Notes
Slide Show
Outline
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BenchMANET: MANET Reference Configurations
  • Thomas Kunz
  • Carleton University



  • (joint work with Mohamed Abou El Saoud and Samy Mahmoud)


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Motivation
  • Service Discovery in MANET
    • Allows automatic discovery and location of services
    • Essential for pervasive computing scenario
    • MANETs levy additional unique challenges:
      • Network Topology constantly changing
      • Multi-hop
      • Decentralized
      • Limited battery and processing power
  • Need Comprehensive and Realistic Performance Evaluation Framework to evaluate and compare SDP/MANET protocols.
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BENCHManet Evolution – Tests
  • Classified MANET applications, and derived 10 benchmark tests. Each test represents an application class:
    • Business & Commercial
      • Low Mobility - Collaborative Conference (conf)
      • High Mobility - Event coverage (event)
    • Crisis Management
      • Almost-Static Target - Rescue Operation (rescue)
      • Moving Target - Police Pursuits
    • Personal Area Networks (pan)
    • Residential Mesh (mesh)
    • Vehicle Applications
      • Vehicle-Roadside Networking (vr)
      • Vehicle-Passenger Networking (vp)
    • Military Applications
      • Organized motion - Soldiers Marching (march)
      • Unexpected continuous motion - Combat (combat)
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BENCHManet Evolution – Mobility
  • Assigned suitable mobility model and parameters to each of the 10 benchmark tests.
  • For instance, in a military march:
    • Movement parallel to travel
      • Column Mobility Model
    • Human Speed
      • 1m/s ± 1m/s
    • No Pauses
      • 0s ± 0s

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BENCHManet Evolution –
Other Configurations
  • Each of the 10 tests possess other scenario-specific features. The following parameters were chosen for each test:
    • Spatial Area
    • Network Size
    • Server Agent Ratio
    • User Agent Ratio
    • Services Per Node
    • Service URL Advertisement lifetime
    • Number of Simultaneous Requests
    • Server Duplication Ratio
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Overview of SLP
  • UAs: mediate for users/applications
    • Request Service information (active)
    • Listen to service broadcasts from DAs (passive)
  • SAs: represent services
    • Reply to SrvRqsts (active)
    • Broadcast service information to DAs (passive)
  • DAs: intermediate centralized service brokers
    • Cache service information from SAs
    • Satisfy UA requests accordingly
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SLPManet – Adapted SLP
  • SLPManet implements all required features in SLP specification (RFC2608).
  • Optional features not suitable for MANET:
    • DAs: can not have pre/continual-existing nodes.
    • Authentication Blocks: digital signatures for security, not goal of research.
    • Other optional messages: add complexity and consume scarce resources.
  • Designed for small multi-hop MANETs under cooperative administrative control.
  • Provided additional caching of service requests/reply
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Evaluation – Experimental Setup
  • SAs and UAs picked randomly from [0,NS)
  • Simulation Time = 2000s
  • Service Requests
    • Total per scenario = 1000
    • Inter-arrival = exponential [1000s, 2000s), mean=1360s
    • Each UA requests ~1000/UAs services
    • Each request is for a random service type
  • BENCHManet test suit run 10 times with different movement files.
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Evaluation – SLPManet (1)
  • Overall Discovery Success
    • 9/10: > 95%
    • combat: 50% due to troublesome mobility and topology



  • Service Lookup Latency
    • Peak latency high for scenarios where nodes are further distant apart, resulting in many retransmissions before a SrvRply is successfully received
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Evaluation – SLPManet (2)


  • Bandwidth Consumption
    • mesh: large number of duplicated services and large number of nodes causing longer delays and more retransmissions of SrvRplys


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Evaluation – Improvement (1)
  • Overall Discovery Success
    • 9/10:   0% – 1%
    • combat:   35%




  • Service Lookup Latency
    • Average:   24% – 98%
    • Peak:   0% – 82%



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Evaluation – Improvement (2)
  • Bandwidth Consumption
    • Average:   15% – 97%
    • Peak:   -8% – 58%
    • vr: deterioration not statistically significant

  • Aggregate Bandwidth
    • 16% – 96%



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Summary of Contributions
  • Provided Taxonomy of MANET applications.
  • Provided BENCHManet, a practical comprehensive performance evaluation framework for MANET protocols.
  • Evaluated performance of SLPManet using BENCHManet.
  • Proposed and implemented a simple extended caching modification to SLPManet.
  • Evaluated performance gain of improvement.
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Future Research
  • Compare current active-discovery SLPManet with passive-discovery versions.
  • Compare SLPManet against other SDPs.
  • Comparison between application-level service discovery and cross-layer service discovery proposals.
  • Enhance BENCHManet by using more sophisticated mobility models.