Predicting and Bypassing EndtoEnd Internet Service Degradation

1
Predicting and Bypassing End-to-End Internet
Service Degradation

• Anat Bremler-Barr Edith Cohen Haim Kaplan Yishay
  Mansour
• Tel-Aviv University ATT Labs
  Tel-Aviv University
• Talk
• Omer Ben-Shalom
• Tel-Aviv University

2
Outline

• Degradation
• deviation from normal (minimum) RTT.
• Predicting Degradation
• Different Predictors
• Performance Evaluation
• Precision/recall methodology
• Suggested Application Gateway selection

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Motivating Application

AS 41
AS 123
Peering link
AS 56
Peering link
AS 12

• Gateway selection (Intelligent Routing device)

• Choosing peering links

4
Data and Measurements Sources

• Aciri (CA2)
• ATT (CA1)

• ATT(NJ1)
• Princeton (NJ2)

• Base Measurements from 4 different location (AS)
  simulated 4
• gateway
• California (CA) ATT ACIRI
• New Jersey (NJ) ATT Princeton

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Data and Measurements Destinations

• Aciri (CA2)
• ATT(CA1)

• ATT(NJ1)
• Princeton (NJ2)

• Obtaining a representative sets of web servers
  weights
• (derived from proxy-log)

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Data and Measurements RTT

• Aciri (CA2)
• ATT(CA1)

• ATT(NJ1)
• Princeton(NJ2)

• Data Weekly RTT (SYN) ( End to End
  (pathserver))
• Hourly measurements ? 35,124 servers
• Once-a-minute weighted sample measurements ? 100
  servers

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Degradation Definition

• Deviation from minimum recorded RTT (propagation
  delay)
• Discrete degradation levels 1-6.

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Objective Avoiding degradation ?

• Attempt to reroute through a different gateway
• Two conditions have to hold
• Need to be able to predict the failure from a
  gateway
• Need to have a substitute gateway (low
  correlation between gateways)
• Blackout (consecutive degradation) through one
  gateway

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Blackout durations

• Longer duration, easier to predict.
• Majority of blackouts are short 1-3 consecutive
  points
• However, considerable fraction occurs in longer
  durations.

Long duration blackout
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Gateways Correlation

• Gateways are correlated but often the correlation
  is not too strong

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Gateways Correlation

• Longer blackouts more likely to be shared
• failure closer to the server
• Majority of 2-gateways blackouts involved
  same-coast pairs

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Building predictors

• For a given degradation level l.
• Prediction per IP.
• Input Previous RTT Measurements for the
  IP-address.
• Output probability for a failure
• Predict failure if probability gt ?

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Precision \ Recall Methodology
Predicted degraded
Actual degraded
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Precision-recall curve

• Sweep the threshold ? in 0,1 to obtain a
  precision-recall curve.
• In other words, let P(t) the predicted failure
  probability at time t

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What is important for prediction?

• Recency principle
• The more recent RTTs are more important.
• Quantity Principle
• The more measurements the higher the accuracy.

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Recency Principle Importance

• Test case Single measurement predictor
• predict according to a measurement x-minute ago.
• observe the change in the quality of the
  prediction.
• ? 15 different between using the last minute
  measurement or the 15 minutes ago measurement

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Quantity Principle Importance

• Test case Fixed-Window-Count (FWC)
• the prediction is the fraction of failures in
  the W most recent measurements
• ? By quantity we can achieve better precision
  for high recall

FWC 1 FWC 5 FWC 10 FWC 50
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Our predictors

• Exponential Decay
• Polynomial Decay
• Model based Predictors
• VW-cover Variable Window Cover algorithm
• HMM Hidden Markov Model

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Exponential-decay predictors

• The weight of each measurement is exponentially
  decreasing with its age by factor ?.
• For consecutive measurements
• Binary variable ft represents a failure at
  time t.
• In general,

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Polynomial-decay predictors

• Exact computation required to maintaining the
  complete history.
• We approximated it.

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The VW-Cover predictor

• Consists of a list of pairs
• ( a1 , b1) ( a2 , b2 ) ( an , bn )
• Predict a failure if exist i such that there are
  at least bi failures among previous ai
  measurements

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VW-Cover predictor Building

• Build the predictor greedily to cover the
  failures.
• Use a learning set of measurements
• Pick ( a1 , b1 ) to be the pair which maximizes
  precision
• Pick ( ai , bi ) to be the pair which maximizes
  precision among uncovered failures

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Hidden Markov Model

• Finite set states S (we use 3 states)
• Output probability as(0),as(1)
• Transition function, determines the probability
  distribution of the next state.
• The probability for a failure
• Where ps(t) is the probability to
• be at state s at time t. Ps(t) is
• updated according to the output
• of time t-1.

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Experimental Evaluation
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Predictor Performance Level 3
FWC10 FWC 50 ExpDecay 0.99 ExpDecay
0.95 VW-Cover HMM
? A recall 0.5 precision close to 0.9
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Predictor Performance Level 6
FWC10 FWC 50 ExpDecay 0.99 ExpDecay
0.95 VW-Cover HMM

• Degradation of level-6 are harder to predict
• recall 0.5 precision 0.4

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Predictor Performance Conclusion

• The best predictors in level 3 and 6 are
• VW-cover and HMM
• But they only slightly outperform ExpDecay0.95
  which is considerable simpler to implement

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Gateway Selection
Level 6
Level 3
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Gateway Selection Conclusion

• Active gateway selection resulted in 50
  reduction in the degradation-rate with respect to
  best single gateway.
• Static gateway selection can avoid at most 25 of
  degradations.
• Again ExpDecay0.95 only slightly under perform
  the best predictor (VW-cover).

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Performance of gateway selection as a function of
recency
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Correlation between coast

• Gateway selection on same-coast pair resulted
  only in 10 reduction.
• Chose independent gateways

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Controlling prediction overhead

• Type of measurements
• Active measurements
• initiate probes (SYN,ping,HTTP request).
• Scalability problem.
• Passive measurements
• collected on regular traffic
• Controlling the prediction overhead
• Using less-recent measurements
• Active measurements only to small set of
  destinations, which cover the majority of
  traffic.
• Cluster destinations. The measurements of one
  destination can be used to predict another.

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• Questions ??
• natali_at_cs.tau.ac.il
• edith_at_research.att.com
• haimk_at_cs.tau.ac.il
• mansour_at_cs.tau.ac.il