEth

IPX

AppleTalk

IP

UDP

TCP

Marks in Web Documents HTML: embedded marks define operations on object XML: support programming of marks Syntax and semantics of marks are programmable

DTD files define syntax XSL files define semantics

Marks meaning can vary in time and among processors XSL files can be dynamically loaded and changed

NFL: Language for Marked Flows Marked language to program processing of network flows NFL programs flows ~ XML programs Web documents/objects Marks replace protocol headers and can program:

Routing, flow control, admission control, resource reservation, content filtering, caching, etc.

Marks can extend or replace protocol stacks

NFL Main Components Mark syntax

FTD: Flow Tag Definition ~ DTD Mark semantics

XFL: eXtensible Flow Language ~ XSL NetScript: natural candidate for semantics of NFL marks

NFL is an execution environment for programming active sensor applications

NFL flow NetScriptboxes and links

Node

Mark processing by NFL Known marks are routed to processing boxes Unknown marks spawn processing boxes from NFL servers

NFL flow NetScriptboxes and links

Node

Server

What Can You Do With Marks? Create your own protocols

Marks replace protocol headers Marks are naturally stacked through nesting

Combine networking and computing functions E.g., contents-based routing E.g., contents-based error/QoS control

Program a SN dynamically

Recent Work Design of NFL

Overall runtime architecture Language syntax and semantics Integration with NetScript

Start design and implementation of sample application Efficient HTTP protocol (UXTP)On-demand controlTarget is very fast web access

Looking at other systems proposed by SenseIT researchersImplement components in NFLDrive the design and implementation of the language

Near Future Challenges Porting NetScript to WINS

NetScript currently works on Java VMHeavy use of resources Java VM for Windows CE

Port dynamic deployment of boxesBox servers come and goSingle hop or multiple-hop

Optimize use of limited resources Minimize memory demand on sensors Minimize overhead of NFL runtime

NFL implementation of interesting SenseIT applications

Examples of NFL Examples of NFL ApplicationsApplications

Example: Prioritization of Samples Problem: samples have different levels of priorities and the

network has to prioritize delivery of flows

5 10 0

PriorityQueue

5 1 10 07

Data flows In

NetScriptboxes and links

NodeData flow Out

7 1 0

0

PrioritySorter

Code: Priority in NFL Samples are marked with name and priority levels

Sensor Server:

<samples URI=”http://www.cs.columbia.edu/multicast1270”>

<data priority=5>

12, 15, 14, 17

</data>

<data priority=10>

7, 75, 882, 1

</data>

<data>

6, 10, 8, 8, 9

</data>

</samples>

Example: Route Discovery for WINS Problem: WINS sensors deployed in battlefield need to

discover efficient routing to minimize power consumption

Code: Route Discovery for WINS Sensors broadcast their known distance to peer sensors Small physical distance ~ less power consumption

Sensor Server:

<announce-name URI=”http://www.sensor16.cs.columbia.edu”>

</announce-name>

Sensor Client:

<announce-routes>

<route-entry>

URI=”http://www.sensor25.cs.columbia.edu” 16 sensor17

</route-entry>

<route-entry>

URI=”http://www.sensor5.cs.columbia.edu” 2 sensor9

</route-entry>

</announce-routes>

Example: Simple Diffusion Routing (I) Problem: WINS should route data contents, not individual

packets

Flow A

FlowSorter

B A B B

Flow B

AA A A A

Example: Simple Diffusion Routing (II) Problem: handling failures to links and nodes New NetScript boxes are loaded to handle enhanced functions

Flow AFlowSorter

B A B B

Flow B

AA A A A

DiffusionCriteria

A

A

Code: Simple Diffusion Routing

Sensor Client:

<event-subscribe id=”102” event-label=”image”

URI=”http://www.sensor1076.cs.columbia.edu”

URI=”http://www.cs.columbia.edu/multicast1270”>

</event-subscribe>

Sensor Server:

<samples URI=”http://www.cs.columbia.edu/multicast1270”>

<data> 12, 15, 14, 17 </data>

<image> <!–- image contents> </image>

<data> 6, 10, 8, 8, 9 </data> </samples>

Manager:

<event-notification id=”765” event=”link-down”

URI=”http://www.cs.columbia.edu/multicast1270”>

URI=”http://www.sensor1053.cs.columbia.edu/link7”

</event-notification>

Example: Trading Processor/Bandwidth Problem: flows should compress or expand to minimize use of

stressed resources in each sensorSensor Server:

<flow URI=”http://www.display7.cs.columbia.edu”>

<conditional-compress condition=”bandwidth < 1Mbps”>

<image>

<!-- image data>

</image>

</conditional-compress>

</flow>

Controlling SN With Marks Marks can be used to configure and control

Sensor functions End-end QoS delivery by the network and sensors Routing strategy to maximize QoS, minimize power, etc. Caching of previous samples (for calculation of statistics)

SN Event Processing with Marks Marks can be used to present event data & control processing

Specify the type of data (seismic, streaming audio, etc.) The processing of marks at sensors the flow crosses may

depend on local conditions (available processor, link bandwidth, etc.)

Conclusions, Plans, and Schedule ANet can bring flexibility and simplicity for SN programming NFL marked flow processing paradigm can specify and

enhance SN functionality Plans and Tentative Schedule

7/1999: New contract started 8/1999: New SOW 10/1999: Work on design and implementation of NFL 1/2000: Projected first prototype of basic NFL 3/2000: Port to SenseIT devices 6/2000: Implement relevant SenseIT application(s) Interact with SenseIT community to identify key needs Use SenseIT applications to drive design