1 Web Traffic Characterization Zipf Web Traffic Characterization - - PDF document

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Web Caching and Content Delivery Web Caching and Content Delivery Caching for a Better Web 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, bandwidth demand, server load
• Misses increase latency (extra hops)

Clients Proxy Cache Servers

Hits Misses Misses

Internet

[Source: Geoff Voelker]

Proxy Caching Proxy Caching

How should we build caching systems for the Web?

• Seminal paper [Chankhunthod96]
• Proxy caches [Duska97]
• Akamai DNS interposition [Karger99]
• Cooperative caching [Tewari99, Fan98, Wolman99]
• Popularity distributions [Breslau99]
• Proxy filtering and transcoding [Fox et al]
• Consistency [Tewari,Cao et al]
• Replica placement for CDNs [et al]

[Voelker]

Issues for Web Caching Issues for Web Caching

• Binding clients to proxies, handling failover

Manual configuration, router-based “transparent caching”, WPAD (Web Proxy Automatic Discovery)

• Proxy may confuse/obscure interactions between

server and client.

• Consistency management

At first approximation the Web is a wide-area read-only file service...but it is much more than that. caching responses vs. caching documents deltas [Mogul+Bala/Douglis/Misha/others@research.att.com]

• Prefetching, scale, request routing, scale, performance

Web caching vs. content distribution (CDNs, e.g., Akamai)

End End-

• to

to-

• End Content Delivery

End Content Delivery

request stream Internet

hosting network request distributor surrogate caches CDN servers proxies server array + storage

upstream downstream

Proxy Cache Effectiveness Proxy Cache Effectiveness

How to measure Web cache effectiveness (goals)?

• Hit ratio
• Savings in bandwidth or server load
• Reduction in perceived user latency

What factors determine/limit effectiveness?

• Capacity?
• User population?
• Proxy placement in the network?
• Updates and invalidations?

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Web Traffic Characterization Web Traffic Characterization

Research question: how do goals and traffic behavior shape strategies for deploying and managing proxy caches?

• Replacement policy: what objects to retain in cache?

Large vs. small, relative importance of popularity and stability

• Deployment: where to place the cache?

Close to server or client?

• How many users per cache?
• Prefetching?

Since the Web is in active deployment on a large-scale, Web traffic characterization is an empirical science.

• Science of mass behavior: observe and test hypotheses.

Zipf 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/iα, for some α with 0 < α < 1”.

• Higher α gives more skew: popular objects are way popular.
• Lower α gives a more heavy-tailed distribution.
• In the Web, α ranges from 0.6 to 0.8 [Breslau/Cao99].
• With α=0.8, 0.3% of the objects get 40% of requests.

Zipf Zipf-

• like Reference Distributions

like Reference Distributions

pi ! 1/iα

Σpi = 1

Probability of access to the object with popularity rank i:

(This is equivalent to a power-law or Pareto distribution.)

alpha-0.7

such that: head tail

[Zipf 49, Duska et al. 97, Breslau et al. 98] Popularity rank

heavy tail

pi

Importance of Traffic Models Importance of Traffic Models

Analytical models like this help us to predict cache hit ratios (object hit ratio or byte hit ratio).

• E.g., get object hit ratio as a function of size by integrating under

segments of the Zipf curve

…assuming perfect LFU replacement

• Must consider update rate

Do object update rates correlate with popularity?

• Must consider object size

How does size correlate with popularity?

• Must consider proxy cache population

What is the probability of object sharing?

• Enables construction of synthetic load generators

SURGE [Barford and Crovella 99]

The “Trickle The “Trickle-

• Down Effect”

Down Effect”

clients cache to servers flood trickle

What is the effect on “downstream” traffic? What is the significance of this effect? How does it impact design choices for components “behind” the caches?

A Look at the Miss Stream A Look at the Miss Stream

synthetic trace SURGE-generated low locality: α= 0.6 log-log plot head: flattened midrange: tapers tail: intact Zipf-like

1035 816

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1998 ibm.com high locality fit Zipf α= 0.76 skewed: 77 % / 1%

Effect on Server Trace ( Effect on Server Trace (ibm ibm.com) .com) What’s Happening? (LRU) What’s Happening? (LRU)

Suppose the cache fills up in R references.

(That’s a property of the trace and the cache size.)

Then a cache miss on object with rank i occurs only if i is referenced…. probability pi …and i has not been referenced in the last R requests. probability (1 - pi)R Stack distance

P(a miss is to object i) is qi = pi(1 - pi)R

Miss Stream Probability by Popularity Miss Stream Probability by Popularity

qi: R = 104, α α α α=0.7 IBM 1998 (32 MB) Moderately popular

• bjects now dominate.

Object Hit Ratio by Popularity (1) Object Hit Ratio by Popularity (1)

synthetic α= 0.6

Object Hit Ratio by Popularity (2) Object Hit Ratio by Popularity (2)

IBM 1998

Limitations/Features of This Study Limitations/Features of This Study

static (cacheable) objects ignore misses caused by updates

• invalidation/expiration

LRU replacement vary cache effectiveness by capacity

• cache intercepts all client traffic

ignore effect on downstream traffic volume

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Proxy Deployment and Use Proxy Deployment and Use

Where to put it? How to direct user Web traffic through the proxy? Request redirection

• Much more to come on this topic…

Must the server consent?

• Protected content
• Client identity

“Transparent” caching and the end-to-end principle

• Must the client consent?

Interception Switches Interception Switches

ISP cache array The client doesn’t know. The server doesn’t know. Neither side told HTTP to disable it. Is it legal? Good thing? Bad thing?

Shouldn’t This Be Illegal? Shouldn’t This Be Illegal?

end end middle RFC 1122: The Internet Architecture (IPv4) specifies that each packet has a unique destination “host” address. Problems middle boxes may be subversive IPsec and SSL dynamic routing

Cache Effectiveness 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