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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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