1 The Web: some jargon r Web page: m consists of “objects” m addressed by a URL r Most Web pages...

1

The Web: some jargon

Web page: consists of “objects” addressed by a URL

Most Web pages consist of: base HTML page, and several referenced

objects.

URL has two components: host name and path name:

User agent for Web is called a browser: MS Internet Explorer Netscape

Communicator

Server for Web is called Web server: Apache (public

domain) MS Internet

Information Server

www.someSchool.edu/someDept/pic.gif

2

The Web: the http protocol

http: hypertext transfer protocol

Web’s application layer protocol

client/server model client: browser that

requests, receives, “displays” Web objects

server: Web server sends objects in response to requests

http1.0: RFC 1945 http1.1: RFC 2068

PC runningExplorer

Server running

NCSA Webserver

Mac runningNavigator

http request

http re

quest

http response

http re

sponse

3

The http protocol: more

http: TCP transport service:

client initiates TCP connection (creates socket) to server, port 80

server accepts TCP connection from client

http messages (application-layer protocol messages) exchanged between browser (http client) and Web server (http server)

TCP connection closed

http is “stateless” server maintains no

information about past client requests

Protocols that maintain “state” are complex!

past history (state) must be maintained

if server/client crashes, their views of “state” may be inconsistent, must be reconciled

aside

4

http exampleSuppose user enters URL www.someSchool.edu/someDepartment/home.index

1a. http client initiates TCP connection to http server (process) at www.someSchool.edu. Port 80 is default for http server.

2. http client sends http request message (containing URL) into TCP connection socket

1b. http server at host www.someSchool.edu waiting for TCP connection at port 80. “accepts” connection, notifying client

3. http server receives request message, forms response message containing requested object (someDepartment/home.index), sends message into socket

time

(contains text, references to 10

jpeg images)

5

http example (cont.)

5. http client receives response message containing html file, displays html. Parsing html file, finds 10 referenced jpeg objects

6. Steps 1-5 repeated for each of 10 jpeg objects

4. http server closes TCP connection.

time

6

Non-persistent and persistent connections

Non-persistent HTTP/1.0 server parses

request, responds, and closes TCP connection

2 RTTs to fetch each object

Each object transfer suffers from slow start

Persistent default for HTTP/1.1 on same TCP

connection: server, parses request, responds, parses new request,..

Client sends requests for all referenced objects as soon as it receives base HTML.

Fewer RTTs and less slow start.But most 1.0 browsers use

parallel TCP connections.

7

http message format: request

two types of http messages: request, response http request message:

ASCII (human-readable format)

GET /somedir/page.html HTTP/1.0 User-agent: Mozilla/4.0 Accept: text/html, image/gif,image/jpeg Accept-language:fr

(extra carriage return, line feed)

request line(GET, POST,

HEAD commands)

header lines

Carriage return, line feed

indicates end of message

8

http request message: general format

9

http message format: respone

HTTP/1.0 200 OK Date: Thu, 06 Aug 1998 12:00:15 GMT Server: Apache/1.3.0 (Unix) Last-Modified: Mon, 22 Jun 1998 …... Content-Length: 6821 Content-Type: text/html data data data data data ...

status line(protocol

status codestatus phrase)

header lines

data, e.g., requestedhtml file

10

http response status codes

200 OK request succeeded, requested object later in this

message

301 Moved Permanently requested object moved, new location specified later

in this message (Location:)

400 Bad Request request message not understood by server

404 Not Found requested document not found on this server

505 HTTP Version Not Supported

In first line in server->client response message.A few sample codes:

11

Trying out http (client side) for yourself

1. Telnet to your favorite Web server:

Opens TCP connection to port 80(default http server port) at www.eurecom.fr.Anything typed in sent to port 80 at www.eurecom.fr

telnet www.eurecom.fr 80

2. Type in a GET http request:

GET /~ross/index.html HTTP/1.0 By typing this in (hit carriagereturn twice), you sendthis minimal (but complete) GET request to http server

3. Look at response message sent by http server!

12

User-server interaction: authentication

Authentication goal: control access to server documents

stateless: client must present authorization in each request

authorization: typically name, password authorization: header

line in request if no authorization

presented, server refuses access, sendsWWW authenticate:

header line in response

client server

usual http request msg401: authorization req.

WWW authenticate:

usual http request msg

+ Authorization:lineusual http response

msg


+ Authorization:lineusual http response

msg

time

Browser caches name & password sothat user does not have to repeatedly enter it.

13

User-server interaction: cookies

server sends “cookie” to client in response mstSet-cookie: 1678453

client presents cookie in later requestscookie: 1678453

server matches presented-cookie with server-stored info authentication remembering user

preferences, previous choices

client server

usual http request msgusual http response

+Set-cookie: #


cookie: #usual http response

msg


cookie: #usual http response msg

cookie-spectificaction

cookie-spectificaction

14

User-server interaction: conditional GET

Goal: don’t send object if client has up-to-date stored (cached) version

client: specify date of cached copy in http requestIf-modified-since:

<date> server: response contains

no object if cached copy up-to-date: HTTP/1.0 304 Not

Modified

client server

http request msgIf-modified-since:

<date>

http responseHTTP/1.0

304 Not Modified

object not

modified

http request msgIf-modified-since:

<date>

http responseHTTP/1.1 200 OK

…

<data>

object modified

15

Web Caches (proxy server)

user sets browser: Web accesses via web cache

client sends all http requests to web cache if object at web cache,

web cache immediately returns object in http response

else requests object from origin server, then returns http response to client

Goal: satisfy client request without involving origin server

client

Proxyserver

client

http request

http re

quest

http response

http re

sponse

http re

quest

http re

sponse

http requesthttp response

origin server

origin server

16

Why Web Caching?

Assume: cache is “close” to client (e.g., in same network)

smaller response time: cache “closer” to client

decrease traffic to distant servers link out of

institutional/local ISP network often bottleneck

originservers

public Internet

institutionalnetwork 10 Mbps LAN

1.5 Mbps access link

institutionalcache

17

참고 자료 1: web caching

18

Large-Scale Web Caching and Content Delivery

Jeff Chase

19

Caching for a Better Web

Performance is a major concern in the Web

Proxy caching is the most widely used method to improve Web performance Duplicate requests to the same document

served from cache Hits reduce latency, network utilization,

server load Misses increase latency (extra hops)

Clients Proxy Cache Servers

Hits

Misses Misses

Internet

[Source: Geoff Voelker]

20

Cache Effectiveness

Previous work has shown that hit rate increases with population size [Duska et al. 97, Breslau et al. 98]

However, single proxy caches have practical limits Load, network topology, organizational

constraints One technique to scale the client

population is to have proxy caches cooperate


21

Cooperative Web Proxy Caching

Sharing and/or coordination of cache state among multiple Web proxy cache nodes

Effectiveness of proxy cooperation depends on:

Inter-proxy communication distance Size of client population served

Proxy utilization and load balance

Clients

ClientsProxy

Clients

Internet


22

Resolve misses through the parent.

Hierarchical Caches

INTERNET

clients

origin Web site(e.g., U.S. Congress)

clientsclients

Idea: place caches at exchange or switching points in the network, and cache at each level of the hierarchy.

upstream

downstream

23

Content-Sharing Among Peers

INTERNET

clientsclients

clients

Idea: Since siblings are “close” in the network, allow them to share their cache contents directly.

24

Harvest-Style ICP Hierarchies

INTERNET

clientquery (probe)

query response

object request

object response

ExamplesHarvest [Schwartz96]Squid (NLANR)NetApp NetCache

Idea: multicast probes within each “family”: pick first hit response or wait for all miss responses.

25

Issues for Cache Hierarchies

With ICP: query traffic within “families” (size n)• Inter-sibling ICP traffic (and aggregate overhead) is quadratic

with n.• Query-handling overhead grows linearly with n.

miss latency• Object passes through every cache from origin to

client: deeper hierarchies scale better, but impose higher latencies.

storage• A recently-fetched object is replicated at every level of

the tree. effectiveness

• Interior cache benefits are limited by capacity if objects are not likely to live there long (e.g., LRU).

26

Hashing: Cache Array Routing Protocol (CARP)

INTERNET

“GET www.hotsite.com”

Microsoft Proxy Server

g-pv-z

q-ua-f

Advantages1. single-hop request resolution2. no redundant caching of objects3. allows client-side implementation4. no new cache-cache protocols5. reconfigurable

hashfunction

27

Issues for CARP

no way to exploit network locality at each level• e.g., relies on local browser caches to absorb repeats

load balancing hash can be balanced and/or weighted with a load factor

reflecting the capacity/power of each server must rebalance on server failures

• Reassigns (1/n)th of cached URLs for array size n.• URLs from failed server are evenly distributed among the

remaining n-1 servers.

miss penalty and cost to compute the hash• In CARP, hash cost is linear in n: hash with each node

and pick the “winner”.

28

Directory-based: Summary Cache for ICP Idea: each caching server replicates the cache

directory (“summary”) of each of its peers (e.g., siblings).

• [Cao et. al. Sigcomm98]

Query a peer only if its local summary indicates a hit. To reduce storage overhead for summaries, implement

the summaries compactly using Bloom Filters.– May yield false hits (e.g., 1%), but not false misses.

– Each summary is three orders of magnitude smaller than the cache itself, and can be updated by multicasting just the flipped bits.

29

A Summary-ICP Hierarchy

INTERNET

clientquery

query response

object request

object response

Summary caches at each level of the hierarchyreduce inter-sibling miss queries by 95+%.

hit

misse.g., Squid configuredto use cache digests

30

Issues for Directory-Based Caches

Servers update their summaries lazily.• Update when “new” entries exceed some

threshold percentage.• Update delays may yield false hits and/or false

misses. Other ways to reduce directory size?

• Vicinity cache [Gadde/Chase/Rabinovich98]• Subsetting by popularity

[Gadde/Chase/Rabinovich97] What are the limits to scalability?

• If we grow the number of peers?• If we grow the cache sizes?

31

On the Scale and Performance.... [Wolman/Voelker/.../Levy99] is a key paper in

this area over the last few years. first negative result in SOSP (?) illustrates tools for evaluating wide-area systems

• simulation and analytical modeling illustrates fundamental limits of caching

• benefits dictated by reference patterns and object rate of change

• forget about capacity, and assume ideal cooperation ties together previous work in the field

• wide-area cooperative caching strategies• analytical models for Web workloads

best traces

32

UW Trace Characteristics

Trace UW

Duration 7 daysHTTP objects 18.4 millionHTTP requests 82.8 millionAvg. requests/sec 137Total Bytes 677 GBServers 244,211Clients 22,984


33

A Multi-Organization Trace

University of Washington (UW) is a large and diverse client population

• Approximately 50K people

UW client population contains 200 independent campus organizations

• Museums of Art and Natural History• Schools of Medicine, Dentistry, Nursing• Departments of Computer Science, History, and Music

A trace of UW is effectively a simultaneous trace of 200 diverse client organizations Key: Tagged clients according to their organization in trace


34

Cooperation Across Organizations

Treat each UW organization as an independent “company”

Evaluate cooperative caching among these organizations

How much Web document reuse is there among these organizations? Place a proxy cache in front of each

organization. What is the benefit of cooperative caching

among these 200 proxies?


35

Ideal Hit Rates for UW proxies

Ideal hit rate - infinite storage, ignore cacheability, expirations

Average ideal local hit rate: 43%


36

Ideal Hit Rates for UW proxies

Ideal hit rate - infinite storage, ignore cacheability, expirations

Average ideal localhit rate: 43%

Explore benefits of perfect cooperation rather than a particular algorithm

Average ideal hit rate increases from 43% to 69% with cooperative caching


37

Sharing Due to Affiliation

UW organizational sharing vs. random organizations Difference in weighted averages across all orgs is ~5%


38

Cacheable Hit Rates forUW proxies

Cacheable hit rate - same as ideal, but doesn’t ignore cacheability

Cacheable hit rates are much lower than ideal (average is 20%)

Average cacheable hit rate increases from 20% to 41% with (perfect) cooperative caching


39

Scaling Cooperative Caching

Organizations of this size can benefit significantly from cooperative caching

But…we don’t need cooperative caching to handle the entire UW population size A single proxy (or small cluster) can handle this

entire population! No technical reason to use cooperative caching

for this environment In the real world, decisions of proxy placement

are often political or geographical

How effective is cooperative caching at scales where a single cache cannot be used?


40

Hit Rate vs. Client Population

Curves similar to other studies [e.g., Duska97, Breslau98]

Small organizations Significant increase in hit

rate as client population increases

The reason why cooperative caching is effective for UW

Large organizations Marginal increase in hit rate

as client population increases


41

In the Paper...

1. Do we believe this? What are some possible sources of error in this tracing/simulation study?

• What impact might they have?

2. Why are “ideal” hit rates so much higher for the MS trace, but the cacheable hit rates are the same?

• What is the correlation between sharing and cacheability?

3. Why report byte hit rates as well as object hit rates?

• Is the difference significant? What does this tell us about reference patterns?

4. How can it be that byte hit rate increases with population, while bandwidth consumed is linear?

42

Trace-Driven Simulation: Sources of Error 1. End effects: is the trace interval long enough?

• Need adequate time for steady-state behavior to become apparent.

2. Sample size: is the population large enough?• Is it representative?

3. Completeness: does the sample accurately capture the client reference streams?

• What about browser caches and lower-level proxies? How would they affect the results?

4. Client subsets: how to select clients to represent a subpopulation?

5. Is the simulation accurate/realistic?• cacheability, capacity/replacement, expiration, latency

43

What about Latency?

From the client’s perspective, latency matters far more than hit rate

How does latency change with population?

Median latencies improve only a few 100 ms with ideal caching compared to no caching.


44

Questions/Issues

1. How did they obtain these reported latencies?

2. Why report median latency instead of mean?

• Is the difference significant? What does this tell us? Is it consistent with the reported byte hit ratios?

3. Why does the magnitude of the possible error decrease with population?

4. What about the future?• What changes in Web behavior might lead to different

conclusions in the future?• Will latency be as important? Bandwidth?

45

Large Organization Cooperation What is the benefit of cooperative

caching among large organizations?

Explore three ways Linear extrapolation of UW trace Simultaneous trace of two large

organizations (UW and MS) Analytic model for populations beyond trace

limits


46

Extrapolation to Larger Client Populations

Use least squares fit to create a linear extrapolation of hit rates

Hit rate increases logarithmically with client population, e.g., to increase hit rate by 10%:

Need 8 UWs (ideal) Need 11 UWs (cacheable)

“Low ceiling”, though: 61% at 2.1M clients (UW

cacheable)

A city-wide cooperative cache would get all the benefit [Source: Geoff Voelker]

47

UW & Microsoft Cooperation

Use traces of two large organizations to evaluate caching systems at medium-scale client populations

We collected a Microsoft proxy trace during same time period as the UW trace Combined population is ~80K clients Increases the UW population by a factor of 3.6 Increases the MS population by a factor of 1.4

Cooperation among UW & MS proxies… Gives marginal benefit: 2-4% Benefit matches “hit rate vs. population”

curve[Source: Geoff Voelker]

48

UW & Microsoft TracesTrace UW MS

Duration 7 days 6.25 days

HTTP objects 18.4 million 15.3 million

HTTP requests 82.8 million 107.7 million

Avg. requests/sec 137 199

Total Bytes 677 GB N/A

Servers 244,211 360,586

Clients 22,984 60,233

Population ~50,000 ~40,000


49

UW & MS Cooperative Caching

Is this worth it?[Source: Geoff Voelker]

50

Analytic Model

Use an analytic model to evaluate caching systems at very large client populations Parameterize with trace data, extrapolate beyond

trace limits

Steady-state model Assumes caches are in steady state, do not start cold Accounts for document rate of change Explore growth of Web, variation in document

popularity, rate of change

Results agree with trace extrapolations 95% of maximum benefit achieved at the scale of a

medium-large city (500,000)


51

Inside the Model

[Wolman/Voelker/Levy et. al., SOSP 1999] refines [Breslau/Cao et. al., 1999], and others

Approximates asymptotic cache behavior assuming Zipf-like object popularity caches have sufficient capacity

Parameters: = per-client request rate = rate of object change pc = percentage of objects that are cacheable = Zipf parameter (object popularity)

52

Zipf [Breslau/Cao99] and others observed that Web

accesses can be modeled using Zipf-like probability distributions. Rank objects by popularity: lower rank i ==> more

popular. The probability that any given reference is to the ith most

popular object is pi

• Not to be confused with pc, the percentage of cacheable objects.

Zipf says: “pi is proportional to 1/i, for some with 0 < < 1”. Higher gives more skew: popular objects are way popular. Lower gives a more heavy-tailed distribution. In the Web, ranges from 0.6 to 0.8 [Breslau/Cao99]. With =0.8, 0.3% of the objects get 40% of requests.

53

n

n

N

dxx

C

dx

nCxCx

C

1

1

1

1

11

Cacheable Hit Ratio: the Formula

CN is the hit ratio for cacheable objects achievable by population of size N with a universe of n objects.

N

54

n

n

N

dxx

C

dx

nCxCx

C

1

1

1

1

11

Inside the Hit Ratio Formula

Approximates a sum over a universe of n objects...

...of the probability of access to each object x...

…times the probability x was accessed since its last change.

C is just a normalizing constant for the Zipf-like popularity distribution,

which must sum to 1. C is not to be confused with CN.

C = 1/in [Breslau/Cao 99]

0 < < 1

N

55

n

n

N

dxx

C

dx

nCxCx

C

1

1

1

1

11

Inside the Hit Ratio Formula, Part 2

What is the probability that i was accessed since its last invalidate?

= (rate of accesses to i)/(rate of accesses or changes to i)

= Npi / (Npi + )

NDivide through by Npi.

Note: by Zipf pi = 1/Ci

so: 1/(Npi) = Ci/N

56

Hit Rates From Model

Cacheable Hit Rate Focus on

cacheable objects Four curves

correspond to different rate of change distributions Believe even Slow

and Mid-Slow are generous

Knee at 500K – 1M


57

Extrapolating UW & MS Hit Rates

[Graph from Geoff Voelker]

These are from the simulation results, ignoring rate of change (compare to graphs from analytic model).

What is the significance of slope ?

58

Latency From Model

Straightforward calculation from the hit rate results


59

Rate of Change

What is more important, the rate of change of popular objects or the rate of change of unpopular objects?

Separate popular from unpopular objects

Look at sensitivity of hit rate to variations in rate of change


60

Rate of Change Sensitivity

Popular docs sensitivity Top curve Unpopular low R-of-C Issue is minutes to

hoursUnpopular docs sensitivity

Bottom curve Popular low R-of-C Days to weeks to month

Unpopular more sensitive than popular!

Compare differences in hit rates between A,C and B,C


61

참고자료 2: Content Distribution Networks and Quality of Service

62

What is a CDN?

A system (often overlay network to Internet) for high-performance delivery of multimedia content.

A CDN maintains multiple locations with mirrors of the same content (known as surrogates) and redirects users to the most appropriate content location.

This distributes the load and also moves the content closer to the user, avoiding potential congestion and reducing response times (latency).

63

Need for CDNs

Multimedia such as videoconferences and streaming broadcasts. Sensitive to response-time delays Require large amounts of bandwidth

CDNs address these requirements by minimizing the number of backbone routers that content must traverse and distributing the bandwidth load

64

Victoria’s Secret

As an example of CDN’s scalability: Once a year the Victoria’s Secret Lingerie

Company broadcasts their Fashion Parade. 1,000,000+ viewers watching live @ 25 Kbps

The first year they tried it the enormous load crashed their centralized servers and many missed the show

Since then they have started using Yahoo and Akamai for their CDN. As many as 2 million watched the show in

2001 without any hiccups.

65

CDNs and Cache

Caches are used in the Internet to move content closer to the user. Reduces load on origin servers Eliminates redundant data traversal Reduces latency

CDNs make heavy use of cache Origin servers are fully or partially cached at

surrogate servers close to the users

66

Infrastructure

Request-routing Initiates communication between client and

a surrogate server. Content Distribution

Mechanisms that move content from origin servers to surrogates.

Content-delivery Consists of surrogate servers to delivery

copies of content to users.

67

How CDN Routing Works

1. Client requests content from a Site.

2. Site uses a CDN as their provider. Client gets redirected to the CDN.

3. Client gets redirected to most appropriate cache.

4. If the CDN has a cache at the Client’s ISP, the Client gets redirected to that cache.

5. The CDN cache serves the content to the client.

6. If content is served for ISP’s cache performance improves due to close proximity to client.

68

Request Routing

Direct a client’s request for objects served by a CDN to the most appropriate server.

Two commonly used methods:1. DNS Redirection2. URL Rewriting

69

DNS Redirection

Authoritative DNS server redirects client request by resolving CDN server to IP address of one content server.

A number of factors determine which content server is used in final resolution. Availability of resources, network conditions

etc Load balancing can be implemented by

specifying a low TTL field in a DNS reply.

70

DNS Redirection…

Two types of CDNs using redirection:1. Full site content delivery2. Partial site content delivery

71

Full Site Content Delivery

All requests for origin-server are redirected by DNS, to a CDN server.

Companies include: Adero NetCaching UniTech’s Networks IntelliDNS

72

Partial Site Content Delivery

Origin site alters an object’s URL so that it’s resolved by the CDN’s DNS server.eg: www.foo.com/bar.gif becomes

foo.cdn.com/www.foo.com/bar.gif Companies include:

Akamai Digital Island MirrorImage Speedera

73

URL Rewriting

Origin server dynamically generates pages to redirect clients to different content servers.

Page is dynamically rewritten with the IP address of a mirror server.

Clearway CDN is one such company.

74

Content Distribution

Mechanisms that move content from origin servers to surrogates.

Two different methods to get content to surrogates:

1. Pre-caching2. Just-in-time

75

Pre-Caching

Content is delivered to cache before requests are generated.

Used for highly distributed usage. Caches can be updated during off-hours

to reduce network load.

76

Just-In-Time

Content is pulled from the origin server to the cache when a request is received from a client.

The object is delivered to the client and simultaneously stored on the cache for later perusal.

Can implement multicasting for efficient content transfer between caches.

Leased lines may be used between servers to ensure QoS.

77

Content Delivery

Consists of servers placed around the Internet with each server caching the central content.

Transparent to end user – looks like it’s coming from central server.

Distributed structure means less load on all servers. Can support QoS for customers with differing needs.

Eg: Gold class, Silver class, Bronze class scheme Costs becomes cheaper

Cost of buying new servers is relatively cheaper than trying to obtain higher output from just one server.

78

CDN Usage in the Real World

Allow organisations to put on large multimedia events Internal: Company announcements, video

conferencing meetings, instructor-led staff training

External: Event hosting such as concerts, fashion shows

Allow organisations to improve internal data flows Decentralised intranet system to reduce WAN

traffic

79

CDN Options

Companies can choose to outsource or build their own network Outsource: setup and maintenance costs much

lower, no need for inhouse experts, providers may also have additional services such as live event management and extensive usage statistics

Own network: greater control, privacy

80

CDN Providers

Some of the largest companies include Akamai Digital Island Yahoo (mainly streaming media)

Extensive networks covering large areas Akamai has over 13000 servers in more than

60 countries

81

Tested Performance

How good are these networks? Largest companies tested for streaming live

broadcast capabilities as well as on-demand Each provider sent 1 hour MPEG-2 stream via

satellite and needed to encode at 100kbps in real-time before transmission

Yahoo achieved average packet loss rate of 0.006%

Another study found internet packet loss of > 9% for similar bandwidth and distance

However, this is upper end of results

82

Tested Performance (cont)

After September 11 many web sites flooded with traffic

Typical sites that experienced massive increases in traffic were airline and news sites

Akamai used to serve 80% of MSNBC.com’s traffic, including around 12.5 million streams of video

Akamai also used by MSNBC.com for Winter Olympics

83

Performance Issues

Many design aspects can affect performance Capabilities of infrastructure Location of equipment DNS Redirection

DNS Redirection is crucial in obtaining optimal performance, but is also one of the hardest areas to perfect

84

DNS Redirection Issues

Study found that neither Akamai nor Digital Island could redirected the client to the optimal server in their content distribution networks consistently

In a small fraction of cases performance was far from optimal

Due to difficulty in determining user’s exact location and the best server at the time

85

Summary

Using a number of mechanisms including load balancing and caching servers, content delivery networks aim to distribute internet content towards the network edge

Avoids bottlenecks involved in centralized architecture, and reduces latency between end user and content

Common uses for these networks is support for a large number of users to access popular web sites, or as a delivery means for streaming multimedia

86

CDN

Analysis

87

Hierarchical Caches and CDNS What are the implications of this study for

hierarchical caches and Content Delivery Networks (e.g., Akamai)? Demand-side proxy caches are widely deployed and

are likely to become ubiquitous. What is the marginal benefit from a supply-side CDN

cache given ubiquitous demand-side proxy caching? What effect would we expect to see in a trace

gathered at an interior cache?

CDN interior caches can be modeled as upstream caches in a hierarchy, given some simplifying assumptions.

88

Level 2

Level 1 (Root)

N2 clients N2 clients

N1 clients

An Idealized Hierarchy

Assume the trees are symmetric to simplify the math.

Ignore individual caches and solve for each level.

89

Hit Ratio at Interior Level i

CN gives us the hit ratio for a complete subtree covering population N

The hit ratio predicted at level i or at any cache in level i over R requests is given by:

11

)(1

level to requests

level at hits

ii

NNc

i

i

hr

CCRp

r

hii

i

i

“the hits for Ni (at level i) minus the hits captured by level i+1, over the miss stream from level i+1”

90

Root Hit Ratio

Predicted hit ratio for cacheable objects, observed at root of a two-level cache hierarchy (i.e. where r2=Rpc):

2

21

11

1

N

NN

C

CC

r

h

91

NL clients NL clients

N clients

Generalizing to CDNs

Request Routing Function

Interior Caches(supply side “reverse proxy”)

NI clients

ƒ(leaf, object, state)

Leaf Caches(demand side)

NL clients

Symmetry assumption: ƒ is stable and “balanced”.

ƒ

92

Hit ratio in CDN caches

Given the symmetry and balance assumptions, the cacheable hit ratio at the interior (CDN) nodes is:

L

LI

N

NN

C

CC

1

NI is the covered population at each CDN cache.NL is the population at each leaf cache.

93

Cacheable interior hit ratio

cacheable

hit

ratio

increasing NI and NL -->

fixed fanout NI /NL

Interior hit rates improve as leaf populations increase....

94

Interior hit ratioas percentage of all cacheable requests

marginal

cacheable

hit

ratio

increasing NI and NL -->

....but, the interior cache sees a declining share of traffic.

1 The Web: some jargon r Web page: m consists of “objects” m addressed by a URL r Most Web pages...

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