Data Dissemination on the Web

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Data Dissemination on the Web
Krithi Ramamritham IIT Bombay krithi_at_cse.iitb.ern
et.in
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Web Content

• Web sites have traditionally served static
  content
• But, dynamic content generation has come into
  vogue
• generated on the fly by running dynamic scripts,
  e.g., Active Server Pages (ASP), Java Server
  Pages (JSP), Servlets
• allows generation of different content for the
  same request

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Dynamic Web Pages
Web Page
A News content site
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Generic Architecture
wired hosts
sensors
Network
Network
mobile hosts
servers
Data sources
End-hosts
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Coherency of Dynamic Data

• Strong coherency
• The client and source always in sync with each
  other
• Strong coherency is expensive!
• Relax strong coherency ? - coherency
• Time domain ?t - coherency
• The client is never out of sync with the source
  by more than ?t time units
• eg Traffic data not stale by more than a minute
• Value domain ?v - coherency
• The difference in the data values at the client
  and the source bounded by ?v at all times
• eg Only interested in temperature changes larger
  than 1 degree

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Generic Architecture
wired host
sensors
Network
Network
servers
Proxies /caches
mobile host
Data sources
End-hosts
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The Push Approach

• Proxy registers the data item of interest and the
  coherency requirement with the server
• Server pushes interesting changes
• Achieves Strong Consistency
• Keeps network overhead minimum
• -- Poor Scalability (has to maintain state and
  has to keep connections open)
• -- Low Resiliency

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The Pull Approach

• Proxy Pulls after
• Time to Live (TTL)
• Time To next Refresh (TTR / TNR)
• Can be implemented using the HTTP protocol
• Stateless and hence is generally scalable with
  respect to state space and computation
• Weak cache consistency
• Heavy polling for stringent coherence requirement
  or highly dynamic data
• Network overheads higher than for Push

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Typical End-to-end Web Site Architecture
Application Server Cluster
Web Server Cluster
Data
. . . .
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WS vs. AS

• Web servers
• Do well defined and quantifiable local work
• e.g., processing HTTP headers, serving static
  content
• Application servers
• Run multi-layer programs
• e.g., scripts involving calls to backends

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Application Layer Details
Servlets
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The Problem Page Generation Delays

• Causes of page generation delays include
• (in addition to pure processing overhead)
• Remote database accesses Heavy I/O loads,
  Network delays
• XML-HTML transformations Extensive processing
  delays
• Personalization logic e.g., Broadvision,
  Vignette, etc.
• Interaction bottlenecks e.g., database
  connection pools
• gt serious performance and scalability
  problems
• for web sites
• due to increased load
• on server-side infrastructure

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Reducing delays

• Approaches fall into 3 broad categories
• Database caching
• Page level caching
• Fragment level caching

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Alternative CDNs
Content Distribution Networks
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Push Based Core Infrastructure

• Resilient and efficient
• content distribution network (CDN)
• for dynamic data.
• Existing CDNs
• Akamai, Dynamai

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Database Caching

• Two broad types
• Query result caching
• Middle tier database caching
• caching database tables in main memory

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Query result caching

• Many application server products offer this
  feature
• Luo et. al., 2000 proposed query result caching
  at Web proxy caches
• -- mitigates only local database access latency
• -- only a subset of query results may be reused
  in page generation
• -- page fragments may not all be from databases

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Middle tier database caching

• Caching database tables in main memory
• Oracle 9i Cache
• Main-memory databases, e.g., TimesTen
• -- mitigates only database access latency
• -- caching at table granularity results in poor
  cache utilization
• -- main-memory databases are difficult to
  integrate and maintain and can be expensive

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Page Level Caching

• Dynamically generated HTML pages are cached
  Iyengar Challenger, 1997 Zhu Yang, 2000
• Several commercially available products follow
  this approach, e.g., SpiderCache, Xcache, Dynamai
• Can completely offload work from web/app
  server
• Low reusability for highly personalized web pages
• URL may not uniquely identify a page
• -- increasing the risk of delivering
  incorrect pages
• Often introduces excessive invalidations
• -- e.g., even if a single element on the
  page changes

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Reducing page generation delays

• Approaches fall into 3 broad categories
• Database caching
• Page level caching
• Fragment level caching

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How Dynamic Scripting Works
Page generation script
Write to Out
Write to Out
. . .
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Code Blocks Perform Work
Page generation script
Write to Out
Write to Out
. . .
. . .
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Code Blocks lt-gt Components
Page generation script
Web Page
Ad Component
Write to Out
Headline Component
Headline Component
Navigation Component
Headline Component
Headline Component
Write to Out
. . .
Personalized Component
(Example News content site)
Certain components can be cached
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DCA Our Solution
Page generation script
Code block
Request
Dynamic Content Accelerator
Code Block Output
Application logic
Code block
Work bypassed
Database calls
HTML formatting
. . .
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DCA in a Typical End-to-end Web Site Architecture

• A single instance of the DCA serves a rack of
  application servers
• Application servers communicate with DCA through
  a lightweight API

Application Server Cluster
Web Server Cluster
Data
Dynamic Content Accelerator
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Cache Management

• A critical aspect of any caching solution
• DCA supports novel cache management strategies
• Prediction-based cache replacement
• Observation-based cache invalidation

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Cache Replacement

• Prediction-based replacement
• fragments having lowest probability of access
  replaced
• Least-Likely-to-be-Used (LLU)
• Access probabilities based on
• Current user navigational patterns over site
  graph
• (in the form of clickstreams)
• Historical user navigational patterns over site
  graph
• (in the form of association rules)

(News, Sports, Hockey) ? Schedules 20
(News, Sports, Hockey) ? Players 15
LLU
(News, Sports, Hockey) ? Teams 10
(News, Sports, Hockey) ? Scores 55
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Cache Invalidation

• DCA supports common cache invalidation
  techniques
• Time-based Each cache element assigned a TTL
• Event-based Updates to the database send an
  invalidation message to the cache
• On demand Manual invalidation of selected
  elements
• DCA supports additional invalidation techniques.

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Cache Invalidation

• Other invalidation techniques supported
• Observation-based
• User-initiated updates are observed in scripts
  each such update sends an invalidation message to
  the cache
• Most appropriate for auction sites, online
  trading sites
• Invalidation does not require communication with
  the databases
• Keyword-based
• Elements can be associated with keywords e.g.,
  a retailer may wish to invalidate all
  seasonal items
• Regular expression-based
• Elements can be invalidated based on regular
  expression matching

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Other Fragment Level Caching
app servers (e.g., BEAs WebLogic, IBMs
WebSphere) cache fragments produced by JSP
scripts
Application Server Cluster

• can offload presentation layer tasks
• runs in the application server process space
• gt competes for server resources
• application server cluster
• gt multiple cache instances,
• duplication of content,
• additional synchronization overhead

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Other Fragment Level Caching.

• Weave system VLDB 2000 caches XML fragments, as
  well as query results and HTML pages
• Requires use of declarative web site
  specification language

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Performance Study

• Metric
• Average page generation time
• time required to construct HTML page

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Performance Study

• Test Site
• Fictitious online retail site, allows browsing of
  product catalog
• Pages generated using JSP scripts
• Site content stored in Oracle database
• Database schema based on Dublin Core Metadata
  Open Standard
• Contains 200,000 products and 44,000 categories
• Each page consists of 3 components, each
  involving a database call

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Performance Study

• Test Setup
• Content Database Server
• Oracle 8.1.6
• Web/Application Server
• WebLogic 6.0 running on cluster of 2 machines
• Server machines
• have 1 GB RAM, dual P III-933 Mhz processors
• run Windows 2K Advanced Server

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Testing Methodology

• DCA compared to 2 middle tier caching solutions
• Main Memory Database TimesTen used to cache the
  content database (entire database is cached, runs
  on database server machine)
• Application Server Cache WebLogic Server JSP
  caching (WLS Cache)
• For both WLS and DCA, 2 (of 3) page components
  are cached
• Usually, DCA runs on a separate machine (512 MB
  RAM, P III-600Mhz processor, running Windows 2K
  Advanced Server)

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Testing Methodology...

• Baseline Parameters
• Cache Size, i.e., percentage of fragments that
  fit into cache 75
• Cache replacement policy LLU for DCA
• User load is varied by sending requests from
  client machines running Radviews WebLoad
• Simulated users navigate site according to Zipf
  80-20 distribution (i.e., 80 of users follow 20
  of navigation links)

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Page Gen. Times vs. Number of Users
TimesTen vs. DCA -- 3x to 9x
improvement TimesTen only mitigates local
database access latency -- still requires
query processing, formatting operations
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Page Generation Times...
WLS vs. DCA -- 2x to 5x improvement WLS runs
in application server process space, competes
for server resources WLS utilizes
multiple caches, causing redundant caching DCA
runs as single, standalone logical cache
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Sensitivity to Cache Size
As expected, performance improves as cache size
increases Since cached elements are typically
quite small (e.g., a few hundred bytes), larger
cache sizes are feasible in practice
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Conclusion

• Increased use of dynamic page generation
  technologies
• gt increases load on application servers
• gt serious performance and scalability
  problems
• for e-business sites
• DCA (Dynamic Content Acceleration)
• gt significantly reduces the load on the
  server side infrastructure, allows e-business
  sites to scale
• gt significantly outperforms existing middle
  tier caching solutions

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IIT Bombays aAQUA Community Forum
Farmers get information and get their questions
answered -- In the local context -- In their
local language
Capitalizes on existing human and infrastructural
resources Agri-extension center KVK,
Baramati NGO Vigyan Ashram, Pabal Corporate
infrastructure -- ITC e-chaupal Government
MCIT
www.aAQUA.org
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Access over low bandwidthResource Optimization
Resource constraints Low/unpredictable bandwidth
gt disconnected operation/access Exploit cachi
ng prefetching (through prediction of future
needs) Profiling by user type, location Data
characteristics Static data text, images land
records, photos can be cached/hoarded Dynamic
data weather/price information cached info need
to be refreshed carefully Continuous media
VoIP, video data QoS considerations