Network Performance
Measurement and Analysis
Outline
Quiz #1 solutions
Measurement
Tools and Techniques
Workload generation
Analysis
Basic statistics
Queuing models
Simulation
Homework #2 posted by end of the day
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Quiz #1 Solutions
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3.
Show how RSA can be used for two way authentication.
Briefly explain (3 or 4 sentences max) the pros and cons
of persistent connections in HTTP/1.1
Solution: Pro: reduces network traffic, Con: can
increase server load
Applications
1. What is a basic difference between SMTP and other
application level protocols?
Solution: SMTP is not an interactive protocol.
1. What was the motivation for Nagle’s algorithm (hint
think about the telnet application)?
Solution: telnet generates “tinygrams” – lots of very
small packets. Nagle sends groups of data based on
ACK process.
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Measurement and Analysis Overview
• Size, complexity and diversity of the Internet makes it very
difficult to understand cause-effect relationships
• Measurement is necessary for understanding current system
behavior and how new systems will behave
– How, when, where, what do we measure?
• Measurement is meaningless without careful analysis
– Analysis of data gathered from networks is quite different from work done in
other disciplines
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Measurement/analysis enables models to be built which can be
used to effectively develop and evaluate new techniques
– Statistical models
– Queuing models
– Simulation models
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Determining What to Measure
• Before any measurements can take place one must determine what
to measure
• There are many commonly used network performance
characteristics
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Latency
Throughput
Response time
Arrival rate
Utilization
Bandwidth
Loss
Routing
Reliability
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Measurement Introduction
• Internet measurement is done to either analyze/characterize
network phenomena or to test new tools, protocols, systems, etc.
• Measuring Internet performance is easier said than done
– What does “performance” mean?
– Workload (what and where you’re measuring) selection is critical
• Reproducibility is often essential
• Many tools have been developed to measure/monitor general
characteristics of network performance
– traceroute and ping are two of the most popular
• These are examples of active measurement tools
– Passive tools are the other major category
• Representative and reproducible workload generation will be a
focus
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Active Measurement Tools
• Send probe packet(s) into the network and measure a response
– Ping: RTT and loss
• Zing: one way Poisson probes
– Traceroute: path and RTT
– Nettimer (Lai): latest bottleneck bandwidth using packet pair method
Tn+1 - Tn = max(S/BW, T1 – T0)
Size/BW
T1 T0
Tn+1 Tn
– Pathchar: per-hop bandwidth, latency, loss measurement
• Pchar, clink: open-source reimplementation of pathchar
• Problem: measurement timescales vary widely
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Passive Measurement Tools
• Passive tools: Capture data as it passes by
– Logging at application level
– Packet capture applications (tcpdump) uses packet capture filter
(bpf,libpcap)
• Requires access to the wire
• Can have many problems (adds, deletes, reordering)
– Flow-based measurement tools
– SNMP tools
– Routing looking glass sites
• Problems
– LOTS of data!
– Privacy issues
– Getting packet scoped in backbone of the network
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Workload Generation
• Local and/or wide area experiments often require representative
and reproducible workloads
• How do we select a workload?
– Currently HTTP makes up the majority of Internet traffic
• Trace-based workloads
– Capture traces and replay them
– Black-box method
• Synthetic workloads
– Abstraction of actual operation
– May not capture all aspects of workload
• Analytic workloads
– Attempt to model workload precisely
– Very difficult
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SURGE Web Workload Generator
• Scalable URl Generator
– Analytic workload generator
– Based on 12 empirically derived distributions of Web browsing
behaviror
– Explicit, parameterized models
– Captures “heavy-tailed” (highly variable) properties of Web
workloads
– Widely used
• SURGE components:
– Statistical distribution generator
– Hyper Text Transfer Protocol (HTTP) request generator
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Workload characteristics captured in SURGE
BF EF1 EF2
Off time
Characteristic Component
File Size
Base file - body
Base file - tail
Embedded file
Single file1
Single file 2
Request Size
Body
Tail
Document Popularity
Temporal Locality
OFF Times
Embedded References
Session Lengths
Fall 2000
SF
Off time
Model
BF
EF1
System Impact
Lognormal
File System
Pareto
Lognormal
Lognormal
Lognormal
Lognormal
Network
Pareto
Zipf
Caches, buffers
Lognormal
Caches, buffers
Pareto
Pareto
ON Times
Inverse Gaussian Connection times
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SURGE Architecture
SURGE Client System
ON/OFF Thread
ON/OFF Thread
ON/OFF Thread
SURGE Client System
LAN
Web Server System
SURGE Client System
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SURGE and SPECWeb96 exercise servers
very differently
Percent CPU Utilization
40
35
30
Surge
25
20
SPECWeb96
15
SPECWeb96
SURGE
10
5
0
-5 0
200
400
600
Packets per Second
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Analyzing Measured Data
• Analyzing measured data in networks is typically done
using statistical methods
– Selecting appropriate analysis method(s) is critical
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Averaging
Dispersion (variability)
Correlations
Regression analysis
Distributional analysis
Frequency analysis
Principal-component analysis
Cluster analysis
• Each form of analysis has strengths and weaknesses
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Self-Similar Nature of Network Traffric
• W. Leland, M. Taqqu, W. Willinger, D. Wilson, On the
Self-Similar Nature of Ethernet Traffic, IEEE/ACM TON,
1994.
– Baker Award winner
• V. Paxson, S. Floyd, Wide-Area Traffic: The Failure of
Poisson Modeling, IEEE/ACM TON, 1995.
• M. Crovella, A. Bestavros, Self-Similarity in World Wide
Web Traffic: Evidence and Possible Causes, IEEE/ACM
TON, 1997.
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Queuing Models
• One of the key modeling techniques for computer
systems in general
– Vast literature on queuing theory
– Nicely suited for network analysis
– Prof. Mary Vernon is our local expert
• Generally, queuing systems deal with a situation where
jobs (of which there are many) wait in line for a resource
(of which there are few)
– Queuing theory can enable us to determine response time
– Examples?
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Queuing Models contd.
• Example: packets arriving at a router – how can we determine how
long it takes for packets to be forwarded by the router?
• Characteristics necessary to specify a queuing system
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Arrival process
Service time distribution
Number of servers
System capacity (number of buffers)
Population size
Service discipline
Kendal notation: A/S/m/B/K/SD
• Response time = waiting time + service time
• For stability, mean arrival rate must be less than mean service rate
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Little’s Law
• One of the most basic theorems in queuing theory (1961)
• Mean number jobs in system = arrival rate * mean response time
– Treats a system as a black box
– Applies whenever number of jobs entering the system equals
number of jobs leaving the system
• No jobs created or lost inside system
– Can be extended to include systems with finite buffers
• Example: Average forwarding time in a router is 100
microseconds, I/O rate for packets is 100k. What is the
mean number of packets buffered in the router?
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Simulation Models
• Simulation is one of the most common/important
methods of analysis/modeling
– Typically an abstraction of the system under consideration
– Can provide significant insight to system’s behavior
• Network simulation is difficult because of the different
layers of operation and the complexity at each layer
• Simulation options: build your own, use someone else’s
• Canonical network simulator is ns developed at LBL
– www.isi.edu/nsnam/ns
– ssf-net is a new, routing-enabled simulator
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