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Web Caching on Smartphones: Ideal vs. Reality . Feng Qian 1 , Kee Shen Quah 1 , Junxian Huang 1 , Jeffrey Erman 2 Alexandre Gerber 2 , Z . Morley Mao 1 , Subhabrata Sen 2 , Oliver Spatscheck 2 1 University of Michigan 2 AT&T Labs - Research. June 27 2012.

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web caching on smartphones ideal vs reality

Web Caching on Smartphones: Ideal vs. Reality

Feng Qian1, KeeShen Quah1, Junxian Huang1, Jeffrey Erman2

AlexandreGerber2, Z. Morley Mao1, Subhabrata Sen2, Oliver Spatscheck2

1University of Michigan 2AT&T Labs - Research

June 27 2012

mobile traffic an explosive growth
Mobile Traffic: An Explosive Growth
  • Deployment of cellular infrastructures: much slower
    • Spectrum shortage and economic issue
    • The cellular infrastructure spending in 2011 was expected to be only a 6.7% rise over 2010

1600% increase

Source: Cisco Visual Networking Index (VNI) Global Mobile Data Traffic Forecast, 2011-2016

web caching on cellular devices
Web Caching on Cellular Devices
  • The big picture: traffic redundancy elimination
  • The first network-wide study of redundant transfers caused by inefficient HTTP caching on cellular devices
    • HTTP: The dominant app-layer protocol for ~20 years
    • Caching: Huge benefits, but complex
    • Caching on cellular devices:Reduces redundant data transferred over the RANImproves performance due to reduced latencyCuts cellular bills for customers
background caching in http 1 1
Background: Caching in HTTP 1.1
  • Use Expiration and Revalidation to ensure caching consistency
  • Before expiration: the client should safely assume the freshness of the cached file
  • After expiration: the client must send a revalidation message to the server to query the freshness of the cache entry

Well known protocol for 20 years

What is the state-of-the-art in the context of cellular devices?

Last-Modified: Feb 1 2012 15:00:00

Expires: Feb 10 2012 15:00:00

?

If-Modified-Since:

Feb 1 2012 15:00:00

304 Not Modified

Last-Modified: Feb 12 2012 15:00:00

Expires: Feb 15 2012 15:00:00

Last-Modified: Feb 1 15:00:00

Expires: Feb 10 15:00:00

Last-Modified: Feb 12 15:00:00

Expires: Feb 15 15:00:00

measurement goal
Measurement Goal
  • Goal: understand the state-of-the-art inHTTP caching on cellular devices
  • What to study: redundant transfers caused by inefficient HTTP caching
  • Potential cause: HTTP implementation Related
    • Caching logic (client/server) not following HTTP spec
    • Limited cache size
    • Non-persistent cache
  • Potential cause: application semantics related
    • Server conservatively sets headers to make files uncacheable or expire too soon

They account for 20% of the total HTTP traffic volume!

measurement data
Measurement Data

User interface for the data

collector/uploader software

methodology
Methodology
  • A simulator strictly follows HTTP/1.1 caching logic (RFC 2616)
    • Expiration and freshness calculation mechanism
    • Non-cacheable objects
    • Partial caching due to byte-range requests and broken connection
    • LRU cache replacement algorithm, and more …
  • Feed each user’s HTTP transactions to the cache simulator
  • Redundant transfers are accurately identified in the simulation process
  • HTTP caching is not simple: 2K C++ LoC even for the simulation core
cacheability and redundancy
Cacheability and Redundancy
  • File cacheability: for both datasets
    • Most bytes (70% to 78%) and most files (66% to 72%) are cacheable.
  • Traffic Redundancy (assuming unlimited cache size)
  • Root causes of redundant transfers (within all HTTP traffic)

Under-estimation

due to HTTPS and app-semantic-related redundancy

Client

Issue

Server

Issue

limited cache size and non persistent cache
Limited Cache Size andNon-persistent cache
  • Which factor has the main responsibility for redundancy?
    • Problematic caching logic
    • Limited cached size: cache size ∞ 4MB, HTTP traffic savings 17%13%
    • Non-persistent cache: 59% of consecutive cache hits < 1 min
  • How large the cache size needs to be?
    • A cache of 50 MBachieves 90% of the gain (w.r.t. traffic reduction) compared to an unlimited cache

It is unlikely that the handset is rebooted during such a short interval.

The benefits are significant even for a small cache.

Dist. of intervals between consecutive cache hits on the same entry (ISP trace)

quantifying the resource impact of redundant traffic
Quantifying the ResourceImpact of Redundant Traffic

Compute the impact:ΔE = (E0 – ER) / E0

  • In cellular networks, we also care about cellular resources
  • Use our trace-driven RRC state machine simulator with a handset radio power model[Qianetal, Mobisys 11]
    • Applied to only cellular traffic within UMICH dataset
  • Three important metrics characterizing cellular resource consumption:
    • D: radio resource consumption
    • S: signaling load
    • E: handset radio energy consumption

ΔE: Radio energy impact of redundant transfers (a positive value)

ER: Radio energy consumption in modified traces with redundant transfers removed

E0: Radio energy consumption

in original traces

quantifying the resource impact of redundant traffic1
Quantifying the ResourceImpact of Redundant Traffic
  • When redundant and other traffic coexist, only eliminating redundant traffic may not reduce resource consumption
    • As long as one of the concurrent transfers exists, the radio is on (i.e., consuming resources)
  • Non-HTTP traffic plays a role (push notification and chatting)
    • Traffic volume: small (1%); resource impact: high (18%)
    • Resource release is controlled by fixed inactivity timers
    • Sending small data incurs high resource overhead
testing http libraries and browsers
Testing HTTP Libraries and Browsers
  • Verify measurement findings by testing popular HTTP libraries and browsers on real handsets
  • Design 13 controlled tests to cover all important aspects of caching implementation
  • Revisit: which factor has the main responsibility for redundancy?
    • Problematic caching logic
    • Limited cached size
    • Non-persistent cache
testing http libraries and browsers1
Testing HTTP Libraries and Browsers
  • Basic caching test
    • Handset requests for a small cacheable file f
    • Server transfers f with a proper Expires directive.
    • Client requests for f again before it expires.
    • PASSiff the 2nd request not incurring any network traffic
  • Cache size test: perform binary search
  • Cache replacement policy test: try popular algorithms (LRU, LFU, FIFO)
  • See paper for all 13 tests
test results
Test Results
  • Implementation issues of caching
  • 4 out of 8 libraries do not support caching at all.
  • For both browsers, when loading the same URL back-to-back, the second request is treated as a full reload from the remote server
  • Android browser uses a small cache of 8MB
  • Partial caching is not supported
  • Some do not properly handle Pragma:no-cacheor Cache-Control:no-cache.

A huge gap between protocol specification and implementation, leading to significant redundancy of network traffic.

summary
Summary
  • The first network-wide studyof cellular HTTP caching
  • Redundant transfers are prevalent
    • 18% (ISP) and 20% (UMICH) of HTTP traffic volume
    • 17% of overall traffic volume (UMICH)
    • 6%~9% of cellular resource consumption (UMICH)
    • The root cause: problematic caching logic on handsets
    • Validated by caching tests of popular libraries and browsers
diversity among applications
Diversity Among Applications
  • Identifying smartphone applications
    • ISP: by user-agent fields in HTTP requests
    • UMICH: by the captured packet-process correspondence
  • Diversity among top apps
    • HTTP redundancy ratios range from 0.0% to 100.0%
  • Validate apps with high redundancy ratios (> 90%)
    • Analyze locally collected tcpdumptraces
    • They do not cache HTTP responses
  • Some apps have negligible redundant transfers
    • Almost all bytes are not cacheablee.g., all requests are HTTP POST instead of HTTP GET
the cache simulator simplified version
The Cache Simulator (Simplified Version)
  • The simulation algorithm:
  • Performs fine-grained caching simulation at a per-user basis
  • Assigns to each HTTP transaction a label indicating its caching status.
  • Red labels correspond to duplicated transfers.

Duplicated transfer: the file has not changed after the cache entry expires, but the handset does not perform cache revalidation.

Cache miss.

The file contains "Cache-Control: no-store“. It cannot be cached.

Duplicated transfer: A request is issued before the file expires.

Duplicated transfer: the file has not changed after the cache entry expires, but the server does not recognize the cache revalidation.

The file has changed after the cache entry expires.

The file has not changed after the cache entry expires, and a cache revalidation is properly performed.

background radio resource management in cellular networks
Background: Radio Resource Management in Cellular Networks
  • RRC (Radio Resource Control) state machine [3GPP TS 25.331]
    • State promotions have promotion delay
    • State demotions incur tail times

Tail Time

Delay: 1.5s

Delay: 2s

Tail Time

UMTS RRC State Machine for a large US 3G carrier

background radio resource management in cellular networks1
Background: Radio Resource Management in Cellular Networks

Promo

Delay

2 Sec

DCH

Tail

5 sec

FACH

Tail

12 sec

Tail Time

Waiting inactivity timers to expire