Slide 1
Rob Lancaster, Orbitz WorldwideSurvival Analysis &TTL
OptimizationOutlineThe ProblemSurvival AnalysisIntroKey
TermsTechniques & Models:Kaplan-Meier EstimatesParametric
ModelsOptimizing Cache TTLMethodsResults
The ProblemThe hotel rate cache and TTL optimization.The Hotel
Rate Cache
The Hotel Rate CacheKey/Value StoreKey: Search Criteria
Value: Hotel Rate Information
Benefit = Reduce looks & latencyCost = Increased re-price
errors
hotel idcheck-in# peoplehostcheck-out# roomsThe Hotel Rate
CacheEach cache entry is given a time-to-live (TTL)TTLs set based
on intuition ages ago.Goal: Optimize TTL to decrease looks, control
re-price errorsHow? Ideally, find greatest TTL value at which
probability of rate change is below an acceptable threshold.
Survival AnalysisA brief? introduction.What is Survival
Analysis?Statistical procedures for predicting time until an event
occurs.Event: death, relapse, recovery, failure.Examples:Heart
transplant patients:Time until death.Leukemia patients in
remission:Time until relapse.Prison parolees:Re-arrest.
Key TermsSurvival Time, T vs. tFailureCensoringSurvival
Function
CensoringPeriod of no
informationLeft-censored.Right-censored.Causes:Individual is lost
to follow-upDeath from cause unrelated to event of interestStudy
endsModels assume either failure or censoring.Survival
FunctionSurvival Function: S(t)Probability of survival greater than
t, i.e. that T > tProperties:Non-increasingS(t) = 1, for
t=0.S(t) = 0, t=
Kaplan-Meier
Estimatestjmjqjnj0001411014211134211160287106910510224tj:
observation timemj: number of failuresqj: number of censored
observationsnj: number at risk
Kaplan-Meier Estimates
Parametric ModelsAccelerated Failure TimeAssume distributionUse
regression to fit parameters. is parameterized in terms of
predictor variables and regression parameters.
DistributionS(t)ExponentialWeibullLog-logistic
Optimizing Cache TTLMethods and early results.Data
CollectionData is collected from service hosts in our hotel
stack.Includes every live rate search (aka burst) performed by our
hotel stack.Raw data: ~200 GB, compressed, 108
records.Extraction:
