zhichun li the robust and secure systems group at nec research labs northwestern university ...
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Zhichun Li
The Robust and Secure Systems group at NEC Research Labs
Northwestern University
Tsinghua University
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Almost everything is related to Web
Web search Web mail Maps Online shopping Online Social network Calendar
Amazon: 100ms extra delay 1% sale loss Google search: 500 ms extra delay reduce
display ads revenues by up to 20%
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Example of Yahoo Maps 110 embedded objects Complex object dependencies 670KB JavaScript multiple data‐centers around the world
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A/B test (controlled experiments)
Idea: • Measure through experiments by various
service setting
Problems:• Hard to fully automated• Expensive to set up• Quite slow: brute force
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Service provider based techniques (WISE SIGCOMM2008)
Idea: • Based on server logs
Problems:• Multiple data sources• Object dependencies• Client side delays, e.g. JavaScript execution
time
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Regression based techniques (INFOCOM2009)
Idea: • Link Gradients predict E2E RST
Problems:• Independence assumption • Only work for small-scale
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A tool for automated performance prediction
• Fast prediction on the user perceived performance• Timing perturbation based dependency discovery• Dependency driven page load simulation
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Parent( ) =
Ancestor( ) =
Descendant( ) =
Simple HTML parsing/DOM traversal is not enough
• Object requests generated by JavaScript depend on the corresponding .JS files
• Event triggers: e.g. image A need image B trigger “onload” event
Extensive browser instrumentation is heavy-weight and browser dependent
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Goal:• Light‐weight black box based approach• Browser independent
Solution: Timing Perturbation based technique
• Inject delay• See how delay propagate• Dependence Graph
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Regular Objects Regular objects have to be fully loaded
before their descendants
HTML Objects are special HTML is stream objects, allowing
incremental rendering
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Discovering Descendants:• Inject delay• Descendant of X: delayed together with X
Discovering Parent:• Non-stream parent: Idea: Parent of Z is X? No Y between Z and X
Algorithm:
• Stream Parent: Offset 20
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PLT of Basic
Scenario
PLT of New
Scenarios
Predict
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Change Delay Factors: Client, Server, RTT and DNS lookup
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Challenge: Keep the model simple while accuracy
Solution: Right level abstraction Most fundamental characteristics
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Where?
What?
Packet traces (Easy deployable)
Three types of activities:
Additional: infer RTT and # of RT
Response Time = RTT + server delay
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What is Client Delay?Last
Parent is Availabl
e
Browser request
X
Parsing HTMLExecuting JAVAscript
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How to get Client Delay?
+Client DelayClient Delay
=
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Timing of Basic
Scenario
Basic Timing
from TraceClient Delay= +
Adjusted Timing of
New Scenario
Change Delay Factors: (According to New Scenario)Client, Server, RTT and DNS lookup
Other Factors:
Add DNS lookup time based on DNS cache Add TCP handshaking time for new connections Add TCP waiting time when all connections are not
available
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One assumption:
# of round-trips involved in loading an object not change
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Timing of New
Scenario
Browser Behavior
+ +
HTTP/1.1
No pipelining• Not been widely supported by proxies• Head of line blocking
Limit of TCP connections (for host, not IP)
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Browser Behavior
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Metrics:
Controlled experiments• Baseline: high latency• New Scenario: low latency• Use controlled gateway to inject and remove delays
Planetlab experiments• Baseline: International nodes• New scenario: US nodes• Improve all delay factors to be the same as the
US node. 49
Setup: visit Yahoo Maps from Northwestern
Baseline: inject 100ms RTT to one DC
New Scenario: removing the 100ms RTT
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Baseline: International node (relative poor performance)
New Scenario: US node (good performance)
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Web service performance prediction is hard – Modern web services are complicated
– Object dependencies are very important
An automated tool for performance prediction – Dependency discovery by Timing Perturbation
– Dependency (PDG) driven Performance Predication
Evaluation on the accuracy and usefulness
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Cannot deal with dynamic object dependencies.
Packet loss assumption.
Lack of error warning scheme when it fails.
Mean and Variance of PLT.
Unit Load Time = PLT/PLW.
Ways to reduce simulation times?53
