1. 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

2. Toward Programmable Adaptive SNs * Key ideas l Active Networks (ANets) enable programming of network transport and processing functions l 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 User Needs Resource Availability SN ASN + A

3. Active Networks * ANet render networks programmable l Deploy code dynamically to create new node functionality l Approaches: code delegation or packet capsules

4. * Dataflow model: l Active element = packet stream processor engine * Active elements are composed from boxes (computational channels) l Boxes encapsulate computations l Composition through interconnection l Synchronization of streams motions l Allocation of underlying resources l Simple model of inter-operability The NetScript Language

5. NetScript Dataflow Model * Example: IP stack l Dynamic deployment of new stack components l On-the-fly change/upgrade of functionality

6. Marks in Web Documents * HTML: embedded marks define operations on object * XML: support programming of marks * Syntax and semantics of marks are programmable l DTD files define syntax l XSL files define semantics * Marks meaning can vary in time and among processors l XSL files can be dynamically loaded and changed

7. 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: l Routing, flow control, admission control, resource reservation, content filtering, caching, etc. * Marks can extend or replace protocol stacks

8. NFL Main Components * Mark syntax l FTD: Flow Tag Definition ~ DTD * Mark semantics l XFL: eXtensible Flow Language ~ XSL l NetScript: natural candidate for semantics of NFL marks * NFL is an execution environment for programming active sensor applications NFL flow NetScript boxes and links Node

9. Mark processing by NFL * Known marks are routed to processing boxes * Unknown marks spawn processing boxes from NFL servers NFL flowNetScript boxes and links Node Server

10. What Can You Do With Marks? * Create your own protocols l Marks replace protocol headers l Marks are naturally stacked through nesting * Combine networking and computing functions l E.g., contents-based routing l E.g., contents-based error/QoS control * Program a SN dynamically

11. Recent Work * Design of NFL l Overall runtime architecture l Language syntax and semantics l Integration with NetScript * Start design and implementation of sample application l Efficient HTTP protocol (UXTP) íOn-demand control íTarget is very fast web access l Looking at other systems proposed by SenseIT researchers íImplement components in NFL íDrive the design and implementation of the language

12. Near Future Challenges * Porting NetScript to WINS l NetScript currently works on Java VM íHeavy use of resources íJava VM for Windows CE l Port dynamic deployment of boxes íBox servers come and go íSingle hop or multiple-hop * Optimize use of limited resources l Minimize memory demand on sensors l Minimize overhead of NFL runtime * NFL implementation of interesting SenseIT applications

14. Example: Prioritization of Samples * Problem: samples have different levels of priorities and the network has to prioritize delivery of flows 5100 Priority Queue 511007 Data flows In NetScript boxes and links Node Data flow Out 710 0 Priority Sorter

15. Code: Priority in NFL * Samples are marked with name and priority levels Sensor Server: 12, 15, 14, 17 7, 75, 882, 1 6, 10, 8, 8, 9

16. Example: Route Discovery for WINS * Problem: WINS sensors deployed in battlefield need to discover efficient routing to minimize power consumption

17. Code: Route Discovery for WINS * Sensors broadcast their known distance to peer sensors * Small physical distance ~ less power consumption Sensor Server: Sensor Client: URI=”http://www.sensor25.cs.columbia.edu” 16 sensor17 URI=”http://www.sensor5.cs.columbia.edu” 2 sensor9

18. Example: Simple Diffusion Routing (I) * Problem: WINS should route data contents, not individual packets Flow A Flow Sorter BABB Flow B AAAAA

19. Example: Simple Diffusion Routing (II) * Problem: handling failures to links and nodes * New NetScript boxes are loaded to handle enhanced functions Flow A Flow Sorter BABB Flow B AAAAA Diffusion Criteria A A

20. 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”> Sensor Server: 12, 15, 14, 17 6, 10, 8, 8, 9 Manager: <event-notification id=”765” event=”link-down” URI=”http://www.cs.columbia.edu/multicast1270”> URI=”http://www.sensor1053.cs.columbia.edu/link7”

21. Example: Trading Processor/Bandwidth * Problem: flows should compress or expand to minimize use of stressed resources in each sensor Sensor Server:

22. Controlling SN With Marks * Marks can be used to configure and control l Sensor functions l End-end QoS delivery by the network and sensors l Routing strategy to maximize QoS, minimize power, etc. l Caching of previous samples (for calculation of statistics)

23. SN Event Processing with Marks * Marks can be used to present event data & control processing l Specify the type of data (seismic, streaming audio, etc.) l The processing of marks at sensors the flow crosses may depend on local conditions (available processor, link bandwidth, etc.)

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