identifying performance bottlenecks in cdns through tcp ...mfreed/docs/wand-wmust11-slides.pdf ·...
TRANSCRIPT
Identifying Performance Bottlenecks in CDNs through
TCP-Level Monitoring
Peng Sun Minlan Yu, Michael J. Freedman, Jennifer Rexford
Princeton University
August 19, 2011
Performance Bottlenecks
2
Server
APP
Server OS
CDN Servers
Internet Clients
APP Write too slowly
Server OS Insufficient send buffer or Small initial congestion window
Internet Network congestion
Client Insufficient receive buffer
Reaction to Each Bottleneck
3
Server
APP
Server OS
CDN Servers
Internet Clients
APP is bottleneck: Debug application
Server OS is bottleneck: Tune buffer size, or upgrade server
Internet is bottleneck: Circumvent the congested part of network
Client is bottleneck: Notify client to change
Server
APP
Packet
Sniffer Server OS
Previous Techniques Not Enough
4
Application logs: No details of network activities
Packet sniffing: Expensive to capture
Active probing: Extra load on network
Transport-layer stats: Directly reveal perf. bottlenecks
How TCP Stats Reveal Bottlenecks
CDN Servers Internet Clients
CDN Server Applications
Server Network Stack Network Path Clients
5
Insufficient data in send buffer
Send buffer full or Initial congestion window too small
Packet loss Receive
window too small
Measurement Framework
• Collect TCP statistics • Web100 kernel patch • Extract useful TCP stats for analyzing perf.
• Analysis tool • Bottleneck classifier for individual connections
• Cross-connection correlation at AS level • Map conn. to AS based on RouteView
• Correlate bottlenecks to drive CDN decisions
6
How Bottleneck Classifier Works
7
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 180
50
100
150
200
250
300
350
Time in seconds
KB
RwinCwinBytesInSndBuf
BytesInSndBuf = Rwin
Rwin limits sending
Client is bottleneck
Cwin drops greatly and Packet loss
Network path is bottleneck
Small initial Cwin
Slow start limits perf.
Network Stack is bottleneck
CoralCDN Experiment
• CoralCDN serves 1 million clients per day
• Experiment Environment • Deployment: A Clemson PlanetLab node • Polling interval: 50 ms • Traces to Show: Feb 19th – 25th 2011 • Total # of Conn.: 209K • After removing
Cache-Miss Conn.: 137K (Total 2008 ASes)
• Log Space overhead • < 200MB per Coral server per day
8
What are Major Bottleneck for Individual Clients?
• We calculate the fraction of time that the connection is under each bottleneck in lifetime
9
Bottlenecks % of Conn. With Bottleneck for
>40% of Lifetime
Server Application 10.75%
Server Network Stack 18.72%
Network Path 3.94%
Clients 1.27%
Reasons: Slow CPU or scarce disk resources of the PlanetLab node
Reasons: Congestion window rises too slowly for short conn. (>80% of the connections last <1 second)
Reasons: Spotty network (discussed in next slide) Reasons: Receive buffer too small (Most of them are <30KB) Our suggestion: Use more powerful PlanetLab machines Our suggestion: Use larger initial congestion window Our suggestion: Filter them out of decision making
AS-Level Correlation
• CDNs make decision at the AS level • e.g., change server selection for 1.1.1.0/24
• Explore at the AS level: • Filter out non-network bottlenecks • Whether network problems exist
• Whether the problem is consistent
10
Filtering Out Non-Network Bottlenecks
• CDNs change server selection if clients have low throughput
• Non-network factors can limit throughput
• 236 out of 505 low-throughput ASes limited by non-network bottlenecks
• Filtering is helpful: • Don’t worry about things CDNs cannot control • Produce more accurate estimates of perf.
11
Network Problem at AS Level
• CDN make decision at AS level
• Whether conn. in the same AS have common network problem
• For 7.1% of the ASes, half of conn. have >10% packet loss rate
• Network problems are significant at the AS level
12
Consistent Packet Loss of AS
• CDNs care about predictive value of measurement
• Analyze the variance of average packet loss rates • Each epoch (1 min) has nonzero average loss rate • Loss rate is consistent across epochs
(standard deviation < mean)
13
Analysis Length # of ASes with
Consistent Packet Loss
One Week 377 / 2008
One Day (Feb 21st) 122 / 739
One Hour (Feb 21st 18:00~19:00)
19 / 121
Conclusion & Future Work • Use TCP-level stats to detect performance
bottlenecks
• Identify major bottlenecks for a production CDN
• Discuss how to improve CDN’s operation with our tool
• Future Works • Automatic and real-time analysis combined into
CDN operation • Detect the problematic AS on the path • Combine TCP-level stats with application logs to
debug online services 14
Thanks!
Questions?
15