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perform.ppt

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									             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      1
                                                                  A                       B

                                                                      E( x ,
                                                                               Pub
                        Quiz #1 Solutions                                         lic )
                                                                                     B

1.     Show how RSA can be used for two way authentication.
                                                                               x
2.     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
3.     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.

     Fall 2000                         CS 640                                       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

     Fall 2000                        CS 640                                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
   Fall 2000                  CS 640                        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 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                       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


                                                             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

    Fall 2000                             CS 640                                       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


    Fall 2000                            CS 640                         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
    Fall 2000                         CS 640                    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

    Fall 2000                  CS 640                         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                              *
                   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                               10
                    SURGE Architecture

                        SURGE Client System



    ON/OFF Thread
      ON/OFF Thread
       ON/OFF Thread    SURGE Client System   LAN   Web Server System




                        SURGE Client System




Fall 2000                          CS 640                      11
SURGE and SPECWeb96 exercise servers
          very differently
                          40
                          35
Percent CPU Utilization




                          30
                          25                          Surge

                          20                                       SPECWeb96
                          15                           SPECWeb96   SURGE
                          10
                          5
                          0
                          -5 0    200          400        600
                                 Packets per Second
       Fall 2000                             CS 640                     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

   Fall 2000                           CS 640               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.


   Fall 2000               CS 640                     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?

   Fall 2000                   CS 640                          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

    Fall 2000                         CS 640                      16
                              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                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 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


   Fall 2000                    CS 640                            18

								
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