Service Discovery using OLSR and Bloom Filters Joakim Flathagen - - PowerPoint PPT Presentation

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Service Discovery using OLSR and Bloom Filters Joakim Flathagen - - PowerPoint PPT Presentation

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Service Discovery using OLSR and Bloom Filters Joakim Flathagen 4th OLSR Interop / joakim.flathagen@ffi.no Workshop, Ottawa, CA, Oct 14 2008 Presentation outline Who is involved Motivation Service Discovery General solutions

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Service Discovery using OLSR and Bloom Filters

Joakim Flathagen joakim.flathagen@ffi.no

4th OLSR Interop / Workshop, Ottawa, CA, Oct 14 2008

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Presentation outline

  • Who is involved
  • Motivation
  • Service Discovery

– General solutions – MANET solutions

  • Our OLSR based Service Discovery Design

– How does it work? – Simulation results – Implementation and use

  • Summary + Future work
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Who is involved?

  • Norwegian Defence Research Establishment (FFI) – Soldier

Modernization Program (NORMANS) – Developing wearable computing for the future soldier – Command Control, Radio technology, HMI etc.

  • UniK – University Graduate Center

– Research and education institute owned by the University of Oslo (UiO) and Norwegian University of Science and Technology (NTNU) – 60 Phd students and 50 M.Sc students

  • Researching Mobile Ad-hoc Networks aimed both for civilian

and military purposes.

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History – OLSR for soldier systems

  • Wearable computing for soldiers 2001
  • Prototypes of UHF 802.11b radios developed 2002
  • UniK OLSR developed 2004 (www.olsr.org)
  • Field trials conducted 2005 running OLSR on UHF Radios
  • New soldier systems developed and fielded 2008 (NORMANS)
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Motivation

  • Main goal:

– Increasing situational awareness and reduce fratricide through automated technology – Reduce user interaction to a minimum

  • Service oriented networks down to the soldier
  • Every vehicle, soldier, headquarter is equipped with services

such as: – Sensors: Chemical detectors, battery indicator, HR-monitor – Tools: Laser Range Finder, Night vision, Map-server – People: Squad leader, commander.

  • Our aim is bandwidth constrained MANETs (< 100Kbps)
  • Also applicable to first responder networks
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Service Discovery

  • Aim: Find the IP-addresses of services and resources without

user interaction.

  • General service discovery solutions are not applicable to

MANETs: – Bad scalability – To much overhead – Relies on directories

  • Tailor made solutions must be provided for MANETs
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Service Discovery in MANETs

  • Proposed solutions differ in many aspects:
  • Service Descriptors

– WSDL, GUID / Integer, Text, Bloom Filters

  • Architecture

– Directory, Directory-less, Hybrid

  • Discovery mode

– Proactive, Reactive

  • Layer

– Application Layer Service Discovery

  • (SLPManet, PDP, Konark, Sailhan)

– Cross-Layer service discovery

  • AODV (SEDRIAN, Engelstad)
  • OLSR (Li, Jodra)
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Our proposal - Mercury

  • Directory-Less

– Fully Distributed – Reactive discovery with a proactive taste

  • Service dissemination using OLSR
  • Service descriptors defined as Bloom Filters
  • Caching to reduce overhead and discovery latency
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Mercury - Functionality

  • Mercury provides an interface to provide service discovery for all

applications on the computer

  • Each node employs three repositories:

– Advertised services (distributed on-demand) – Foreign services (caching incoming advertisements) – Requested services

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How it works

  • Services are registered locally by all applications
  • Service are advertised on request
  • Service requests are disseminated if the cache is empty for the

given service (or if the app searches for ALL services).

  • All service advertisements are cached for a period of time.
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Mercury Protocol

  • OLSR default forwarding algorithm ensures backwards

compatibility – Not every node needs to run Mercury.

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Bloom Filters

  • Bloom Filters allows data-representation in a space efficient

manner by hashing service descriptors.

  • As an effect, Bloom Filters allow false positives
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Bloom Filters

  • Bloom Filters are used to represent all services.
  • Distribute a series of services: Space efficient, predictable, and

does not interfere with OLSR operation

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Simulation Results - Overhead

  • Average discovery process compared to App-level SD

Piggybacking and Bloom Filters Caching

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Simulation Results - Delay

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Caching

  • Caching in mobile networks are challenging as the cache may

be false due to mobility.

  • We propose a path-aware caching approach

– Consults the OLSR routing table on service lookup – Reduces false positives to application – However, the routing table may still be false.

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Simulation Results – Caching effect

  • Simulations show the probability of the cache to return false

positives upon a query.

  • Using a path-aware cross-layer approach, the false positive

probability almost eliminated

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OLSRd implementation

  • Mercury is implemented as a plugin to OLSRd (www.olsr.org)
  • Applications connect, advertise, discover and withdraw services

using a simple socket interface

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Discovery of SIP UAs

  • Using the Inter-process communication interface, any existing

distributed application can be extended to utilize Mercury.

  • Simple socket Code.

mySD = new Socket("localhost",port);

  • ut = new PrintWriter(mySD.getOutputStream(), true);

in = new BufferedReader(new InputStreamReader( mySD.getInputStream()));

  • ut.println("ADVR SIP");
  • ut.println("RQST SIP ALL");
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Summary & Future work

  • OLSR facilitate cross-layer support of Service Discovery
  • Bloom Filters is an flexible and efficient way to distribute

services.

  • Caching is beneficial (use with caution).
  • The OLSRd plugin library is an efficient way to implement

service discovery.

  • Future work:

– Real-world trials. – Optimize OLSR and SD settings for bandwidth-constrained environments – Examining different movement patterns for simulation

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SLIDE 21

References

  • [1] M. Abou El Saoud, T. Kunz, and S. Mahmoud. SLPManet: service location protocol for
  • MANET. In IWCMC ’06: Proceeding of the 2006 international conference on

Communications and mobile computing, pages 701–706, New York, NY, USA, 2006.

  • [2] B. H. Bloom. Space/time trade-offs in hash coding with allowable errors.

Communications of the ACM, 13(7):422–426, 1970.

  • [3] C. Campo, C. Garc’ia-Rubio, A. M. Lopez, and F. Almenarez. PDP: a lightweight

discovery protocol for local-scope interactions in wireless ad hoc networks. Comput. Networks, 50(17):3264–3283, December 2006.

  • [4] S. Cheshire and M. Krochmal. DNS-Based Service Discovery, Au- gust. INTERNET-

DRAFT draft-cheshire-dnsext-dns-sd-04.txt, Work in progress, 2006.

  • [5] T. Clausen and P. Jacquet. Optimized Link State Routing Protocol (OLSR). RFC 3626

(Experimental), October 2003.

  • [6] P. E. Engelstad, Y. Zheng, R. Koodli, and C. E. Perkins. Service discovery architectures

for on-demand ad hoc networks. International Journal of Ad Hoc and Sensor Wireless Networks, Old City Publishing (OCP Science), 2(1):27–58, March 2006.

  • [7] J. Flathagen. Mercury Service Discovery Plugin for OLSRd. (http://olsr-

mercury.sourceforge.net), Accessed 2008.

  • [8] Y. Goland, T. Cai, P. Leach, and Y. Gu. Simple service discovery protocol/1.0.

INTERNET-DRAFT draft-cai-ssdp-v1-03.txt, Work in progress, 1999.

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SLIDE 22

References

  • [9] E. Guttman, C. Perkins, J. Veizades, and M. Day. Service Location Protocol, Version 2.

RFC 2608 (Proposed Standard), June. Updated by RFC 3224, 1999.

  • [10] S. Helal, N. Desai, V. Verma, and C. Lee. Konark - a service discovery and delivery

protocol for ad-hoc networks. Proceedings of the Third IEEE Conference on Wireless Communication Networks (WCNC), New Orleans, 2003.

  • [11] J. L. Jodra, M. Vara, J. M. Cabero, and J. Bagazgoitia. Service discovery mechanism
  • ver OLSR for mobile ad-hoc networks. Advanced Information Networking and Applications,

AINA, 2:534–542, 2006.

  • [12] L. Li and L. Lamont. A lightweight service discovery mechanism for mobile ad hoc

pervasive environment using cross-layer design. Pervasive Computing and Communications Workshops, pages 55–59, 2005.

  • [13] J. Macker. Simplified multicast forwarding for manet. INTERNET- DRAFT draft-ietf-

manet-smf-05, Work in progress, 2007.

  • [14] Martineau, Y. Peers SIP User Agent. (http://peers.sourceforge.net/), Accessed 2008.
  • [15] Naval Research Laboratory. NRL-OLSR. (http://cs.itd.nrl.navy.mil/), Accessed 2008.
  • [16] A. Obaid, A. Khir, and H. Mili. A Routing Based Service Discovery Protocol for Ad hoc
  • Networks. In ICNS ’07: Proceedings of the Third International Conference on Networking

and Services, 2007. [17] olsr.org. The OLSR daemon. (http://www.olsr.org/), Accessed 2008.

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SLIDE 23

References

  • [18] C. Perkins, E. Belding-Royer, and S. Das. Ad hoc On-Demand Distance Vector (AODV)
  • Routing. RFC 3561 (Experimental), July 2003.
  • [19] R. Rivest. The MD5 Message-Digest Algorithm. RFC 1321 (Informa- tional), April 1992.
  • [20] Sun. Jini. (http://www.jini.org/), Accessed 2008. [21] A. Tønnesen, A. Hafslund and Ø.
  • Kure. The Unik-OLSR Plugin Library. In The OLSR Interop and Workshop, 2004.
  • [22] University of California. ns2 Network Simulator. (http://www.isi.edu.nsnam/ns/),

Accessed 2008.

  • [23] University of Murcia. UM-OLSR. (http://masimum.dif.um.es/), Ac- cessed 2008.