Online Feedback-based Estimation of Dynamic Page Service Time
Ashwini Kumar Kaushik Veeraraghavan Benjamin Wester Kang Shin Slide
2 2 Motivation Dynamic pages Increasingly prevalent Pages difficult
to cache Service time applications Differentiated QoS (prioritizing
requests) Load balancing Virtual host services Estimates: credit
card payment times Slide 3 3 Idea History Maintain records of past
service times Predict based on existing records Assume a
correlation between past and future What is the invariant property?
What system state can we store to track it? We explore three
estimators and their design Slide 4 4 Outline Motivation Design
& Implementation Evaluation Conclusion and Future Work Slide 5
5 History 1.Maintain per-URL fixed-size history table 2.Data
depends on the estimator used 3.Tag incoming requests 4.Update
history when request processing finishes Slide 6 6 SEDA: Haboob
Slide 7 7 Sirocco Slide 8 8 Estimators SchemeStore in
historyEstimation Average STQ STQT Slide 9 9 Outline Motivation
Design & Implementation Evaluation Conclusion and Future Work
Slide 10 10 Evaluation How close are our estimators? Load patterns
Steady load: httperf 50ms / request Changing load: SURGE Slide 11
11 Average Estimator (steady) Request takes ~35ms to complete
Estimator should not be too responsive Slide 12 12 Average
Estimator (changing) Each point averages 20 requests Vertical bars
indicate the addition of a handling thread Slide 13 13 Average
Estimator How to read the graph: Enters system:t = 5.4s Estimated
service time:1.6s Real service time:4.9s Slide 14 14 Average
Estimator History becomes out-of-date Base estimation on the
current state of the system Slide 15 15 STQ Estimator Its better
than the Average Estimator Inaccurate around a thread change Slide
16 16 STQ Estimator Estimations will be inaccurate unless the
estimator knows about the coming change in the number of threads
After a change, recently-completed requests show a mixed view of
system state and will pollute the history Slide 17 17 STQT
Estimator Overshoots target because of a polluted history Fix:
Integrate request times across the thread change Slide 18 18
Outline Motivation Design & Implementation Evaluation
Conclusion and Future Work Slide 19 19 Concluding Remarks Takeaways
Dont adapt to transient delays Use current state along with the
history Be aware of underlying changes Future Directions
Extrapolate requests service times URL service time as a
distribution Slide 20 Questions?
