Web Cache
Characterizing Roles of Front-end Servers in End-to-End
Performance of Dynamic Content Distribution
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46842197
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Li ZHANG
Dakuo WANG
Xuejie SUN
Yang LIU
1. Introduction of Web Cache
1. Related Paper Overview
2.1 Summary Cache: A Scalable Wide-Area Web Cache Sharing
Protocol
2.2 Going Viral: Flash Crowds in an Open CDN
3. Characterizing Roles of Front-end Servers in End-toEnd Performance of Dynamic Content Distribution
3.1 Problem Definition
3.2 Motivation
3.3 Model
3.4 Result
3.5 Conclusion
3.6 Pro & Con
4. Q & A
Introduction to Web Caching(Proxy Server)
CONCEPT
Web cache is a mechanism for the temporary caching of web documents
to reduce bandwidth usage, server load, and perceived lag.
TYPES OF PROXY SERVER
Forward proxies, Open proxies, Reverse proxies, Performance Enhancing
Proxies
USES OF PROXY SERVER
To speed up access to resources .
To control access to internal resources.
To filter content.
To hide the real IP.
To circumvent Internet filtering to access content otherwise blocked by
governments.
To breakthrough own IP access restrictions.
Summary Cache: A Scalable WideArea Web Cache Sharing Protocol
Li Fan, Member, IEEE, Pei Cao, Jussara Almeida, and Andrei Z. Broder
Summary Cache: A Scalable WideArea Web Cache Sharing Protocol
Li Fan, Member, IEEE, Pei Cao, Jussara Almeida, and Andrei Z. Broder
Internet Cache Protocol (ICP)
Simple Cache Sharing: fetch and store locally
No load balancing
Overhead: UDP messages (factor of 73 to 90), network traffic
(8% - 13%), client HTTP request latency (8%-12%)
Summary Cache
Each proxy store a summary of its directory of cached
document in every other proxy
Cache miss, check the summaries to see if it exist in other
proxies
Summary Cache Enhanced ICP (SC-ICP)
Add new opcode in ICP version 2
Introduce additional header follows regular ICP header
Modify Squid 1.1.4 software to implement the protocol
Going Viral: Flash Crowds in an Open
CDN
Patrick Wendell, Michael J. Freedman
Flash Crowds on CoralCDN
CoralCDN: an open Content Distribution Network (CDN) running
at several hundred POPs
Flash Crowds: a period over which request rates for a particular
fully-qualified domain name are increasing exponentially
average per minute request rate over a particular period ti
4 years CDN traffic, 33 billion HTTP requests
Analysis conclusion:
Potential benefits of cooperative vs. independent caching by
CDN node
The efficacy of elastic redirection and resource provisioning
The ecosystem of portals, aggregators and social networks
Going Viral: Flash Crowds in an Open
CDN
Patrick Wendell, Michael J. Freedman
Flash Crowd Cacheability
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The degree of caches coordination in fetching origin content
CoralCDN uses a distributed hash table for global content
discovery
Commercial CDNs, Akamai, use non-cooperative caching, where
each remote proxy independently fetches content from the origin
site
Fewer requests to origin site, higher complexity and additional
overhead
Going Viral: Flash Crowds in an Open
CDN
Patrick Wendell, Michael J. Freedman
Flash Crowd Cacheability
The Motivations
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Most content on the Internet is stored at data centers in the
cloud, and they are dynamic for user’s request.
The scale and cost of building and operating large-scale
powerful data centers are increasing.
The way to improve the overall response time is to deploy
“proxy” servers closer to users.
FE servers can be exploited to improve the user-perceived
performance due to：1) A portion of the dynamic content may
be static; thus can be cached and delivered immediately from
the FE servers. 2) Via split TCP connections, a FE server can
establish a persistent TCP connection with the data center
which not only eliminates the effect of TCP slow-start between
the FE and BE, but also reduce the RTT between the user and
the server.
The Problem
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Authors conduct an active measurement-based comparative
study of Google and Microsoft Bing web search services.
Use the PlanetLab nodes to perform extensive measurements
of Google and Bing search services using a variety of keyword
search, and collect dynamically generated content and
application-layer measurement data.
Use these collected data to analysis the role of FE.
How to solve it
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They develop an in-house user search query emulator,
which performs exactly the same functionality as the webbased search box.
They conduct extensive measurements by submitting the
same search queries to both Bing and Google search
engines, and collect detailed TCPdump with full
application-layer payloads.
Perform two sets of experiments:
1) In the first set, search queries are launched from all
measurement nodes to their default 3 FE servers every 10
seconds.
2) In the second set, they fix one FE server (of Bing or
Google respectively) at a time, and launch queries from all
measurement nodes to this server.
Content distribution
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Content includes static and dynamic
(i.e., search results)
Static portion: HTTP header, HTML
header, CSS style files and the static
menu bar.
Dynamic portion: keyworddependent menu bar, search results
and ads.
Static portion is cached and directly
delivered by FE servers. Dynamic
portion is generated by BE data
centers and them passed onto the FE
servers for delivery.
The experiment shows Tdynamic
varies significantly with the types of
search keywords used, whereas
Tstatic is mostly insensitive.
Several parameters:
Tb: start of TCP three-way handshake
T1: HTTP GET request
T2: receive packets from server
T3/T4: receive first/last packet containing the static portion
T5/T6: receive first/last packet containing the dynamic portion
Observation:
• Tstatic depends mostly on the time to generate and deliver the static content
portion at the FE server.
• When RTT is small, Tdynamic is roughly a constant while Tdelta decreases
as a function of RTT.
• When RTT increases beyond a certain threshold, the dynamic content portion
will be received by the FE server before the static content portion is
entirely delivered to the client. Hence Tdynamic increases as
a function of RTT, while Tdelta becomes zero.
Performance
• First cluster represents the three-way TCP handshake between the
client and the FE server. The second and third cluster represent
the delivery of static and dynamic contents.
• As the RTT increases, the gap between the end of the second and the beginning of
the third clusters decreases, and eventually the two are
lumped together.
 Google has slightly farther FE
servers from the clients, but has
significantly lower Tstatic and
Tdynamic.
 These results illustrate that placing
FE servers closer to clients does
not necessarily reduce Tstatic and
Tdynamic.
 The x-axis represents the
PlanetLab nodes, and the yaxis
represents the box-plot for the
distribution for different
samples.
 The results show that
comparing Google, users using
the Bing search service tend to
experience slightly longer and
more variable overall response
times.
Comparing Bing & Google
Performance and discuss
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The fetch time between Google FE servers and BE data centers
tends to be smaller and more stable. In contrast, fetch time between
Akamai FE servers and Bing data centers tends to be larger and
shows higher variability.
Although Bing place FE servers closer to client, it has significantly
higher Tstatic and Tdynamic compare to Google. The reason for this
may be due to the higher and more variable loads at Akamai FE
server, as Bing shared with other servicers.
The end to end performance is determined solely by the FE-BE
fetch time. Tfetch consists of two key components: Tproc and RTTbe
Several Results of This Paper
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FE severs do not cache any dynamically generated search result. It
only cache the static information, such as Http header, Html header.
Placing FE closer to users can improve user-perceived performance.
There is a trade-off between placement of FE severs and the FE-BE
fetch time.
There is a threshold within which placing FE further closer to users
is no longer helpful.
While placing FE severs closer to users can help reduce latency,
other key factors, such as processing times, loads at FE/BE data
centers, and the quality of connections between them also play a
critical role in determining the overall user-perceived performance.
Improving and optimizing these factors are important for overall
user-perceived performance in dynamic content distribution such as
dynamic generation of search results in response to user requires.
Strong point of the paper
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This paper investigated the role of FE sever in improving
user-perceived performance of dynamic content distribution,
which is emerging as the next big business for CDN.
This paper developed a good and simple model-based
inference framework to measure and quantify the frontendto-backend fetching time, which contains the query
processing time at BE and delivery time between BE and
FE.
They used Bing and Google search services, and performed
extensive network measurement and analysis, based on
several sets of experiments.
This paper also took into consideration about the difference
between the FE of Bing and FE of Google.
Weakness of the Paper
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In this paper, they focused on standard search functions
of search engines. However, more recently, some search
engines introduced more advanced search features such
as the interactive feature. By using this feature, after
each letter user typed, a separate query is sent to the FE
sever. And subsequent queries are highly correlated.
Most nodes they used for test may introduce some
unfairness between Bing and Google (because they are
placed closer to Bing FE sever).
No significant packet loss during the measurements. In
a high loss rate environment, placing FE closer to users
may significantly improve the user-perceived end-toend performance.
Thanks
Any Questions?