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Improving Performance of Internet Services Through Reward-Driven Request Prioritization. Alexander Totok and Vijay Karamcheti Computer Science Department New York University. Web Server Overload Conditions. Consequences increased request response times some requests are dropped

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improving performance of internet services through reward driven request prioritization

Improving Performance of Internet Services Through Reward-Driven Request Prioritization

Alexander Totok and Vijay Karamcheti

Computer Science Department

New York University

web server overload conditions
Web Server Overload Conditions
  • Consequences
    • increased request response times
    • some requests are dropped
    • successful session throughput suffers dramatically
    • client dissatisfaction → reduced revenues
  • Current solutions work with static client identity
    • session-based admission control (SBAC)
    • service level agreements (SLA)
    • service membership
    • per-client history-based approach
      • looks at a client’s previous visits to the web site
maximizing profit brought by internet services
Maximizing Profit Brought By Internet Services
  • Service profit (reward) maximization
    • shopping web site: number of items sold
  • Idea: assign higher execution priority to the requests, whose sessions are likely to bring more reward
    • how to predict a session’s reward?
  • Our solution:Reward-Driven Request Prioritization (RDRP)
    • predicts a session\'s activities by comparing requests seen in it with aggregated client behavior
    • uses Bayesian inference analysis to dynamically compute request priority in real time
    • contrasts with the per-client history-based approach
service usage profiles patterns
Service Usage Profiles (Patterns)
  • Session structure: first-order Markov chain
    • corresponds to a typical service usage profile (pattern)
  • Shopping scenario for TPC-W application (web store selling books)
    • “Mostly Buyers” profile – more buying activity
    • “Mostly Browsers” profile – more browsing activity
what information does rdrp use
What Information Does RDRP Use?
  • User load structure:
    • {Profilei}; {pi} – percentage of sessions belonging to Profilek
    • on-line request profiling and clustering analysis [Menasce’99]
  • Request reward
    • rewardi:per request type
    • specified by the service provider
    • web shopping scenario: reward(addToCart)=1
  • Relative request execution cost
    • for prediction of future server resource consumption
    • costi: per request type – average request processing time
    • fine-grained profiling of request execution
  • Only request reward is specified by the service provider
how does the algorithm work
How Does The Algorithm Work?

reward_attained + reward_expected

priority =

Step4:

cost_expected

cost_incurred +

prototype implementation in j2ee
Prototype Implementation in J2EE
  • Request priority used to allocate threads and DB connections
evaluation
Evaluation
  • Shopping scenario for TPC-W
    • user load with two usage patterns: “Mostly Buyers”/“Mostly Browsers”
    • new sessions: bursty arrival process (B-model [Wang’02])
  • Techniques compared
    • default – FIFO prioritization
    • session-based admission control (SBAC)
    • per-client history-based approach
      • success depends on how well prediction of a session’s behavior works
      • model different correlation between sessions’ rewards and assigned priorities:

c = 0 (coin flip)

c = 0.25

c = 0.5 (good oracle)

c = 0.75 (very good oracle)

c = 1 (perfect oracle)

    • Reward-Driven Request Prioritization (RDRP)
overload 135 reward
Overload (135%): Reward

similar

The bigger the better

overload 135 response times
Overload (135%): Response Times

The smaller the better

underload 80 response times
Underload (80%): Response Times

The smaller the better

discussion
Discussion
  • Main distinguishing features of RDRP
    • tries to predict future session’s reward
    • is oriented towards session completion
    • works in an abstract application-generic manner
  • May also take into account differences in user think times
    • helps to distinguish between different service usage profiles in the Bayesian inference analysis
  • What if clients do not show stable behavioral patterns?
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Thank You!

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