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“ The Peregrine Falcon is renowned for its speed, reaching over 322 km/h (200 mph) during its characteristic hunting stoop (high speed dive), making it the fastest member of the animal kingdom. ”. falcon. F ast A pp L aunching for Mobile Devices Using Predictive User Con text .

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f ast a pp l aunching for mobile devices using predictive user con text

“The Peregrine Falcon is renowned for its speed, reaching over 322 km/h (200 mph) during its characteristic hunting stoop (high speed dive), making it the fastest member of the animal kingdom.”

falcon

Fast App Launching for Mobile Devices Using Predictive User Context

Tingxin Yan University of Massachusetts Amherst

David Chu Microsoft Research

Deepak Ganesan University of Massachusetts Amherst

Aman Kansal Microsoft Research

Jie Liu Microsoft Research

MobiSys 2012

Low Wood Bay, Lake District, UK

mobile apps
Mobile Apps…

Loading Slowly

Top Apps in Marketplace

Average loading time > 12s

measured on Samsung Focus Windows Phone

slow network content fetching
Slow Network Content Fetching

Email

News

Social

Loading time > 10s in 3G

measured on iPhone 3GS

app prelaunch with context
App Prelaunch with Context

What is Prelaunch?

Prelaunch: Schedule an app to run before the user launches it

Challenges and Approaches

inference

optimization

systems

Which app to prelaunch?

At what cost and benefit?

Requiring what systems support?

Context clues indicate predictable app usage patterns

Data insights shed light on informative context (3)

Cost: energy, memory

Benefit: latency reduction

Problem formulation efficiently provides

optimal solution

FALCON’s scheduling and memory management complement kernel’s

Windows Phone OS mod prototype implementation

trigger context
Trigger Context

Session

Time

Email

News

Game

Trigger Context: given a trigger app, identify most likely follower apps

Trigger

Followers

[interpretation]

Interruptions lead us to habitual routines

most popular triggers overall

most popular triggers for Angry Birds

location context
Location Context

Location Context: given a location (cluster id), identify most likely apps

Twitter

Game

Calendar

Email

Facebook

Email

Twitter

Cal

[interpretation]

What we are doing depends on Where we are

Twitter

Twitter

Twitter

Email

Facebook

Cal

Game

Social Apps

@ Outdoors

Productivity Apps @ Work

burst context
Burst Context

Burst window

2010

2011

Days

Apps exhibit bursts of popularity

Burst Context: detect and predict whether an app is bursting

How long are bursts?

[interpretation]

We get bored of stuff

decision engine step 1 likelihood estimation
Decision Engine, Step 1: Likelihood Estimation

Context:

Trigger, Location, Burst

better

+burst

App Likelihood Estimation: Conditional probability on <Trigger, Location, Burst>

+location

trigger

baseline

Likelihood Estimates used as ranking metric…

decision engine step 2 cost benefit analysis
Decision Engine, Step 2: Cost-Benefit Analysis

Likelihood Estimates

Potential Benefit: Latency reduction

Potential Cost:

Energy wasted

Optimization Problem

  • Maximize Latency Reduction
  • Subject to energy budget (e.g. 2%)
  • Knapsack Problem
  • Top-K Greedy Approximation
runtime example
Runtime Example

Memory

AngryBirds

WordsW

Friends

Phone

SMS

Email

Weather

Trigger

Prelaunched Followers

Decision

Engine

Facebook

Energy Budget

falcon architecture prototype
FALCON Architecture & Prototype

FALCON UI

  • Inference
  • Cost-Benefit Analysis
  • Context
  • Trigger
  • Location
  • Burst

App1

App2

AppN

User

Kernel

FALCON

components

shown

Launch Predictor

Context Source Manager

Feature Extractors

Decision Engine

Dispatcher

Geo Coords

Apps Used

Model Trainer

Date/Time

Proc Tracker

Kernel Memory Manager (not FALCON)

Windows Phone 7.5 OS mod

2MB main memory; negligible processing & energy overhead

Server-based Model Trainer

see paper for details

evaluation falcon vs lru
Evaluation: FALCON vs. LRU

Reduced from 9s to 6s

Results for Games & Entertainment apps

similar results for all apps

evaluation time since last update
Evaluation: Time-Since-Last-Update

Reduced from 30min to 3min

Polling (30 mins)

FALCON

FALCON outperforms polling most of the time

No Prefetching

Email Time-Since-Last-Update (minutes)

evaluation cost benefit analysis
Evaluation: Cost-Benefit Analysis

6s latency reduction for 2% energy budget

conclusion
Conclusion

FALCON: Fast App Launching with Context

Which app to prelaunch?

At what cost and benefit?

Requiring what systems support?

Context clues indicate predictable app usage patterns

Data insights shed light on informative context

Cost: energy, memory

Benefit: latency reduction

Problem formulation efficiently provides

optimal solution

FALCON’s scheduling and memory management complement kernel’s

Windows Phone OS mod prototype implementation

inference

optimization

systems

Insightful Context: Trigger, Location, Burst

Cost-Benefit Decision Engine

(see paper)

evaluation micro benchmarks
Evaluation: Micro-benchmarks

Popular & 1st Party Apps

cost: energy expenditure if predictions always wrong

benefit: latency reduction if predictions always right

context acquisition cost
Context Acquisition Cost
  • App History is free
    • Trigger Context
    • Burst Context
  • Location can be cheap
    • Coarse-grained location is cheap (base station id, Wi-Fi AP id)
    • Location Service cache amortizes fine-grained location cost across apps