Brett Higgins

Balancing Interactive Performance and Budgeted Resources

in Mobile Applications

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The Most Precious Computational Resource

“The most precious resource in a computer system is no longer its processor, memory, disk, or network, but rather human attention.”

– Gollum Garlan et al.(12 years ago)

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User attention vs. energy/data

3 GB$$$

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Balancing these tradeoffs is difficult!

• Mobile applications must:• Select the right network for the right task• Understand complex interactions between:

• Type of network technology• Bandwidth/latency• Timing/scheduling of network activity• Performance• Energy/data resource usage

• Cope with uncertainty in predictions

Opportunity:systemsupport

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Spending Principles

• Spend resources effectively.

• Live within your means.

• Use it or lose it.

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Thesis Statement

By:• Providing abstractions to simplify multinetworking,• Tailoring network use to application needs, and• Spending resources to purchase reductions in delay,

Mobile systems can help applications significantly improve user-visible performancewithout exhausting limited energy & data resources.

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Roadmap

• Introduction• Application-aware multinetworking• Intentional Networking

• Purchasing performance with limited resources• Informed Mobile Prefetching

• Coping with cloudy predictions• Meatballs

• Conclusion

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Diversity of networks

Diversity of behavior

Email• Fetch messages

The Challenge

YouTube•Upload video

Match traffic to available networks

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Current approaches: two extremes

All details hidden All details exposed

Result: mismatched Result: hard for traffic applications

Please insert

packets

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Solution: Intentional Networking

• System measures available networks

• Applications describe their traffic

• System matches traffic to networks

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Abstraction: Multi-socket

• Multi-socket: virtual connection• Analogue: task scheduler• Measures performance of each alternative• Encapsulates transient network failure

Client Server

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Abstraction: Label

• Qualitative description of network traffic• Interactivity: foreground vs. background• Analogue: task priority

Client Server

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Evaluation Results: Email

• Vehicular network trace - Ypsilanti, MI

0

10

20

30

40

50

60

70

On-demand fetch time

Best band-widthBest latency

Aggregate

Intentional Networking

Tim

e (s

econ

ds)

0

50

100

150

200

250

300

350

400BG fetch time

Tim

e (s

econ

ds)

3%

7x

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Intentional Networking: Summary

• Multinetworking state of the art• All details hidden: far from optimal• All details exposed: far too complex

• Solution: application-aware system support• Small amount of information from application• Significant reduction in user-visible delay• Only minimal background throughput overhead

• Can build additional services atop this

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Roadmap

• Introduction• Application-aware multinetworking• Intentional Networking

• Purchasing performance with limited resources• Informed Mobile Prefetching

• Coping with cloudy predictions• Meatballs

• Conclusion

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Mobile networks can be slow

$#&*!!

No need for profanity,

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Data fetching is slowUser: angry

Fetch time hiddenUser: happy

With prefetchingWithout prefetching

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Mobile prefetching is complex

• Lots of challenges to overcome• How do I balance performance, energy, and cellular data?• Should I prefetch now or later?• Am I prefetching data that the user actually wants?• Does my prefetching interfere with interactive traffic?• How do I use cellular networks efficiently?

• But the potential benefits are large!

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Who should deal with the complexity?

Users? Developers?

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What apps end up doing

The Data MiserThe Data Hog

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Informed Mobile Prefetching

• Prefetching as a system service• Handles complexity on behalf of users/apps• Apps specify what and how to prefetch• System decides when to prefetch

Application

IMP

prefetch()

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Informed Mobile Prefetching

• Tackles the challenges of mobile prefetching• Balances multiple resources via cost-benefit analysis• Estimates future cost, decides whether to defer• Tracks accuracy of prefetch hints• Keeps prefetching from interfering with interactive traffic• Considers batching prefetches on cellular networks

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Balancing multiple resources

CostBenefit

Performance Energy Cellulardata

3 GB$$$

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Balancing multiple resources

• Performance (user time saved)• Future demand fetch time• Network bandwidth/latency

• Battery energy (spend or save)• Energy spent sending/receiving data• Network bandwidth/latency• Wireless radio power models (powertutor.org)

• Cellular data (spend or save)• Monthly allotment• Straightforward to track

3 GB$$$

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Weighing benefit and cost

• IMP maintains exchange rates• One value for each resource• Expresses importance of resource

• Combine costs in common currency• Meaningful comparison to benefit

• Adjust over time via feedback• Goal-directed adaptation

Joules Bytes

Seconds Seconds

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Users don’t always want what apps ask for

• Some messages may not be read• Low-priority• Spam

• Should consider the accuracy of hints• Don’t require the app to specify it• Just learn it through the API

• App tells IMP when it uses data• (or decides not to use the data)

• IMP tracks accuracy over time

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Evaluation Results: EmailTi

me

(sec

onds

)

Average email fetch time500

400

300

200

100

0

8

7

6

5

4

3

2

1

0

Energy usage

Ener

gy (J

)

3G d

ata

(MB)

3G data usage5

4

3

2

1

0

Budgetmarker

~300ms

2-8x

Less energy than all others

(including WiFi-only!)

2x

Only WiFi-only used less 3G data(but…)

IMP meets all resource goalsOptimal

(100% hits)

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IMP: Summary

• Mobile prefetching is a complex decision• Applications choose simple, suboptimal strategies• Powerful mechanism: purchase performance w/ resources

• Prefetching as a system service• Application provides needed information• System manages tradeoffs (spending vs. performance)• Significant reduction in user-visible delay• Meets specified resource budgets

• What about other performance/resource tradeoffs?

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Roadmap

• Introduction• Application-aware multinetworking• Purchasing performance with limited resources• Coping with cloudy predictions• Motivation• Uncertainty-aware decision-making (Meatballs)

• Decision methods• Reevaluation from new information

• Evaluation

• Conclusion

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Mobile Apps & Prediction

• Mobile apps rely on predictions of:• Networks (bandwidth, latency, availability)• Computation time

• These predictions are often wrong• Networks are highly variable• Load changes quickly

• Wrong prediction causes user-visible delay• But mobile apps treat them as oracles!

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Example: code offload

99.9%

0.1%

Response time

10 sec100 sec

Likelihood of 100 sec response time0.1% 0.0001%

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Example: code offload

Elapsed timeExpected response time

Expected response time (redundant)

0 sec10.09 sec10.00009 sec

99.9%

0.1%

Response time

10 sec100 sec

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Example: code offload

Elapsed timeExpected response time

Expected response time (redundant)

9 sec1.09 sec1.00909 sec

99.9%

0.1%

Response time

10 sec100 sec

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Example: code offload

Elapsed timeExpected response time

Expected response time (redundant)

11 sec89 sec10.09 sec

99.9%

0.1%

Response time

10 sec100 sec

Conditionalexpectation

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Spending for a good cause

• It’s okay to spend extra resources!• …if we think it will benefit the user.

• Spend resources to cope with uncertainty• …via redundant operation

• Quantify uncertainty, use it to make decisions• Benefit (time saved)• Cost (energy + data)

✖

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Meatballs

• A library for uncertainty-aware decision-making• Application specifies its strategies• Different means of accomplishing a single task• Functions to estimate time, energy, cellular data usage

• Application provides predictions, measurements• Allows library to capture error & quantify uncertainty

• Library helps application choose the best strategy• Hides complexity of decision mechanism• Balances cost & benefit of redundancy

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Roadmap

• Introduction• Application-aware multinetworking• Purchasing performance with limited resources• Coping with cloudy predictions• Motivation• Uncertainty-aware decision-making (Meatballs)

• Decision methods• Reevaluation from new information

• Evaluation

• Conclusion

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Deciding to operate redundantly

• Benefit of redundancy: time savings• Cost of redundancy: additional resources

• Benefit and cost are expectations• Consider predictions as distributions, not spot values

• Approaches:• Empirical error tracking• Error bounds• Bayesian estimation

Empirical error tracking

• Compute error upon new measurement• Weighted sum over joint error distribution• For multiple networks:

• Time: min across all networks• Cost: sum across all networks

ε(BP2)

.5 .6 .7 .8 .9 1 1.1 1.2 1.3 1.4

ε(BP1)

.5 .6 .7 .8 .9 1 1.1 1.2 1.3 1.4

40

observedpredicted

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error =

Error bounds

• Range + p(next value is in range)• Student’s-t prediction interval

• Calculate bound on time savings of redundancy

9876543210

BP1 BP2

Band

wid

th (M

bps)

Network bandwidth

9876543210

T1 T2

Tim

e (s

econ

ds)

Time to send 10Mb

Max time savingsfrom redundancy

Predictor says: Use network 2

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Error bounds

• Calculate bound on net gain of redundancy max(benefit) – min(cost) = max(net gain)

• Use redundancy if max(net gain) > 0

9876543210

T1 T2

Tim

e (s

econ

ds)

Time to send 10Mb

Max time savingsfrom redundancy

Predictor says: Use network 2

9876543210

BP1 BP2

Band

wid

th (M

bps)

Network bandwidth

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Bayesian Estimation

• Basic idea:• Given a prior belief about the world,• and some new evidence,• update our beliefs to account for the evidence,

• AKA obtaining posterior distribution

• using the likelihood of the evidence

• Via Bayes’ Theorem: posterior = likelihood * prior p(evidence) Normalization factor;

ensures posterior sums to 1

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Bayesian Estimation

• Example: “will it rain tomorrow?”• Prior: historical rain frequency• Evidence: weather forecast (simple yes/no)• Posterior: believed rain probability given forecast• Likelihood:

• When it will rain, how often has the forecast agreed?• When it won’t rain, how often has the forecast agreed?

• Via Bayes’ Theorem: posterior = likelihood * prior p(evidence) Normalization factor;

ensures posterior sums to 1

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Bayesian Estimation

• Applied to Intentional Networking:• Prior: bandwidth measurements• Evidence: bandwidth prediction + implied decision• Posterior: new belief about bandwidths• Likelihood:

• When network 1 wins, how often has the prediction agreed?• When network 2 wins, how often has the prediction agreed?

• Via Bayes’ Theorem: posterior = likelihood * prior p(evidence) Normalization factor;

ensures posterior sums to 1

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Decision methods: summary

• Empirical error tracking• Captures error distribution accurately• Computationally intensive

• Error bounds• Computationally cheap• Prone to overestimating uncertainty

• Bayesian• Computationally cheap(er than brute-force)• Appears more reliant on history

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Reevaluation: conditional distributions

99.9%

0.1%

Response time

10 sec100 sec

Decision

Elapsed Time

One server Two servers

0 10 …

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Evaluation: methodology

• Network trace replay, energy & data measurement• Same as IMP

• Metric: weighted cost function• time + cenergy * energy + cdata * data

Low-cost Mid-cost High-cost

cenergy 0.00001 0.0001 0.001

Energy per 1 second delay reduction

100 J 10 J 1 J

Battery life reduction under average use (normally 20 hours)

6 min 36 sec 3.6 sec

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Evaluation: IntNW, walking traceW

eigh

ted

cost

(nor

m.)

No cost Low cost Mid cost High cost0

0.20.40.60.8

11.21.41.61.8

2

2x

24%

Low-resource strategies improve

Meatballs matches the best strategy

Error boundsleans towardsredundancy

Simple Meatballs

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Evaluation: PocketSphinx, server load

No cost Low cost Mid cost High cost0

0.2

0.4

0.6

0.8

1

1.2

1.4

23%

Meatballs matches the best strategy

Simple Meatballs

Error boundsleans towardsredundancy

Wei

ghte

d co

st (n

orm

.)

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Meatballs: Summary

• Uncertainty in predictions causes user-visible delay• Impact of uncertainty is significant• Considering uncertainty can improve performance

• Library can help applications consider uncertainty• Small amount of information from application• Library hides complex decision process• Redundant operation mitigates uncertainty’s impact• Sufficient resources are commonly available

• Spend resources to purchase delay reduction

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Conclusion

• Human attention is the most precious resource• Tradeoffs between performance, energy, cellular data• Difficult for applications to get right• Necessitates system support – proper abstractions

• We provide abstractions for:• Using multiple networks effectively• Budgeted resource spending• Hedging against uncertainty with redundancy

• Overall results• Improved performance without overspending resources

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Future work

• Intentional Networking• Automatic label inference• Avoid modifying the server side (generic proxies)

• Predictor uncertainty• Better handling of idle periods

• Increase uncertainty estimate as measurements age• Add periodic active measurements?

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Abstraction: Atomicity & Ordering Constraints

• App specifies boundaries, partial ordering• Receiving end enforces delivery atomicity, order

ServerChunk 1

Chunk 3

Chunk 2use_data()

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Evaluation Results: Vehicular Sensing

• Trace #2: Ypsilanti, MI

0

1

2

3

4

5

6

7

Urgent update time

Best band-width

Best latency

Aggregate

Intentional Networking

Tim

e (s

econ

ds)

0

1

2

3

4

5

6

7

8BG throughput

Thro

ughp

ut (K

B/se

c)

48%

6%

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Evaluation Results: Vehicular Sensing

• Trace #2: Ypsilanti, MI

0

1

2

3

4

5

6

7

Urgent update timeBest band-width

Best latency

Aggregate

Intentional Networking

Intentional Networking, two-process

Tim

e (s

econ

ds)

0

1

2

3

4

5

6

7

8

BG throughput

Thro

ughp

ut (K

B/se

c)

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IMP Interface

• Data fetching is app-specific• App passes a fetch callback• Receives a handle for data

• App provides needed info• When prefetch is desired• When demand fetch occurs• When data is no longer wanted

Application

IMP

prefetch()

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How to estimate the future?

• Current cost: straightforward• Future cost: trickier• Not just predicting network conditions• When will the user request the data?• Simplify: use average network conditions

• Average bandwidth/latency of each network• Average availability of WiFi

• Future benefit• Same method as future cost

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Balancing multiple resources

• Cost/benefit analysis• How much value can the resources buy?• Used in disk prefetching (TIP; SOSP ‘95)

• Prefetch benefit: user time saved• Prefetch cost: energy and cellular data spent• Prefetch if benefit > cost• How to meaningfully weigh benefit and cost?

Error bounds

• How to calculate bounds?• Chebyshev’s inequality?

• Simple, used before; provides loose bounds• Bounds turned out to be too loose

• Confidence interval?• Bounds true value of underlying distribution• Quantities (e.g., bandwidth) neither known nor fixed

• Prediction interval• Range containing the next value (with some probability)• Student’s-t distribution, alpha=0.05

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How to decide which network(s) to use?

• First, compute the time on the “best” network T1 = Σ s/B1 * posterior(B1| BP1 > BP2)

• Next, compute the time using multiple networks Tall = Σ min(s/B1, s/B2) * posterior(B1, B2 | BP1 > BP2)

• Finally, do the same with resource costs.• If cost < benefit, use redundancy

Tall = Σ min(s/B1, s/B2) * posterior(B1, B2 | BP1 > BP2) L(BP1 > BP2|B1, B2) * prior(B1, B2)

p(BP1 > BP2)

Here is where each component comes from.

= Σ s/max(B1, B2) *

1 2 3 4 5 6 7 8 9 10

1 2 3 4 5 6 7 8 9 10

B1 = 1

B2 = 4

B1 = 8

B1 = 9

B2 = 5 B2 = 6

likelihood(BP1 > BP2|B1, B2)

p(BP1 > BP2) p(BP1 < BP2)

prior(B1)

prior(B2)

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The weight of history

• A measurement’s age decreases its usefulness• Uncertainty may have increased or decreased• Meatballs gives greater weight to new observations• Brute-force, error bounds: decay weight on old samples• Bayesian: decay weight in prior distributions

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Reevaluating redundancy decisions

• Confidence in decision changes with new information• Suppose we decided not to transmit redundantly• Lack of response is new information

• Consider conditional distributions of predictions• Meatballs provides interface for:• Deferred re-evaluation• “Tipping point” calculation: when will decision change?

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Applications

• Intentional Networking• Bandwidth, latency of multiple networks• Energy consumption of network usage• Reevaluation (network delay/change)

• Speech recognition (PocketSphinx)• Local/remote execution time• Local CPU energy model• For remote execution: network effects• Reevaluation (network delay/change)

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Evaluation: IntNW, driving trace

No cost Low cost Mid cost High cost0

0.5

1

1.5

2

Not much benefitfrom using WiFi

Simple Meatballs

Wei

ghte

d co

st (n

orm

.)

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Evaluation: PocketSphinx, walking trace

No cost Low cost Mid cost High cost0

0.2

0.4

0.6

0.8

1

1.2

1.4

Benefit of redundancy persists more

23-35%

>2x

Meatballs matches the best strategy

Simple Meatballs

Wei

ghte

d co

st (n

orm

.)