Optimal Sensing-Transmission Structure for Dynamic Spectrum ...

fishecologistMobile - Wireless

Dec 12, 2013 (3 years and 8 months ago)

70 views

Eric Jung, Yichuan Wang, Iuri Prilepov, Frank Maker,

Xin Liu, & Venkatesh Akella

University of California, Davis

eajung@ucdavis.edu


User
-
Profile
-
Driven Collaborative
Bandwidth Sharing for Mobile Phones

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1

Outline


Introduction


Resource Aware Collaborative Execution (RACE)


RACE Modeling and Policies


Evaluation


Conclusion

2

Mobile Apps are Cloud Apps

3

Social Networking

Location

Synchronization

Network Overloading


Smartphones have changed the game for providers


Mobile data usage will double annually through 2014 (Cisco)



Driven by smartphone ascent


for AT&T, 3% of smart
-
phone
customers take up 40% of data usage(WSJ)


Smartphones were ~ 15% of device sales in 2009, 41% in 2013
(Telecom Industry Association)


Expected long
-
term solutions


New Infrastructure Rollout (LTE, WiMAX, Femto)


Tiered servicing models

4

New Problems for Providers/Users


Phones
-

battery life performance, crowded
spectrum


Service providers
-

Network loads


5

Outline


Introduction


Resource Aware Collaborative Execution
(RACE)


RACE Modeling and Policies


Evaluation


Conclusion

6

Resource
Aware Collaborative
Execution
(RACE
)


A relay scheme


Phones act as data relay nodes to
augment network connectivity




7

Benefits of RACE


Network performance


Improve network coverage


Possibly offload data traffic onto femtocell or non
-
cellular networks


Energy efficiency


Energy for WiFi is significantly less than 3G/EDGE


Users with heavy usage profiles can leverage resources of
less constrained users

8

User
-
Profile
-
Driven Management


Bandwidth Sharing/Tethering


Microsoft


COMBINE


UMich
-

TCP
-
level Inverse Multiplexing (PRISM)


UCSB/Microsoft
-

Cool
-
Tether


What’s new?


User Protection


Dynamic and User
-
Profile
-
Driven Decisions



9

Design Issues


User Protection


Helpers reserve resources for their own use


Reject requests if it endangers future activity


In the current work, voice is considered primary phone
activity to be protected


Dynamic User
-
Profile
-
Driven Decisions


Decision based on
state of phone
(Battery, Signal
Strength, Queue)


User Profile
used as input to decision process

10

User Profiles


can people afford to help?


Normalized histogram of 53
-
day call history for 3 users


User profiles are
widely varying


User 3 likely has significant extra energy over time

11

Outline


Introduction


Resource Aware Collaborative Execution (RACE)


RACE Modeling and Policies


Evaluation


Conclusion

12

System Modeling


1 requester, 2 helpers assumed


System state consists of


Time to recharge


16 hour discharge time assumed


Battery levels


Signal Strength


Download queue


Actions: Self
-
serve, request, serve/reject request


Rewards: successful call minutes, downloads,
service


13

RACE Decision Policy

14

RACE Formulation


Policy 1


Altruistic RACE


Central server with global knowledge of all phone states makes
collaborative execution decisions


Policy 2


RACE with helper protection


Helper phones with self protection


Policy 3
-

Decentralized Heuristic


Heuristic policy based on “energy
-
threshold”


Note: 1 requester, 2 helpers assumed


15

Policy 1
-
Altruistic RACE


Cloud Server determines
decision for ALL phones


Objective to maximize
the global reward


“Altruistic” : helper
phones may sacrifice
their protection for
greater global
performance

16

Policy 2
-
RACE with Helper Protection

17


Cloud Server controls
requester decisions


Helper phone has its
own MDP


Reward for helping, call
minutes


Rewards determine
protection for call time


Helper MDP calculated
on phone

Energy Threshold


Predicts energy required to handle all future voice activity with
certain probability


Calculated from call history


Can be calculated on phones

18

19

Heavy Energy Threshold

Light Call Profile

Light Energy Threshold

Heavy Call Profile

Policy 3
-
Energy Threshold Heuristic

20


Decentralized


Simple Heuristic Policy


Requester:


Self
-
Serve if


Energy>threshold



Helper:


Serve request if


Energy>threshold

Outline


Introduction


Resource Aware Collaborative Execution (RACE)


RACE Modeling and Policies


Evaluation


Conclusion

21

Evaluation


Power measurement


Simulations


Real call traces


Controlled data traffic

22

Power Measurement Setup


Power measured through DC power supply,
PyVISA Python Package

23

Power Profiling


Power discharge downloading 1MB file

24

EDGE: ~55 J

WiFi
: ~5.3J


Ad
-
Hoc
WiFi

Connection Setup: ~6.5J


For requester, potential reduction in energy of almost 10x for 1MB

Energy Transitions

25

Legend

e
c
: call cost (1 min)

e
oi
: WiFi wakeup

e
f
/e
g
: forwarding/
receiving cost (WiFi)

e
dl
: download cost
(1MB file)

Simulations


Constant download arrival probability
p
d


Simulate over multiple download arrival probabilities


Constant download size of 1MB


User 1 is requester, user 2 & 3 are helpers


Simulated over 10 days for each phone


Metrics


Average throughput


Downloads served


Average phone lifetime


26

User Profiles


User 1 (requester) has heavy profile: will likely make requests


User 2 (helper) has moderate profile: may or may not accept
request


User 3 (helper) has sparse profile: should have extra energy to
serve

27

Throughput, Number Served


Throughput higher for any
RACE type policy than
without


Policy 1 achieves highest
throughput


Energy threshold achieves
lowest out of RACE


Phone 3 (light user) serves
much more than phone 2


28

Phone Lifetime (16 hour max)


Well protected helper: lasts (close to) 16 hrs


Policy 1 helpers: lower lifetimes because of global reward


Helper phones > 920 min for all energy
-
threshold policies


Tradeoff: Protection and Requester Throughput

29

Conclusion


RACE exploits smart phone technology, user diversity to
improve energy efficiency/network connectivity


RACE is dynamic decision process based on energy costs,
user profiles, system states


3 policy types: centralized, helper protected, heuristic


Centralized, helper
-
protected formed as MDP


Heuristic is decentralized, based on energy threshold concept


Policy trends:


MDP policies favor throughput over helper phone protection


Heuristic protects better with lower throughput to requester

30

Future Work


Network
-
side improvement


Amount of data offloaded


Study energy/bit reductions


Incentive possibilities


Incentive


Use social networking sites to implement incentive structure


More extensive profiling, thresholding improvements

31

eajung@ucdavis.edu

32

Cloud Services


Cloud services can be used to mitigate many
overhead issues


Locating Peers


BrightKite, Loopt


Service provider
-
enabled solutions


Incentive


Social Networking Sites/Groups for Participation


Service Provider Tracker/Incentives


Policy Determination


Policies for sharing can be calculated in the cloud server

33

Incentives


Service provider oriented


Better connectivity/coverage


Higher data rate


Extend coverage of WiFi/femto cells


Social network oriented


Car
-
pool group


Social groups


Current practice


Several Phones already with WiFi hotspot capability