From%Feast%to%Famine!!mfreed/docs/tango-trios14-slides.pdf · 48 0 1000 2000 3000 4000 5000 6000 0...

From Feast to Famine Managing mobile network resources across

environments and preferences

Rob Kiefer Erik Nordström

Michael Freedman Princeton University

Network Usage Makes Demands on Limited Resources

•  Data

•  Ba>ery

•  Performance

2

! 2GB

!

App App App App App App ! Running

tasks

•  User mobility –  indoor vs outdoor – WiFi vs LTE – mulIpath & migraIon

•  User interest –  foreground vs background apps –  InteracIve vs streaming

3

Limita<ons and Opera<ng Condi<ons Are Not Sta<c

Divergent Goals = Resource Conflicts

•  User and apps may differ – e.g., apps may prioriIze perf, user is cost sensiIve

•  User has to moderate resource usage between apps

4

Streaming Music App

5

User Goals Don’t Match App Goals

Streaming Music App

6

User Goals Don’t Match App Goals

User wants to sacrifice quality; app overuses cell network

Streaming Music App

Flow-‐level Fairness != App-‐level Fairness

7

Video App

Network Usage

Flows

Streaming Music App


8

Video App

Network Usage

Flows

StreamingMusic App


9

Video App

Network Usage

Flows

StreamingMusic App

Interac<ve Apps Lack Priori<za<on

10

Browser

Network Usage

StreamingMusic App


11

Browser

Network Usage

Streaming Music App


12

Browser

Network Usage

Interac<ve apps stuck behind long-‐running flows, hur<ng UX.

Strawman: Users micromanage across seQngs

13


14

•  Requires user to monitor and esImate usage over potenIally long epochs (hours, days)

•  Restricts typically all-‐or-‐nothing, e.g., allow on mobile or not


15

Poor reuse in similar apps


16

Varying levels of control

Few

Many



17

Updates can add, remove, or change seangs without user knowing

Varying levels of control


•  OS limits background acIvity

•  Only certain classes of apps

•  Used by iOS and WinPhone

•  User has no choice

18

Strawman: OS-‐level limita<ons and restric<ons

Streaming Music App

✔ OK!

Random App XYZ

✖ NOPE!

•  Usage preferences through programmaIc policy; user is priority, but app flexibility

•  Address conflicts proac0vely

•  MigraIon / mulIpath in mind

•  Handle dynamic operaIng condiIons

19

Our Solu<on: Tango

20

Tango Overview

Kernel Measure Control

Controller

Gather plans

Execute

Apps

Plans

User Policy

State

21

Tango Overview


Controller

Gather plans

Execute

Apps

Plans

User Policy

State

State – metrics from kernel sources Plans – acIons to be taken Policy – generates plans from given state

22

Tango Overview


Controller

Gather plans

Execute

Apps

Plans

User Policy

State

1. Controller

2. User Policy

3. App Policies

23

Tango Overview


Controller

Gather plans

Execute

Apps

Plans

User Policy

State

1. Controller

2. User Policy

3. App Policies

Tango: Controller

•  Measures device state

•  Generates policy constraints

•  Carries out policy plans –  Verify against constraints –  Perform ac<ons in plan

24


Controller

Gather plans

Execute

Apps

Plans

User Policy

State

Tango: Controller

•  Measures device state

•  Executes the user policy

•  Carries out policy plans –  Verify against constraints –  Perform ac<ons in plan

25


Controller

Gather plans

Execute

User Policy

State

Apps

Plans

Tango: Policies

26


Controller

Gather plans

Execute

User Policy

State

•  Programs

•  Turn state into plans

•  Two levels: user & app

Apps

Plans

Tango: Policies

27


Controller

Gather plans

Execute

Apps

Plans

User Policy

State

•  Programs

•  Turn state into plans

•  Two levels: user & app

•  User level – Global management of network resources – Sets resource constraints for app policies – Default plan for (classes of) apps

28

Policies: Turning State into Ac<ons

29

Policies: Turning State into Ac<ons net_name: eth0 net_type: CELL, ba>_charge: false ba>_perc: 50

flows: 10, 11, 12… …

30


flows: 10, 11, 12… …

Source Info

Transport Layer # retrans., RTTs, cong. window…

Network Layer (IP) addresses, rouIng rules…

Link Layer type, signal quality, bit errors…

Ba>ery charging status, percent…

State

31


flows: 10, 11, 12… …

Policy

State Source Info





32


flows: 10, 11, 12… …

Policy

MIGRATE(10, eth0), MIGRATE(11, eth0), MIGRATE(12, eth0), …, RATELIMIT(eth0, 500kbps)

State Source Info





33

Policies: Turning State into Ac<ons

Ac<on Iface Flow

ENABLE ✔ ✔

RATELIMIT ✔ ✔

LOG ✔ ✔

MANAGE ✔

MIGRATE ✔

State

Ac<ons

net_name: eth0 net_type: CELL, ba>_charge: false ba>_perc: 50

flows: 10, 11, 12… …

Policy

MIGRATE(10, eth0), MIGRATE(11, eth0), MIGRATE(12, eth0), …, RATELIMIT(eth0, 500kbps)

Source Info






Controller

Gather plans

Execute State

•  Leverage local state

•  Only act on their flows

•  Subject to user policy constraints

•  App can provide hints

34

Tango: App Policies

Apps

Plans

User Policy

•  ProacIvely address resource conflicts – Constraints known a priori – Simplifies controller & app policy interacIon cycle

•  Apps can hint at desired resource needs for future rounds

35

Tango: Constraints & Hints

evaluate(s, c): // Plan returned.

genConstraints(s): c = new Constraints()

for a in s.apps():

if a.isForeground():

c.put(a, HIGH)

return c

36

USER POLICY

Tango: User & App Policy



for a in s.apps():


c.put(a, HIGH)

return c

37

evaluate(s, ca): plan = new Plan() pstate = GetPlayerState()

// Rest of policy omitted.

plan.hintPriority = NORM

urgent = false

buffer = pstate.bufferTime

if buffer > 30:

urgent = false

elif buffer < 20 || urgent:

urgent = true

plan.hintPriority = HIGH

return plan

USER POLICY APP POLICY




for a in s.apps():

hint = a.hintPriority


c.put(a, HIGH)

elif Allowed(a, hint):

c.put(a, hint)

return c

38

evaluate(s, ca): plan = new Plan() pstate = GetPlayerState()

// Rest of policy omitted.

plan.hintPriority = NORM

urgent = false

buffer = pstate.bufferTime

if buffer > 30:

urgent = false

elif buffer < 20 || urgent:

urgent = true

plan.hintPriority = HIGH

return plan

USER POLICY APP POLICY


•  Controller, sample policies, sample apps in Java for Android phones

•  Flow migraIon provided by Serval (NSDI ‘12)

•  Explored the policy space for single and mulIple apps

39

Prototype

•  Campus WiFi offers chance to offload

•  But, even seamless switching w/ migraIon has problems

•  How to use policy to improve when to switch?

•  How to minimize cell usage?

•  How can app policies help?

40

Evalua<on: Streaming Music Across SpoXy Campus WiFi

•  Campus WiFi offers chance to offload

•  But, even seamless switching w/ migraIon has problems

•  How to use policy to improve when to switch?

•  How to minimize cell usage?

•  How can app policies help?

41

Evalua<on: Streaming Music Across SpoXy Campus WiFi

42

Clinging to WiFi Leads to Poor Performance

Good WiFi Bad WiFi

43

0 100 200 300 400 500

0 100 200 300 400 500 600 700

Buffe

r (se

cs)

Time (secs)

WiFiCell


Good WiFi Bad WiFi

44

0 100 200 300 400 500

0 100 200 300 400 500 600 700

Buffe

r (se

cs)

Time (secs)

WiFiCell


Unneeded cell usage

Good WiFi Bad WiFi

45

0 100 200 300 400 500

0 100 200 300 400 500 600 700

Buffe

r (se

cs)

Time (secs)

WiFiCell

0

100

200

300

0 100 200 300 400 500 600 700

Buffe

r (se

cs)

Time (secs)

Pause

WiFiCell


Good WiFi Bad WiFi

•  More aggressive in leaving WiFi; more conservaIve in joining

•  Use heurisIcs on signal strength trends

•  Based on measurements across campus of signal strength vs achieved throughput

46

Improving Network Switching

47

0 1000 2000 3000 4000 5000 6000

0 100 200 300 400 500 600 700 800

Buffe

r (se

cs)

Time (secs)

WiFiCell

Improving Streaming Music Through Policy

No rate limit

48

0 1000 2000 3000 4000 5000 6000

0 100 200 300 400 500 600 700 800

Buffe

r (se

cs)

Time (secs)

WiFiCell

0 100 200 300 400 500 600 700 800

0 100 200 300 400 500 600 700 800

Buffe

r (se

cs)

Time (secs)

WiFiCell


No rate limit Rate limit

49

0 100 200 300 400 500 600 700

0 100 200 300 400 500 600 700 800

Buffe

r (se

cs)

Time (secs)

WiFiCell

0 1000 2000 3000 4000 5000 6000

0 100 200 300 400 500 600 700 800

Buffe

r (se

cs)

Time (secs)

WiFiCell

0 100 200 300 400 500 600 700 800

0 100 200 300 400 500 600 700 800

Buffe

r (se

cs)

Time (secs)

WiFiCell


No rate limit Rate limit

App Policy

50

0 50

100 150 200 250

Unl Rate App

Dat

a (M

B)

Policy

WiFiCell

Total

User and App Policy Coopera<on Yields Best Usage

Rate uses 5-‐6x less cell data than Unl. App uses about 30x less cell data than Unl.

51

0

2

4

6

8

0 20 40 60 80 100 120 0

10

20

30

40

50Pa

ge lo

ad ti

me

(s)

Play

back

buf

fer (

s)

Time (secs)

Music appWeb app

Interac<ve App Compe<ng w/ Streaming App

Problem Poor resource isolaIon leads to erraIc load Imes Solu<on App fair share, prioriIze music only when buffer low

52

0

2

4

6

8

0 20 40 60 80 100 120 0

10

20

30

40

50Pa

ge lo

ad ti

me

(s)

Play

back

buf

fer (

s)

Time (secs)

Music appWeb app


Problem Poor resource isolaIon leads to erraIc load Imes Solu<on App fair share, prioriIze music only when buffer low

53

0

2

4

6

8

0 20 40 60 80 100 120 0

10

20

30

40

50Pa

ge lo

ad ti

me

(s)

Play

back

buf

fer (

s)

Time (secs)

Music appWeb app

0

2

4

6

8

0 50 100 150 200 250 300 350 0

10

20

30

40

50

Page

load

tim

e (s

)

Play

back

buf

fer (

s)

Time (secs)

Music appWeb app


•  Context awareness: CASS, CARISMA, JCAF – Using context to improve apps – Not looking at resource usage; not single device

•  Network Choice/Wifi Offloading – Many: Whiffler, IMP, BreadCrumbs, SALSA… –  Complementary to Tango

•  Serval, MPTCP, TCP-‐Migrate –  Need policy to guide decisions on nets to use –  Complementary to Tango

54

Related Work


•  Network Choice/WiFi Offloading – Many: Whiffler, IMP, BreadCrumbs, SALSA… –  Complementary to Tango


55

Related Work


•  Network Choice/WiFi Offloading – Many: Whiffler, IMP, BreadCrumbs, SALSA… –  Complementary to Tango


56

Related Work

•  Tango is a network resource management framework based on programmaIc policy

•  ProacIvely handle resource conflicts while including user and app prefs into decisions

•  Demonstrated the value it adds for single apps as well as across apps

57

Conclusions

Thanks! Ques<ons?

58

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