Network Performance Measurement and Analysis

1
Network Performance Measurement and Analysis

• Outline
• Measurement
• Tools and Techniques
• Workload generation
• Analysis
• Basic statistics
• Queuing models
• Simulation

2
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

3
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

4
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 youre 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

5
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

Tn1 - Tn max(S/BW, T1 T0)
Size/BW
T1 T0
Tn1 Tn
6
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

7
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

8
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

9
Workload characteristics captured in SURGE
BF
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 E
mbedded 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
10
SURGE Architecture
SURGE Client System
ON/OFF Thread
ON/OFF Thread
SURGE Client System
LAN
ON/OFF Thread
Web Server System
SURGE Client System
11
SURGE and SPECWeb96 exercise servers very
differently
Surge
SPECWeb96
12
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

13
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.

14
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?

15
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

16
Littles 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?

17
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 systems
  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
  elses
• Canonical network simulator is ns developed at
  LBL
• www.isi.edu/nsnam/ns
• ssf-net is a new, routing-enabled simulator