Traffic Forecasting Medium Access

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					Traffic Forecasting Medium
           Access
        TRANSFORMA

        Vladislav Petkov
        Katia Obraczka


                             1
                      Motivation
• Goals of a good wireless MAC protocol
   –   High channel utilization
   –   Predictable performance under heavy load
   –   Energy efficiency
   –   Low latency
• Schedule based MACs provide first three at expense of
  the fourth
• We want to address that problem
• Using per-flow traffic forecasting:
   – Can determine rate of service flow needs
   – Allocate just right amount of resources to each flow


                                                            2
               Related work
• Other schedule based approaches:
  – Traffic Adaptive Medium Access (TRAMA)
  – Flow Aware Medium Access (FLAMA)
  – Dynamic Multi-Channel Medium Access
    (DYNAMMA)




                                             3
TRANSFORMA design
            • Time slotted
              channel
            • 2-hop
              neighborhood
              information
              propagated
            • Distributed
              medium
              scheduling

                             4
                  Control Plane




A flow is defined by:      Each flow has own:
• Source IP, source port   • Queue
• Dest. IP, dest port      • Traffic forecaster
                           • Transmission opportunities
                                                      5
Traffic forecaster
         For every packet arrival traffic
         forecaster runs following algorithm:
         1.Computes the latest packet
         interval, τ
         2.Calculates loss of each expert, xi
         3.Reduce weights of bad experts
         4.Share some of remaining weights
         5.Calculate new forecaster output




                                                6
Flow selection algorithm
            • Rate Monotonic Algorithm
              approximated to select flow for
              each slot
                – Flow fi, with interval ti,
                  pseudorandomly chooses one slot in
                  each ti interval
                – Ties are broken by flow and node ID
                  hash
                – Flows persist (try for every slot) if
                  they fail to win their chosen slot
            • Flows with interval below τmin are
              considered best-effort
            • We empirically determine best
              τmin value (currently 10ms)
            • TRANSFORMA guarantees
              collision free medium access
              under all conditions
                                                          7
           Performance evaluation
                                    Hotspot layout
• We use Qualnet Network
  Simulator
• PHY is 802.11a at 6.0Mbps
• Radio range is ≈400m
• 2 experiments:
   – Heterogeneous flows
       • Variable number of CBR
         flows of different rates
   – Real-time vs. best-effort
       • 3 real-time flows with
         increasing amount of
         background traffic
                                                     8
 Expt 1 results: Heterogeneous Flows
                   DYNAMMA (a schedule based
TRANSFORMA         protocol)




                                               9
Expt 2 results: Real-time vs best-effort
                               Foreground & background
Foreground application delay   goodput




                                                         10
    Conclusion and future work
• TRANSFORMA has predictable performance,
  even at high load
• TRANSFORMA delivers lower delays to delay
  sensitive applications than DYNAMMA and
  even 802.11 under high load
• Future work:
  – Implementation based experimentation with
    more applications
  – Ways of adding routing awareness in scheduling

                                                     11

				
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posted:7/17/2011
language:English
pages:11