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The Network Flow Language (NFL) for Active Sensor Networks (ASNs)

The Network Flow Language (NFL) 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. ASN.

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The Network Flow Language (NFL) for Active Sensor Networks (ASNs)

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  1. The Network Flow Language (NFL)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

  2. Toward Programmable Adaptive SNs ASN User Needs + A Resource Availability SN Scenario • 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

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

  4. The NetScript Language • 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

  5. NetScript Dataflow Model • Example: IP stack • Dynamic deployment of new stack components • 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 • 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

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

  8. 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 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 Server NFL flow NetScript boxes and links Node

  10. 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

  11. 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 control • Target is very fast web access • Looking at other systems proposed by SenseIT researchers • Implement components in NFL • Drive the design and implementation of the language

  12. Near Future Challenges • Porting NetScript to WINS • NetScript currently works on Java VM • Heavy use of resources • Java VM for Windows CE • Port dynamic deployment of boxes • Box servers come and go • Single 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

  13. Examples of NFL Applications

  14. Example: Prioritization of Samples • Problem: samples have different levels of priorities and the network has to prioritize delivery of flows Data flows In 7 1 0 5 10 0 NetScript boxes and links Priority Sorter Priority Queue Node Data flow Out 7 5 1 10 0 0

  15. 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>

  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: <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>

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

  19. Example: Simple Diffusion Routing (II) • Problem: handling failures to links and nodes • New NetScript boxes are loaded to handle enhanced functions B A A B B Flow A Flow Sorter Diffusion Criteria Flow B A A A A 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”> </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>

  21. Example: Trading Processor/Bandwidth • Problem: flows should compress or expand to minimize use of stressed resources in each sensor Sensor Server: <flow URI=”http://www.display7.cs.columbia.edu”> <conditional-compress condition=”bandwidth < 1Mbps”> <image> <!-- image data> </image> </conditional-compress> </flow>

  22. 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)

  23. 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.)

  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 • 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

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