short presentation on caching Caching.ppt

1
Analysis of Caching and
Replication Strategies for Web
Applications
Presented By
Sudarsan Maddi
Graduate Student
Authors:
Swaminathan Sivasubramaniam,
Guillaume Pierre,
Maarten van Steen.

Analysis of Caching and Replication
Strategies for Web Applications 2
Topics That We Will be Seeing…
 Introduction
 Techniques to scale Web applications
 Performance Analysis
 Choosing the Right Strategy

Introduction
 In this paper the authors present qualitative
and quantitative analysis of replication and
caching techniques to host Web applications.
 Their analysis shows that selecting the best
mechanism depends heavily on data
workload and application characteristics.

Introduction
 Web sites are slow dew to many reasons,
one of the main reason is dynamic generation
of web documents.
 Web page caching: Fragments of HTML
pages the application generates are cached
to serve future requests.
 Content-delivery networks such as Akamai do
this by deploying edge servers around the
Internet thus reducing request’s network
latency.

Introduction
 Limitations of page caching have given raise
to different approaches for scalable Web
applications, classified broadly into:
 Application code replication
 Cache database records
 Cache query results
 Entire Database replication
 In this article they have given overview of
various scalable techniques compared and
analyzed their features and performance.

Techniques to scale Web Applications
 The techniques we are going to see are
 Edge Computing
 Data Replication
 Content-Aware data Caching (CAC)
 Content-Blind data Caching (CBC)

Techniques to Scale Web Applications 7
Edge Computing
 In this the application code is replicated at
multiple edge servers and data is centralized.
 Akamai and ACDN use this technique.
 The data centralization create problems,
 If the edge servers are located worldwide, each data
access incurs WAN latency.
 The central database becomes a performance
bottleneck if the load increases.

Data Replication
 Solution for Edge computing is to place the
data at each edge server.
 Database replication (REPL) techniques can
help maintaining identical copies at multiple
locations.
Continued…

Data Replication
 The problem with this is when there is a
database update.
 This creates huge network traffic and
performance overhead.

Content-Aware data Caching (CAC)
 Instead of maintaining full copies of database CAC
systems cache database query results as the
application code issues them.
 Query Containment Check: The application running
at the edge-server issues a query, the local
database checks if it has enough data to answer the
query locally.
 Containment check results positive query is present
locally, else its sent to central database and inserts
the result in its local database.
Continued…

An Example of CAC
 CAC store query results efficiently
 For example:
Query Q1: Select* from items where price<50
Query Q2: Select* from items where price<20
 Query template QT1:
“Select* from items where price<”

Content-Aware data Caching (CAC)
 This query containment check is highly
computationally expensive because it must
check the new query with all previously
cached queries.
 In order to reduce this cost CAC makes use
of query template, which is a parameterized
SQL query whose parameter values are
parse at runtime
 In, CAC systems update queries is always
executed at the central database.

Content-Blind data Caching (CBC)
 Here, edge servers don’t need to run a
database at all.
 Instead they store the results of remote
database queries independently.
 The query results aren't merged here storing
redundant information, and will have a hit
only if application issues exact query, so hit
rates are low
Continued….

Content-Blind data Caching (CBC)
 This have some advantages over CAC as,
 Incurs very little computational load.
 Caching query results as result sets instead of database
records, so can return results immediately.
 Finally, inserting a new element into the cache doesn't
require a query rewrite.

15
Scalable Web hosting.
(a) edge computing, (b) content-aware caching,
(c) content-blind caching, and (d) data replication.

Performance Analysis
 To compare the four techniques, they have
made use of two different applications,
 RUBBoS, a bulletin-board benchmark application that
models Slashdot.org,
http://jmob.objectweb.org/rubbos.html
 TPC-W, an industry-standard e-commerce benchmark
that models an online book store such as Amazon.com,
http://pgfoundry.org/projects/tpc-w-php/

Performance Analysis
 They have measured the end-to-end client
latency, which is the sum of network latency
and internal latency.
 The results shows that CBC performed best
in terms of client latency whereas EC
performed the worst for RUBBoS.
 Were as for TPC-W REPL performed the best
and EC worst again.

Performance Results
(a) RUBBoS benchmark
(b) TPC-W Browsing
(c) TPC-W Ordering

Analysis of Caching and Raeplication
Choosing the Right Strategy
 According to the author the Web designers should
choose the scalable technique by carefully
analyzing their Web application characteristics.
 They have suggested the best strategy is the one
that minimizes the applications end-to-end client
latency.
 This latency is affected by many parameters as hit
ratio, database query execution time, application
server execution time.
 To do this they have proposed a concept called
virtual caches (VC).
Continued…

Analysis of Caching and Raeplication
Choosing the Right Strategy
 VC behaves just like a real cache but it stores
only meta data, such as the list of objects in
the cache, sizes. So this requires less
memory compared to real caches.
 So with the help of these VC we can get the
hit ratios and execution times for servers and
can estimate end-to-end latency.

Thank You.

short presentation on caching Caching.ppt

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