Post on 13-Dec-2015
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Mining Web Traces:Workload Characterization, Performance Diagnosis, and Applications
Lili QiuMicrosoft Research
Internal TalkNovember 2002
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Motivation
Why do we care about Web traces? Content providers
How do users come to visit the Web site? Why do users leave the Web site? Is poor
performance the cause for this? Where are the performance bottlenecks? What content are users interested in? How does users’ interest change in time? How does users’ interest change across
different geographical regions?
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Motivation (Cont.)
Web hosting companies Accounting & billing Server selection Provisioning server farms: where to place servers
ISPs How to save bandwidth by storing proxy caches? Traffic engineering & provisioning
System designers Where are the performance bottlenecks? How to improve Web performance? Examples: Traffic measurements have influenced
the design of HTTP (e.g., persistent connections and pipeline), TCP (e.g., initial congestion window)
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Outline
Background Web workload characterization Performance diagnosis Applications of traces Bibliography
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Part I: Background
Web software components Web semantic components Web protocols Types of Web traces
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Web Software Components Web clients
An application that establishes connections to send Web requests
E.g., Mosaic, Netscape Navigator, Microsoft IE
Web servers An application that
accepts connections to service requests by sending back responses
E.g., Apache, Microsoft IIS
Web proxies (optional) Web replicas (optional)
Internetreplica
proxy
replica
proxy
proxy
WebClients
WebServer
s
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Web Semantic Components Uniform Resource Identifier (URI)
An identifier for a Web resource Name of protocol: http, https, ftp, .. Name of the server Name of the resource on the server e.g., http://www.foobar.com/info.html
Hypertext Markup Language (HTML) Platform-independent styles (indicated by markup tags)
that define the various components of a Web document Hypertext Transfer Protocol (HTTP)
Define the syntax and semantics of messages exchanged between Web software components
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Example of a Web Transaction
BrowserWeb server
DNSserver1. DNS query
2. Setup TCP connection
3. HTTP request
4. HTTP response
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Internet Protocol Stack
Application layer: application programs (HTTP, Telnet, FTP, DNS)
Transport layer: error control + flow control (TCP,UDP)
Network layer: routing (IP)
Datalink layer: handle hardware details(Ethernet, ATM)
Physical layer: moving bits(coaxial cable, optical fiber)
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HTTP Protocol Hypertext Transfer Protocol (HTTP)
HTTP 1.0 [BLFF96] The most widely used HTTP version A “Stop and wait” protocol
HTTP 1.1 [GMF+99] Persistent connections: use one TCP connection for
multiple HTTP requests Pipelining: send multiple requests without waiting for
a response between requests Content negotiation Range requests Caching control …
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Types of Web Traces Application level traces
Server logs: CLF and ECLF formats CLF format
<remoteIP,remoteID,usrName,Time,request,responseCode,contentLength>e.g., 192.1.1.1, -, -, 8/1/2000, 10:00:00, “GET /news/index.asp HTTP/1.1”, 200, 3410
Proxies logs: CLF and ECLF formats Client logs: no standard logging formats
Packet level traces Collection method: monitor a network link Available tools: tcpdump, libpcap, netmon Concerns: packet dropping, timestamp accuracy
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Tutorial Outline
Background Web workload characterization Performance diagnosis Applications of traces Bibliography
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Part II: Web Workload Characterization
Overview of workload characterization
Content dynamics Access dynamics Common pitfalls Case studies
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Overview of Workload Characterization
Process of trace analyses Common analysis techniques Common analysis tools Challenges in workload characterization
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Process of Trace Analyses
Collect traces where to monitor, how to collect (e.g.,
efficiency, privacy, accuracy) Determine key metrics to characterize Process traces Draw inferences from the data Apply the traces or insights gained from
the trace analyses to design better protocols & systems
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Common Analysis Techniques - Statistics
Mean Median Geometric mean: less sensitive to outliers
Variance and standard deviation
Confidence interval A range of values that has a specified probability of
containing the parameter being estimated Example: 95% confidence interval 10 x 20
)var()(,)(1
)var(1
2 xxstduxN
xN
ii
)(log xEnixGM
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Common Analysis Techniques – Statistics (Cont.)
Cumulative distribution (CDF): (a, P(X a)) Complementary CDF: (a, P(x>a)) Probability density function (PDF)
Derivative of CDF: f(x) = dF(x)/dx Check for heavy tail distribution
Log-log complementary plot, and check its tail Example: Pareto distribution
If 2, distribution has infinite variance (a heavy tail)If 1, distribution has infinite mean
axax
axF ,0,,)(1)(
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Common Analysis Techniques – Data Fitting
Visually compare two distributions Chi Squared tests [AS86,Jain91]
Divide the data points into k bins Compute
If X2 X2(,k-c), then two distributions are close, where is significance level, c is the number of estimated parameters for the distribution + 1
Need enough samples Kolmogorov-Smirnov tests [AS86,Jain91]
Compares two distributions by finding the maximum differences between two variables’ cumulative distribution functions
Need to fully specify the distribution
k
i i
ii
E
ExX
1
22 )(
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Common Analysis Techniques – Data Fitting (Cont.)
Anderson-Darling Test [Ste74] Modification of the Kolmogorov-Smirnov test, giving more
weight to the tails
If A critical value, two distributions are similar; otherwise they are not (F is CDF, and Yi are ordered data)
Quantile-quantile plots [AS86,Jain91] Compare two distributions by plotting the inverse of the
cumulative distribution function F-1(x) for two variables, and find best fitting line
If the slope of the line is close to 1, and y-intercept is close to 0, the two data sets are almost identically distributed
N
iiNi YFInYInF
N
iS
whereSNA
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2
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,
20
Common Analysis Tools
Scripting languages VB, Perl, awk, UNIX shell scripts, …
Databases SQL, DB2, …
Statistics packages Matlab, S+, R, SAS, …
Write our own low level programs C, C++, C#, …
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Challenges in Workload Characterization
Workload characteristics vary both in space and in time
Each of the Web components provides a limited perspective on the functioning of the Web
Internetreplica
proxy
replica
proxy
proxy
Clients Servers
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Workload Variation Vary with measurement points Vary with sites being measured
Information servers (news site), e-commercial servers, query servers, streaming servers, upload servers
US vs. Europe, … Vary with the clients being measured
Internet clients vs. wireless clients University clients vs. home users US vs. Europe, …
Vary in time Day vs. night Weekday vs. weekend Changes with new applications, recent events Evolve over time, …
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Different Web Components’ Views
View from clients Know details of client activities, such as requests satisfied by
browser caches, client aborts The ability to record detailed information
View from servers Most requests to the servers, excluding those satisfied by browser
& proxy caches May not log detailed information to ensure fast processing of client
requests View from proxies
Depending on the proxy’s location A proxy close to clients see requests from a a small number of
clients to a large number of servers [KR00] A proxy close to the servers see requests from a large number of
clients to a small number of servers [KR00] Requests satisfied by browser caches or proxy caches encountered
earlier will not appear in the logs
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Part II: Web Workload Overview Content dynamics Access dynamics Common pitfalls Case studies
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Content Dynamics
File types File size distribution File update patterns
How often files are updated How much files are updated
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File Types
Text files HTML, plain text, …
Images Jpeg, gif, bitmap, …
Applications Javascript, cgi, asp, pdf, ps, gzip, ppt, …
Multimedia files Audio, video
…
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File Size Distribution
Two definitions D1: Size of all files on a Web server D2: Size of all files transferred by a Web
server D1 D2, because some files can be
transferred multiple times or not in completion and other files are not transferred
Studies show that the distribution of file sizes in both definitions exhibit heavy tails (i.e., P[F > x] ~ x-, 0 2)
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File Update Interval
Varies in time Hot events and fast changing events require more
frequent update, e.g., Worldcup Varies across sites
Depending on server update policies & update tools Depending on the nature of content (e.g., University
sites have slower update rate than news sites) Recent studies
Study of the proxy traces collected at DEC and AT&T in 1996 showed the rate of file change depended on content type, top-level domains etc. [DFK+97]
Study of 1999 MSNBC logs shows that modification history yields a rough predictor of future modification interval [PQ00]
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Extent of Change upon Modifications
Varies in time Different events trigger different amount of
updates Varies across sites
Depending on servers’ update policies and update tools
Depending on the nature of the content Recent studies
Studies of 1996 DEC and AT&T proxy [MDF+97] and 1999 MSNBC log [PQ00] show that most file modifications are small delta encoding can be very useful
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Part II: Web Workload Motivation Limitations of workload
measurements Content dynamics Access Dynamics Common pitfalls Case studies
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Access Dynamics
File popularity distribution Temporal stability Spatial locality User request arrivals & durations
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Document Popularity
Web requests follow Zipf-like distribution Request frequency 1/i, where i is a document’s ranking The value of depends on the point of measurements
Between 0.6 and 1 for client traces and proxy traces Close to or larger than 1 for server traces [ABC+96, PQ00]
The value of varies over time (e.g., larger during hot events)
0
0.5
1
1.5
2
Clients/Proxies Less popular servers MSNBC
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Impact of the value Larger means more
concentrated accesses on popular documents caching is more beneficial
90% of the accesses are accounted by
Top 36% files in proxy traces [BCF+99, PQ00]
Top 10% files in small departmental server logs reported in [AW96]
Top 2-4% files in MSNBC traces
0
0.2
0.4
0.6
0.8
1
0 0.2 0.4 0.6 0.8 1
Percentage of Documents (sorted by popularity)
Pe
rce
nta
ge
of R
eq
ue
sts
12/17/98 Server Traces 08/01/99 Server Traces10/06/99 Proxy Traces
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Temporal Stability Metrics
Coarse-grained: likely duration that a set of current popular files remain popular
e.g., overlap between the set of popular documents on day 1 and day 2
Fine-grained: how soon a requested file will be requested again
e.g., LRU stack distance [ABC+96]
File 5
File 4File 3
File 2File 1
File 2
File 5File 4
File 3File 1
Stack distance = 4
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Spatial Locality
Refers to if users in the same geographical location or at the same organization tend to request a similar set of content E.g., compare the degree of requests locally
shared
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Spatial Locality (Cont.)
Normal Day
0
0.2
0.4
0.6
0.8
1
0.E+00 1.E+04 2.E+04 3.E+04 4.E+04 5.E+04
Domain ID
Fra
cti
on
of
req
ue
sts
s
ha
red
Domain membership is significant except when there is a “hot” event of global interest
Hot Event
0
0.2
0.4
0.6
0.8
1
1.2
0.0E+00 5.0E+03 1.0E+04 1.5E+04 2.0E+04 2.5E+04 3.0E+04 3.5E+04
Domain IDFr
actio
n of
re
ques
ts s
hare
d
Domain-based clustering Random clustering
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User Request Arrivals & Duration User workload at three levels
Session: a consecutive series of requests from a user to a Web site
Click: a user action to request a page, submit a form, etc. Request: each click generates one or more HTTP requests
Exponential distribution [LNJV99,KR01] Session duration
Heavy-tail distribution [KR01] # clicks in a session, most in the range of 4-6 [Mah97] # embedded references in a Web page Think time: time between clicks Active time: time to download a Web page and its
embedded images
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Common Pitfalls Trace analyses are all about writing scripts & plotting
nice graphs Challenges
Trace collection: where to monitor, how to collect (e.g., efficiency, privacy, accuracy)
Identify important metrics, and understand why they are important
Sound measurements require disciplines [Pax97] Dealing with errors and outliers Draw implications from data analyses
Understanding the limitation of the traces No representative traces: workload changes in time and in
space Try to diversify data sets (e.g., collect traces at different
places and different sites) before jumping into conclusions Draw inferences more than what data show
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Part II: Web Workload Motivation Limitations of workload measurements Content dynamics Access dynamics Common pitfalls Case studies
Boston University client log study UW proxy log study MSNBC server log study Popular Mobile server log study
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Case Study I: BU Client Log Study
Overview One of the few client log studies Analyze clients’ browsing pattern and their impact on
network traffic [CBC95] Approaches
Trace collection Modify Mosaic and distribute it to machines in CS Dept. at
Boston Univ. to collect client traces in 1995 Log format: <client machine, request time, user id, URI,
document size, retrieval time> Data analyses
Distribution of document size, document popularity Relationship between retrieval latency and response size Implications on caching strategies
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Major Findings
Power law distributions Distribution of document sizes Distribution of user requests for documents # requests to documents as a function of
their popularity Caching strategies should take into
account of document size (i.e., give preference to smaller documents)
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Case Study II: UW Proxy Log Study
Overview Proxy traces collected at the University of
Washington Approaches [WVS+99a, WVS+99b]
Trace collection: deploy a passive network sniffer between the Univ. of Washington and the rest of the Internet in May 1999
Set well-defined objectives Understand the extent of document sharing within
an organization and across different organizations Understand the performance benefit of cooperative
proxy caching
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Major Findings
Members of an organization are more likely to request the same documents than a random set of clients
Most popular documents are globally popular
Cooperative caching is most beneficial for small organizations
Cooperative caching among large organizations yield minor improvement if any
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Case Study III: MSNBC Server Log Study
Overview of MSNBC server site a large news site server cluster with 40 nodes 25 million accesses a day (HTML content
alone) Period studied: Aug. – Oct. 99 & Dec. 17, 98
flash crowd
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Approaches Trace collection
HTTP access logs Content Replication System (CRS) logs HTML content logs
Data analyses Content dynamics
How often files are modified? How to predict modification interval? How much does a file change upon modification?
Access dynamics Document popularity Temporal stability Spatial locality Correlation between document age and popularity
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Major Findings
Content dynamics Modification history is a rough predictor guide for setting
TTL, but need an alternative mechanism (e.g., callback based invalidation) as backup
Frequent but minimal file modifications delta encoding Access dynamics
Set of popular files remains stable for days pushing/prefetching previous hot data that have undergone modifications
Domain membership has a significant bearing on client accesses except during a flash crowd of global interest make sense to have a proxy cache for an organization
Zipf-like distribution of file popularity but with a much larger than at proxies potential of reverse caching and replication
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Case Study IV: Popular Mobile Server Log
Overview of a popular commercial Web site for mobile clients Content
news, weather, stock quotes, email, yellow pages, travel reservations, entertainment etc.
Services Notification Browse
Period studied 3.25 million notifications in Aug. 20 – 26, 2000 33 million browse requests in Aug. 15 – 26, 2000
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Approaches Analyze by user categories
Cellular users Browse the Web in real time using cellular technologies
Offline users Download content onto their PDAs for later (offline)
browsing, e.g. AvantGo Desktop users
Signup services and specify preferences Analyze by Web services
Browse Notifications
Use SQL database to manage data
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Major Findings
Notification Services Popularity of notification messages follows a Zipf-like
distribution, with top 1% notification objects responsible for 54-64% of total messages multicast notifications
Exhibits geographical locality useful to provide localized notification services
Browse Services 0.1% - 0.5% queries account for 90% requests cache
the results of popular queries The set of popular queries remain stable cache a
stable set of queries or optimize query based on a stable workload
Correlation between the two services Correlation is limited influence design of pricing plans
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Tutorial Outline
Background Web Workload Performance Diagnosis Applications of traces
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Part III: Performance Diagnosis
Overview of performance diagnosis Infer the causes of high end-to-end delay
in Web transfers [BC00] Infer the causes of high end-to-end loss
rate in Web transfers [CDH+99,DPP+01,NC01,PQ02, PQW02]
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Overview of Performance Diagnosis
Goal: determine trouble spot locations
Metrics of interest Delay Loss rate Raw bandwidth Available bandwidth Traffic rate
Why interesting Resolve the trouble spots Server selection Placement of mirror
servers
Sprint
AT&T
Web Server
UUNET
MCI
Qwest AOL
EarthlinkWhy so slow?
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Finding the Sources of Delays Goal
Why is my Web transfer slow? Is it because of the server or the network or the client?
Sources of delay in Web transfer DNS lookup Server delays Client delays Network delays
Propagation delays Queuing delays Delays introduced by packet losses (e.g., signaled by
the fast retransmit mechanism or TCP timeouts)
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TCPEval Tool
Inputs: “tcpdump” packet traces taken at the communicating Web server and client
Generates a variety of statistics for file transactions File and packet transfer latencies Packet drop characteristics Packet and byte counts per unit time
Generates both timeline and sequence plots for transactions
Generates critical path profiles and statistics for transactions
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Critical Path Analysis Tool [BC00]
Client Server Client ServerData flow Critical Path
Network delay
Network delayServer delayNetwork delay
Client delayNetwork delayServer delay
Network delaydue to pkt loss
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Finding Sources of Packet Losses Goal
Identify lossy links
l1
l8l7l6
l2
l4 l5
l3
server
clientsp1 p2 p3 p4 p5
(1-l1)*(1-l2)*(1-l4) = (1-p1)
(1-l1)*(1-l2)*(1-l5) = (1-p2)…(1-l1)*(1-l3)*(1-l8) = (1-p5)
Challenges - an under-constrained
system of equations- measurement errors
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Approaches Active probing
Probing Multicast probes Striped unicast probes
Technique -- Expectation Maximization (EM) [CDH+99, DPP+01]
a numerical algorithm to compute that maximizes P(D|), where D are observations, are ensemble of link loss rates
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Approaches (Cont.) Passive monitoring
Random sampling Random sample the solution space, and draw conclusions
based on samples Akin to Monte Carlo sampling
Linear optimization Determine a unique solution by optimizing an objective
function Gibbs sampling
Determine P(|D) by drawing samplings, where is ensemble of loss rates of links in the network, and D is observed packet transmission and losses at the clients
EM A numerical algorithm to compute that maximizes P(D|)
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Other Performance Studies using Web traces
Characterize Internet performance (e.g., spatial & temporal locality) [BSS+97]
Study the behavior of TCP during Web transfers [BPS+98]
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Tutorial Outline
Background Web Workload Performance Diagnosis Applications of traces Bibliography
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Part IV: Applications of Traces Synthetic workload generation Cache design
Cache replacement policies [CI97,BCF+99] Cache consistency algorithms [LC97, YBS99,YAD+01] Cooperative cache or not [WVS+99] Cache infrastructure
Pre-fetching algorithms [CB98, FJC+99] Placement of Web proxies/replicas [QPV01] Other optimizations
Improving TCP for Web transfers [Mah97,PK98,ZQK00] Concurrent downloads, pipelining, compression,…
…
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Synthetic Workload Generation
Generate user requests Generate user sessions using a Poisson arrival
process For each user session, determine # clicks
using a Pareto distribution Assign a click to a request for a Web page,
while making sure The popularity distribution of files follows a Zipf-like
distribution [BC98] Capture the temporal locality of successive requests
for the same resource Generate a next click from the same user with
think time following a Pareto distribution
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Synthetic Workload Generation (Cont.)
Generate Web pages Determine the number of Web pages Generate the size of each Web pages using a
log-normal distribution Associate a page with some number of
embedded pages using an empirical distribution (heavy-tail)
Generate file modification events Examples of generators
Webbench [Wbe], WebStone[TS95], Surge [BC98], SPecweb99 [SP99], Web Polygraph [WP], …
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Cache Replacement Policies Problem formulation
Given a fixed size cache, how to evict pages to maximize the hit ratio once the cache is full?
Hit ratio Fraction of requests satisfied by the cache Fraction of the total size of requested data satisfied by the
cache Factors to consider
Request frequency Modification frequency Benefit of caching: reduction in latency & BW Cost of caching: storage Caveat: NOT all hits are equal. Hit ratios do NOT map
directly to performance improvement.
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Cache Replacement Policies (Cont.) Approaches
Least recently used (LRU) Least frequently used (LFU)
Perfect: maintain counters for all pages seen In-cache: maintain counters only for pages that are in
cache GreedyDual-size [CI97]
Assign a utility value to each object, and replace the one with the lowest utility
Use of traces Evaluate the algorithms using trace-driven simulations Analytically derive the hit ratios for different
replacement policies based on a workload model
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References [AS86] R. B. D’Agostino and M. A. Stephens. Goodness-of-Fit Techniques. Marcel
Dekker, New York, NY 1986. [ABC+96] Virgilio Almeida, Azer Bestavros, Mark Crovella and Adriana de Oliveria.
Characterizing reference locality in the WWW. In Proceedings of 1996 International Conference on Parallel and Distributed Information Systems (PDIS'96), December 1996.
[ABQ01] A. Adya, P. Bahl, and L. Qiu. Analyzing Browse Patterns of Mobile Clients. In Proc. of SIGCOMM Measurement Workshop, Nov. 2001.
[ABQ02] A. Adya, P. Bahl, and L. Qiu. Characterizing Alert and Browse Services for Mobile Clients. In Proc. of USENIX, Jun. 2002.
[AL01] P. Albitz, and C. Liu. DNS and BIND (4th Edition), O’Reilly & Associates, Apr. 2001.
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67
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[BCF+99] L. Breslau, P. Cao, L. Fan, G. Phillips, and S. Shenker. Web Caching and Zipf-like Distributions: Evidence and Implications. In Proc. of INFOCOM, Mar. 1999.
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[BSS+97] H. Balakrishnan, S. Seshan, M. Stemm, and R. H. Katz. Analyzing Stability in Wide-Area Network Performance. In Proc. of SIGMETRICS, Jun. 1997.
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[CB98] M. Crovella and P. Barford. The network effects of prefetching. In Proc. of INFOCOM, 1998.
[CBC95] C. R. Cunha, A. Bestavros, and M. E. Crovella. Characteristics of WWW client-based traces. Technical Report BU-CS-95-010, CS Dept., Boston University, 1995.
[CI97] P. Cao and S. Irani. Cost-Aware WWW proxy caching algorithms. In Proc. of USITS, Dec. 1997.
[DFK+97] F. Douglis, A. Feldmann, B. Krishnamurth, and J. Mogul. Rate of change and other metrics: a live study of the World Wide Web. In Proc. of USITS, 1997.
[DPP+01] N. G. Duffield, F. Lo Presti, V. Paxson, D. Towsley. In Proc. Infocom, Apr. 2001.
[FCD+99] A. Feldmann, R. Caceres, F. Douglis, and M. Rabinovich. Performance of Web Proxy Caching in heterogeneous bandwidth enviornments. In Proc. of INFOCOM, March 1999.
69
References (Cont.) [FJC+99] L. Fan, Q. Jacobson, P. Cao and W. Lin. Web Prefetching Between Low-
Bandwidth Clients and Proxies: Potential and Performance. In Proc. of SIGMETRICS, 1999.
[FCT+02] Y. Fu, L. Cherkassova, W. Tang, and A. Vahdat. EtE: Passive End-to-End Internet Service Performance Monitering. In Proc. of USENIX, Jun. 2002.
[GMF+99] J. Gettys, J. Mogul, H. Frystyk, L. Masinter, P. Leach, T. Berners-Lee. Hypertext Transfer Protocol – HTTP 1.1. RFC 2616, Jun. 1999.
[JK88] V. Jacobson, M. J. Karels. Congestion Avoidance and Control. In Proc. SIGCOMM, Aug. 1988.
[JJK+01] S. Jamin, C. Jin, A. R. Kurc, D. Raz, and Y. Shavitt. Constrained Mirror Placement on the Internet. In Proc. of INFOCOM, Apr. 2001.
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[Kel02] T. Kelly. Thin-Client Web Access Patterns: Measurements from a Cache-Busting Proxy. Computer Communications, Vol. 25, No. 4 (March 2002), pages 357-366.
[KR01] B. Krishnamurthy and J. Rexford. Web Protocols and Practice, HTTP/1.1, Networking Protocols, Caching, and Traffic Measurement. Addison-Wesley, May 2001.
70
References (Cont.) [LC97] C. Liu and P. Cao. Maintaining Strong Cache Consistency in the World-
Wide Web. In Proc. of ICDCS'97, pp. 12-21, May 1997. [LNJV99] Z. Liu, N. Niclausse, and C. Jalpa-Villaneuva. Web Traffic Modeling
and Performance Comparison Between HTTP 1.0 and HTTP 1.1. In Erol Gelenbe, editor, System Performance Evaluation: Methodologies and Applications. CRC Press, Aug. 1999.
[Mah97] Bruce Mah. An empirical model of HTTP network traffic. In Proc. of INFOCOM, April 1997.
[Mogul95] Jeffrey C. Mogul. The Case for Persistent-Connection HTTP. In Proc. SIGCOMM '95, pages 299-313. Cambridge, MA, August, 1995.
[MDF+97] J. C. Mogul, F. Douglis, A. Feldmann, and B. Krishnamurthy. Potential benefits of delta-encoding and data compression for HTTP, In Proc. of SIGCOMM, September 1997.
[NC01] R. Nowak and M. Coates. Unicast Network Tomography using the EM algorithm. Submitted to IEEE Transactions on Information Theory, Dec. 2001
[Pad95] V. N. Padmanabhan. Improving World Wide Web Latency. Technical Report UCB/CSD-95-875, University of California, Berkeley, May 1995.
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References (Cont.) [PQ00] V. N. Padmanabhan and L. Qiu. The Content and Access Dynamics of a
Busy Web Server. In Proc. of SIGCOMM, Aug. 2000. [PQ02] V. N. Padmanabhan and L. Qiu. Network Tomography using Passive End-
to-End Measurements, DIMACS on Internet and WWW Measurement, Mapping and Modeling, Feb. 2002.
[PQW02] V. N. Padmanabhan, L. Qiu, and H. J. Wang. Passive Network Tomography using Bayesian Inference. Internet Measurement Workshop, Nov. 2002.
[QPV01] L. Qiu, V. N. Padmanabhan, and G. M. Voelker. On the Placement of Web Server Replicas. In Proc. of INFOCOM, Apr. 2001.
[SP99] SPECWeb99 Benchmark. http://www.spec.org/osg/web99/. [Pax98] V. Paxson. An Introduction to Internet Measurement and Modeling.
SIGCOMM’98 tutorial, August 1998. [Ste74] M. A. Stephens. EDF Statistics for Goodness of Fit and Some Comparison.
Journal of the American Statistical Association, Vol. 69, pp. 730 – 737. [TS95] G. Trent and M. Sake. WebStone: The First Generation in HTTP Server
Benchmarking, Feb. 1995. http://www.mindcraft.com/webstone/paper.html.
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References (Cont.) [Wbe] Webbench. http://www.zdnet.com/etestinglabs
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Acknowledgement
Thank Alec Wolman for his helpfulcomments.
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