1. 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
Fall 2000
CS 640

2. Quiz #1 Solutions 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
What is a basic difference between SMTP and other application level protocols?
Solution: SMTP is not an interactive protocol.
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.
Fall 2000
CS 640

3. 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
Measurement/analysis enables models to be built which can be used to effectively develop and evaluate new techniques
Statistical models
Queuing models
Simulation models
Fall 2000
CS 640

4. Determining What to Measure
Before any measurements can take place one must determine what to measure
There are many commonly used network performance characteristics
Latency
Throughput
Response time
Arrival rate
Utilization
Bandwidth
Loss
Routing
Reliability
Fall 2000
CS 640

5. 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
Fall 2000
CS 640

6. 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
Pathchar: per-hop bandwidth, latency, loss measurement
Pchar, clink: open-source reimplementation of pathchar
Problem: measurement timescales vary widely
Tn+1 - Tn = max(S/BW, T1 – T0)
Size/BW
T1 T0
Tn+1 Tn
Fall 2000
CS 640

7. 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
Fall 2000
CS 640

8. 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
Fall 2000
CS 640

9. 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
Fall 2000
CS 640

10. Workload characteristics captured in SURGEBF
EF1
EF2
Off time SF
Off time BF
EF1
Characteristic Component Model System Impact
File Size Base file - body Lognormal File System *
Base file - tail Pareto *
Embedded file Lognormal *
Single file1 Lognormal *
Single file 2 Lognormal *
Request Size Body Lognormal Network *
Tail Pareto *
Document Popularity Zipf Caches, buffers
Temporal Locality Lognormal Caches, buffers
OFF Times Pareto *
Embedded References Pareto ON Times *
Session Lengths Inverse Gaussian Connection times
Fall 2000
CS 640

11. SURGE Architecture Fall 2000 CS 640 SURGE Client System ON/OFF Thread
LAN
Web Server System
Results of this section show that through rate controlled prefetching, network characteristics can be enhanced. These could be added to browsers.
Idea behing rate control is that we don’t have to transfer at maximum rate - deliver JIT. Web will always have OFF time while people read. Rate controlled method presented assumes you can predict OFF times but results show that accuracy is not critical. Draw picture of how OFF times are “used up” in this approach.
We assume one-ahead prefetching and analyze various hit rates.
Window based approach - vary TCP at client per Window size = number pkts * RTT/OFF time. W=P*R/T. SHOW GRAPH RESULTS
Rate Controlled always better than non controlled prefetch and usually better than no prefetching EVEN though extra traffic is added.
SURGE Client System
Fall 2000
CS 640

12. SURGE and SPECWeb96 exercise servers very differently
Fall 2000
CS 640

13. Analyzing Measured Data
Analyzing measured data in networks is typically done using statistical methods
Selecting appropriate analysis method(s) is critical
Averaging
Dispersion (variability)
Correlations
Regression analysis
Distributional analysis
Frequency analysis
Principal-component analysis
Cluster analysis
Each form of analysis has strengths and weaknesses
Fall 2000
CS 640

14. 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.
Fall 2000
CS 640

15. 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?
Fall 2000
CS 640

16. 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
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
Fall 2000
CS 640

17. 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?
Fall 2000
CS 640

18. 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
ssf-net is a new, routing-enabled simulator
Fall 2000
CS 640