Opportunistic Traffic Scheduling Over Multiple Network Path

1
Opportunistic Traffic Scheduling Over Multiple
Network Path

• Coskun Cetinkaya and Edward Knightly

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Multi-Path Routing

• Establishes and simultaneously uses multiple
  parallel paths
• Key advantage is efficiency
• Routing protocol assigns weights to paths
• OSPF, QoS routing, traffic engineering

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Existing Splitting Techniques

• Per packet round robin forwarding
• Simplest and most frequently used
• Degrades TCP throughput due to re-ordering
• Per flow hashing
• Fine splitting granularity and no TCP re-ordering
• Per-TCP-flow lookup limits implementation
  feasibility
• Destination prefix based forwarding
• Coarse-granularity splitting and no TCP
  re-ordering
• Unpredictable load splitting that may not match
  desired weights

All ignore path quality in splitting decision
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Our Thesis

• Observe
• Routing weights change slowly (from traffic
  engineering)
• Quality of paths changes continuously
• Opportunistic Multipath Scheduling
• Exploits short-term capacity variations on
  different paths via scheduling packets to
  opportunistically favor low-delay paths
• Obey weights at long time scales to ensure
  global objectives
• Hypothesis
• Improve throughput/delay, no per-flow lookup,
  satisfy weights
• TCP throughput improvements due to RTT reduction
  will overwhelm re-ordering effects

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System Model

• Design scheduling/traffic splitting policy
• Objective minimize mean delay of multipath
  traffic
• Decrease RTT and loss rate ? increase TCP
  throughput
• Subject to mean traffic on path i Fi (path
  weight)

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Mathematical Formulation

• Xk size of packet k
• I(sk,i) 1 of packet k is scheduled on path i, 0
  otherwise
• For equal capacity paths minimizing delay is
  equivalent to minimizing the expected queue
  length

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Optimal Scheduler

• Assumptions
• Cross-traffic and multi-path traffic are
  stationary processes ? queue length is stationary
  
• Multi-path traffic does not change path
  conditions
• Using a wireless scheduling analogy LCS02, we
  can show that the optimal scheduler is threshold
  based

• Contrast to join the shortest queue policy
  which ignores weights

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Performance of the Optimal Policy

• Evaluate the performance under self-similar cross
  traffic
• Queue size distribution is Weibull
• Expected queue size (and delay)
• Round Robin Optimal Scheduler

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On-Line Computation of v

• In practice, we do not know the queue length or
  its distribution
• Threshold update
• stochastic approximation technique KC78,LCS02
• Scheduling decision
• Qik estimated via probes

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Evaluation Scenario

• Two paths with capacity 10 Mb/sec
• Cross-traffic self-similar with mean rate
  m?0.3, 0.9, variance coefficient a?0.5,4, and
  Hurst Parameter H?0.5,0.9
• Multi-path traffic is constant-rate or TCP
• Gain defined as

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Homogeneous Paths Model

• Model gain depends only on H and paths and is
  ? 50
• Higher N ? more path diversity ? higher gain
• Large H ? long-time scale path correlation ?
  higher gain

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Homogeneous Paths Simulation

• Simulated gains higher than predicted by model
• Model serves as lower bound
• Queue distribution is asymptotic lower bound,
  tighter for larger queues
• Delay increases with increasing mean (m) and
  variance coefficient (a)
• Gain (relative) is highest under higher H, lower
  m, lower a

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Heterogeneous Paths Impact of Variance
Coefficient Ratio

• Gain increases with path diversity (increasing
  ratio of variance coefficient)
• OMS exploits different path properties subject to
  weights

H0.6 m0.7
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Effect of Information Delay

• So far, assumed path information is immediately
  available at scheduler/splitter
• RTT-scale delay to obtain buffer state (via
  probes or ECN)
• Gain decreases as information delay increases
• High gain for measured values of traffic (0.7 lt H
  lt 0.85) and delay (1 lt RTT lt 100 msec)

m0.9 a0.5
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Limits of OMS

• When can OMS do worse than RR? Three combined
  factors
• iid traffic having no long-time-scale bursts
• High information delay
• High ratio of multi-path traffic to cross-traffic
  (scheduled traffic itself determines conditions)

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TCP Multi-Path Traffic

• With RR, multipath traffic achieves only 20 to
  38 of fair share
• High cost of mis-ordering and delay
• TCP/OMS significantly outperforms TCP/RR
• TCP/OMS requires an aggregate level of only 10
  cross-traffic flows to achieve maximum
  performance
• OMS impact overwhelms effect of TCP variants

10 msec probing interval 32 kb/s probing
overhead (0.32 of capacity)
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Probing Interval and TCP Traffic

• Base case probing interval 10 msec interval and
  32 kb/sec
• Faster 1 msec probing yields higher-than-fair
  share for multi-path flows
• Slower probing (e.g., 3.2 kb/sec) reduces
  performance

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Summary

• Multipath routing promises increased efficiency
  and performance
• Todays traffic splitting ignores path dynamics
  and
• inhibits TCP throughput via reordering,
• requires expensive per-TCP flow lookups, or
• cannot achieve weights via prefix splitting
• Opportunistic Multipath Scheduling
• Improves throughput/delay via a measurement based
  opportunistic policy that satisfies routing
  weights
• Gains overwhelm occasional misordering

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