Web Performance

1
Web Performance
2
Web Performance

• Why do we care?
• What is performance?
• User Experience
• Web Server
• Network
• How can we tell how we are doing?
• What good is it?

3
Why do we care?

• Twenty-eight percent of shoppers who have
  suffered failed performance attempts said they
  stopped shopping at the web site where they had
  problems, and six percent said they stopped
  buying at that particular companys off-line
  store. (Boston Consulting Group, quoted in
  Infoworld / Computerworld 3/00)
• It takes only 8 ½ seconds for half of the
  subjects to give up (Peter Bickford, Worth
  the Wait? in Netscape/View Source Magazine
  10/97)
• Perhaps as much as 4.35 billion in e-commerce
  sales in the U.S. may be lost each year due to
  unacceptable download speeds and resulting user
  bailout behaviors. (Zona Research 4/99)
• Fifty-eight percent of online customers surveyed
  indicated quick download time as a key factor in
  determining whether they would return to a web
  site. (Forrester Research 1/99)
• One of the top three reasons cited by online
  shoppers for dissatisfaction with a web site is
  slow site performance. (Jupiter Communications /
  NFO Worldwide 1/99)
• At one site, the abandonment rate fell from 30
  to 6-8 because of a one second improvement in
  load time. (Zona Research 4/99)

4
Effects of Poor Performance

• Lost prospective customer
• If the site didnt work, or took too long, your
  prospect may not return for a long time if
  ever.
• Lost sale
• If your competitors site was up and responsive,
  you may have lost a single sale.
• Lost customer
• If this happens repeatedly, youve lost a
  customer,
• AND the customer may stop going to associated web
  sites and physical locations!
• Lost reputation
• People talk about poor performance word spreads.
• People are looking for a few good sites that they
  can trust!

5
What is performance?

• User Experience
• How fast does the page load?
• How available is the site?
• Web Server
• How many requests/second can be served?
• throughput
• What is the effect of web proxies?
• Network
• What is the network performance?
• Latency, bandwidth

6
Network Performance

• At the network level, performance can be measured
  in terms of
• Latency
• How long it takes a message to travel from one
  end of the network to the other
• Bandwidth
• The number of bits that can be transmitted over
  the network in a certain period of time

latency
bandwidth
7
Network Performance Measures

• Overhead latency of interface vs. Latency
  network

8
Universal Performance Metrics
Sender
(processor busy)
Transmission time (size bandwidth)
Time of Flight
Receiver Overhead
Receiver
(processor busy)
Transport Latency
Total Latency
Total Latency Sender Overhead Time of Flight
Message Size BW
Receiver Overhead
Includes header/trailer in BW calculation?
9
Total Latency Example

• 1000 Mbit/sec., sending overhead of 80 µsec
  receiving overhead of 100 µsec.
• a 10000 byte message (including the header),
  allows 10000 bytes in a single message
• 3 situations distance 1000 km v. 0.5 km v. 0.01
• Speed of light 300,000 km/sec (1/2 in media)
• Latency0.01km
• Latency0.01km
• Latency1000km

10
Total Latency Example

• 1000 Mbit/sec., sending overhead of 80 µsec
  receiving overhead of 100 µsec.
• a 10000 byte message (including the header),
  allows 10000 bytes in a single message
• 3 situations distance 1000 km v. 0.5 km v. 0.01
• Speed of light 300,000 km/sec
• Latency0.01km 80 0.01km / (50 x 300,000)
  10000 x 8 / 1000 100 260 µsec
• Latency0.5km 80 0.5km / (50 x 300,000)
  10000 x 8 / 1000 100 263 µsec
• Latency1000km 80 1000 km / (50 x 300,000)
  10000 x 8 / 1000 100 6931
  µsec
• Long time of flight gt complex WAN protocol

11
So What?

• Long distance long msg transmission time
• Servers should be as close as possible to clients
• Low bandwidth long msg transmission time
• Servers should have high bandwidth links
• High Overhead long msg transmission time
• Reduce the communication overhead as much as
  possible
• Fast TCP implementation
• More memory

12
The Internet
13
The Internet Performance

• Routers
• Read packet headers and send along
• Each hop adds delay
• ISP Peering
• Congestion may occur at peering points
• End-to-end route in one direction my differ from
  route in the other direction

14
The Internet Performance

• Network Connection
• Performance of connection to ISP is generally a
  limiting factor
• ISP Services
• Domain Name Service (DNS)
• Each time a request is made, the server name must
  be translated into an IP address
• Name Caching
• DNS server retains addresses until time to live
  has passed
• Client machine may also cache names for a short
  period of time
• Web Proxies
• Cache most frequently accessed pages
• Zipfs law

15
Web Server Performance

• Throughput Requests per second
• How do you measure?
• Live
• May be too late.
• Offline
• Replay logs - does the past characterize the
  future?
• Synthetic Workload - does it characterize
  reality?
• ...factoring out I/O, the primary determinant to
  server performance is the concurrency strategy.
• -- JAWS Understanding High Performance Web
  Systems

16
Applications of Workload Models

• Identify Performance Problems
• Problems may only occur under high load
• Benchmark Web Components
• Deployment decisions
• Evaluate new features
• Capacity Planning
• Determine network, memory, disk and clustering
  needs

17
Web Workload Characterization

• Based on the results of numerous studies
• Key properties
• HTTP Message Characteristics
• Several request methods and response codes
• Resource Characteristics
• Diverse content-type, size, popularity, and
  modification frequency
• User Behavior
• User browsing habits significantly affect workload

18
Parameter Characterization

• Associate each parameter with quantitative values
• Statistics
• Mean, median, mode
• OK for parameters that dont vary much
• Probability Distributions
• Capture how a parameter varies over a wide range
  of values

19
Probability Distribution

• Every random variable gives rise to a probability
  distribution
• Probability Density Function
• Assigns a probability to every interval of the
  real numbers
• Cumulative Distribution Function
• Describes the probability distribution of a
  real-valued random variable X
• F(x) P(X lt x)
• The probability that a random variable will be
  less than or equal to x
• In the following slides, we will show the CDF of
  commonly used distributions

20
Poisson Distribution

• F(x) (e-??k)/k!
• Used to model the time between independent events
  that happen at a constant average rate

• The number of times a web server is accessed per
  minute is a Poisson distribution
• For instance, the number of edits per hour
  recorded on Wikipedia's Recent Changes page
  follows an approximately Poisson distribution.

21
Exponential Distribution

• F(x) e-?x
• Used to model the time until the next occurrence
  of an event in a Poisson process

• Session interarrival times are exponential
• Time between the start of one user session and
  the start of the next user session

22
Pareto Distribution

• F(x) (x/a)-k
• k is shape, a is minimum value for x
• Power law
• 80-20 rule
• 20 of the sample is responsible for 80 of the
  results

• Response sizes, Resource sizes, Number of
  embedded images, Request interarrival times
• Often used to model self-similar patterns

23
Probability Distributions in Web Workload Models
24
Probability Distribution Conversion

• Most languages have random number library
  functions
• Uniform distribution
• Must convert from uniform distribution to the
  chosen distribution

• Given the cumulative distribution function, CDF,
  of the chosen distribution
• 1. Generate a random number call this number p
• 2. Compute x such that CDF(x) p
• Determine the inverse of the CDF
• 3. x is the random number you use

Inverse of the CDF For the exponential
distribution
25
User Experience

• 8 - second rule
• Probably 4 seconds today
• Typical page
• Multiple requests
• Example
• Page has 20 elements
• Server must be capable of 5 requests/second

26
User Experience
27
Performance Tips

• Check for web standards compliance
• Minimize the use of JavaScript and style sheets
• Turn off reverse DNS lookups on the server
• Get more memory
• Index your database tables
• Make fewer database queries
• Decrease the number of page components
• Decrease the size of each component
• Minimize Perceived Delay
• Give the viewer something to look at while the
  page is loading

28
Website Analysis

• Websites quickly become large and difficult to
  test and optimize
• Use tools
• Workload generators
• Webstone
• JMeter
• Site analysis - log files
• Webalizer

29
JMeter
30
(No Transcript)