Modelling TCP Reno with Spurious Timeouts in Wireless Mobile Environments

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Modelling TCP Reno with Spurious Timeouts in
Wireless Mobile Environments

• Shaojian Fu
• School of Computer ScienceUniversity of
  Oklahoma.
• Email sfu_at_ou.edu

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Introduction

• Delay spikes are especially more frequent in
  today's wireless mobile networks than in
  traditional wired network, which will cause
  Spurious Timeouts (ST).
• Previous studies on Modelling TCP performance
  over wireless networks focus on the impact of
  wireless random losses.
• Spurious Timeouts must be considered explicitly
  to accurately model the steady state sending rate
  and throughput of TCP.
• Proposed an analytical model for the sending rate
  and throughput of TCP Reno as a function of
  packet error rate and characteristics of spurious
  timeouts.

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Outline

• The background on Delay Spike and Spurious
  Timeout provided.
• Impact of this model on future transport protocol
  research.
• Modelling assumptions.
• Analytical model.
• Validation of the model with simulation works.
• Compare with previous model on TCP perfromance.

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Examples of Delay Spikes
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Events causing delay spikes in wireless mobile
environment

• The handoff of a mobile host between cells
  requires the registration with the base stations.
• The physical disconnection of the wireless link
  during a hard handoff.
• Retransmission at Radio Link Control (RLC) layer,
  e.g. GPRS and CDMA2000.
• Higher-priority traffic, such as circuit-switched
  voice, can preempt (block) the data traffic
  temporarily.

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Spurious timeout illustrated
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Senders congestion window
Spurious Timeout
Begin transmit new data
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Previous models on TCP performance

• Early TCP analytical models only consider slow
  start and congestion avoidance.
• Recent models take into account the RTO timeout
  caused by random losses during transmission, such
  as the model proposed by Padhye et. al. (Referred
  as PFTK model in our paper).
• Since Spurious Timeouts are not frequent in wired
  networks, they are considered to be a transient
  state, and thus cannot produce much impact on the
  steady state performance of TCP.
• In wireless mobile environment, Spurious Timeouts
  are more frequent. They must be modelled
  explicitly to estimate the steady state
  performance of TCP more accurately.

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A new analytical model considering the
characteristics of Spurious Timeouts

• Impacts of Spurious Timeouts are explicitly
  built into the analytical TCP performance model.
• Stochastic analysis of the steady state sending
  rate and throughput of TCP Reno as a function of
• packet error rate,
• interval between long delays,
• duration of long delays.
• The model proposed by Padhye et. al. (referred as
  PFTK model) can accurately predict TCP
  performance over a wide range of loss rates. We
  use this model as a basis of our work.

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Possible application of the model

• Fundamental trade off between the detection
  rapidness of actual losses versus the risk of
  unnecessary retransmissions
• small RTO fast detection, more risk of spurious
  timeout
• large RTO slow detection, less risky but long
  stall time.
• RTOmin has significant impact on RTO value,
  common practice is set it to 2clock. This model
  can assist in determining an appropriate value of
  RTOmin since it considers spurious timeouts
  explicitly.
• Help evaluating the impact of lower layer
  protocols settings on the performance of TCP.
• Help evaluating different TCP modifications
  designed for alleviating the effect of Spurious
  Timeout.

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Modelling Assumptions

• The sending rate is not limited by the advertised
  receiver window, and the sender always has
  sufficient data to send.
• Segment losses in a round are independent from
  losses in other rounds. All other segments which
  were sent after the first lost segment in a
  specific round are also lost.
• The time required to send a window of data is
  smaller than an RTT.
• No RTT fluctuation measurements caused by queuing
  delays. In the absence of delay spikes, the RTT
  measurements compose a stationary random process.

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Stochastic model of long delay pattern
Variation of RTT showing delay spikes
Markov Chain model
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One Long Delay Period (LDP)
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One Long Delay Cycle (LDC)
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Steady-state Sending Rate Estimation

• Consider LDC as the basis for steady state
  sending rate calculation, instead of using NP in
  PFTK model.
• The proposed model considers a larger time scale
  than PFTK model one LDC is composed of several
  NP and one LDP.
• A high-level expression of the model

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Steady-state Throughput Estimation

• Use the sending rate as the basis of throughput
  estimation.
• Subtract dropped segments and multiple
  retransmitted segments for the same segment from
  total number of segments sent during NP.
• Subtract dropped segments and spuriously
  retransmitted segments from total number of
  segments sent during LDP.
• The duration of LDC unchanged for throughput
  estimation.

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Simulation Setup
Topology
Parameters
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Sending rate estimation comparison (200ms RTT)
RTT200ms E(I)30s
RTT200ms E(I)240s
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Sending rate estimation comparison (400ms RTT)
RTT400ms E(I)30s
RTT400ms E(I)240s
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Throughput estimation comparison (200ms RTT)
RTT200ms E(I)30s
RTT200ms E(I)240s
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Throughput estimation comparison (400ms RTT)
RTT400ms E(I)30s
RTT400ms E(I)240s
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Estimation error vs. LDF
Throughput estimation error
Sending rate estimation error
LDF E(D)/E(I)
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Estimation error vs. RTT
Throughput estimation error
Sending rate estimation error
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Estimation error vs. packet error rate
Throughput estimation error
Sending rate estimation error
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Conclusion

• The proposed model can characterize the impact of
  delay spikes with different patterns on TCPs
  performance.
• The proposed model is more accurate than the PFTK
  model in estimating the steady state sending rate
  and throughput of TCP, especially in presence of
  frequent long delays.
• Future research can be made on applying the model
  in TCP RTO setting selection or lower layer
  retransmission protocol design evaluation.