Informed Mobile Prefetching T.J. Giuli † Christopher Peplin † David Watson †‡ Brett Higgins Jason Flinn Brian Noble †‡

Informed Mobile Prefetching

T.J. Giuli†

Christopher Peplin†

David Watson†‡

Brett HigginsJason Flinn Brian Noble

† ‡

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Prefetching + Mobile Networks

Time, distance

$#&*!!

Phone activity

Read article

Phone activity

Slow fetch

PrefetchArticle loads instantly

Sleeping

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A Tale of Two Apps

The Data Hog The Data Miser

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A Tale of Two Apps

• Four hours later:• Battery died hours ago• Fetch time: INF

• Four hours later:• Battery: 10%• Fetch time: many seconds

Scenario:50% battery

4 hours until recharge

We must conserve energy!(…no big surprise there.)

✖

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A Tale of Two Apps

• One hour later:• Battery: 5% (charging)• Fetch time: << 1 second

• One hour later:• Battery: 40% (charging)• Fetch time: many seconds

Scenario:50% battery

1 hour until recharge

It’s okay to spend resources……if it will benefit the user.

(And if we can spare the resources.)

✖

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One more scenario: Reality

• The only scenario that matters• Network conditions change rapidly• User/app behavior changes too

• No one strategy is always best• Even for two scenarios

• Mobile prefetching is complex.• Must consider:

• Network availability/quality• Network energy cost model• Overall resource spending• Time until resource replenishment• Observed usefulness of prefetching…

• But the benefits could be great!

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So, who should deal with it?• Users?

• Monitor resources, networks• Adjust behavior• Fiddle with app settings

• Developers?• Monitor resources, networks• Observe user behavior• Adjust prefetch strategy

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Informed Mobile Prefetching

• System support for prefetching on mobile networks• Applications specify hints• What & how to prefetch

• System decides when to prefetch• Monitors network availability and quality• Tracks resource spending over time• Adjusts the importance of energy/data conservation• Tracks how often the user consumes hinted data

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Roadmap

• Motivation• IMP Design & Implementation• Interface• Prefetch decision algorithm

• Applications & Evaluation• Summary

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IMP Interface

• App passes a Fetcher• Callback to fetch data• Size of data item• Optional prefetch class

• IMP returns a Future• Handle for future access• get() invokes app fetch

• Intentional Networking• Simple use of multiple networks• Prioritize FG traffic over

prefetches

Application

IMP

Intentional Networking

prefetch()get()

fetch()

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How to decide when to prefetch?

• Core: cost/benefit analysis• Inspired by TIP (SOSP ‘95)

• Benefit: time savings• Cost: energy, cellular data• Prefetch when:• Benefit > cost

• How to calculate?• How to compare?

HintsIMP

BenefitCost

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• Network measurements• Bandwidth, RTT, availability

• Benefit = future fetch time• Cost = energy + data• PowerTutor power model

(CODES+ISSS ‘10)• (Current cost) – (future cost)

• Future depends on user• How often is data used?

HintsIMP

BenefitCost

Observations:Bandwidth

RTTPower state

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• API lets us track accuracy• Fraction of hinted data used• “Used” = get()• “Not used” = cancel()

• Use accuracy in calculations• e.g. benefit = time × accuracy

• Per-class accuracy estimate• If app specifies classes

HintsIMP

BenefitCost


RTTPower state

Accuracy of prefetch

hints

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• Budgeted resources• Fixed, replenishable capacity• Time period to spend

• Approach: Price is Right™• Spend close to budget• But not over budget!

• Goal-directed adaptation• Odyssey (TOCS ‘04)• Measure resource spending• Adjust resource importance

HintsIMP

BenefitCost

UserTime

Energy,Data


RTTPower state


hints

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• Re-evaluation• Outcome may change later

• Network conditions change• Resource spending slows

• Periodically re-run analysis

• Batching• Can amortize tail energy cost• Evaluate all batch sizes• Prefetch when benefit > cost

• For any batch size

HintsIMP

BenefitCost

UserTime

Energy,Data


RTTPower state


hints

New batch,or wait a bit

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Roadmap

• Motivation• IMP Design & Implementation• Interface• Prefetch decision algorithm

• Applications & Evaluation• Summary

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Applications

• K9 email client• Popular Android email client• Supported transparently via IMP-aware IMAP proxy• Default: prefetch emails over 32KB

• OpenIntents News Reader• Open-source Android Atom/RSS feed reader• Added IMP support code• Default: no article text/media prefetching (summary only)

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Evaluation: Methodology

• Gathered network trace in a moving vehicle• Sprint 3G & open WiFi• BW up/down, RTT

• Replayed in lab (trace map

here)

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Evaluation: Comparison Strategies

• Never prefetch• Prefetch items under a size threshold• App-specific threshold value

• Prefetch only over WiFi• Always prefetch

• Common heuristics; simple to implement

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Evaluation Results: Email

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Evaluation Results: Email

• All resource goals met• Much smaller fetch time• Within 300ms of “always”• 2-8x less than others

• Walking trace on campus• Similar results

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Evaluation Results: News

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• All resource goals met• Smaller fetch time

• Whenever other strategies meet the goals

• Walking trace on campus• Similar results*

* …with one exception.

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• Effect of prefetch classes• Lower average fetch time• Meet goals more reliably

• Small developer effort• Significant improvement

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Summary

• Informed Mobile Prefetching• App supplies prefetch hints• System decides when to prefetch• Manages budgeted resources

• “Price is Right” approach

• Tracks prefetch accuracy• Avoids wasteful prefetching

• Meets all resource goals• Improves average fetch time in most cases

• Compared to simple strategies that meet same goals

Informed Mobile Prefetching T.J. Giuli † Christopher Peplin † David Watson †‡ Brett Higgins Jason Flinn Brian Noble †‡

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