Mobile Data Offloading: A Tutorial

Transcription

Mobile Data Offloading: A Tutorial
Jianwei Huang
Network Communications and Economics Lab (NCEL)
Department of Information Engineering
The Chinese University of Hong Kong (CUHK)
Jianwei Huang (CUHK)
Mobile Data Offloading (Tutorial)
June 2014
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Global Mobile Data Traffic, 2013 to 2018
Global Mobile Data Traffic
Overall mobile data traffic is expected to grow to 15.9 exabytes per month by 2018, nearly an 11-fold increase over
2013. Mobile data traffic will grow at a CAGR of 61 percent from 2013 to 2018 (Figure 1).
Figure 1.
Cisco Forecasts 15.9 Exabytes per Month of Mobile Data Traffic by 2018
Global Mobile Data Traffic Growth Projection (source: Cisco VNI Mobile 2014)
The Asia Pacific and North America regions will account for almost two-thirds of global mobile traffic by 2018,
as shown in Figure 2. Middle East and Africa will experience the highest CAGR of 70 percent, increasing 14-fold
over the forecast period. Central and Eastern Europe will have the second highest CAGR of 68 percent, increasing
Annual growth rate
∼over61%
13-fold
the forecast period. The emerging market regions of Asia Pacific and Latin America will have CAGRs
I
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of 67 percent and 66 percent respectively.
Expected to reach 15.9 exabytes per month by 2018
A 11-fold increase over 2013
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Cellular Mobile Figure
Network
Capacity
3: Historical Increases
in Spectral Efficiency
16
Historical Increases in Spectral Efficiency (source: Femtoforum)
If available spectrum is increasing at 8% per year and the number of cell sites is
increasing at 7% per year and technology performance is improving at 12% per year
then operators can expect their network capacities to increase – on average – at 29%
per year (1.08 x 1.07 x 1.12). If network capacity is growing at 29% per year and demand
is growing currently
at 108%band
per year,
then there 8%
is a significant
gap, which begs for
I Available
spectrum
growth:
per year
further innovation.
Annual grow rate ∼ 36%
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I
Cell site increase: 7% per year
What other options
exist? One
possibility <
is architectural
What if –
the2013)
Spectrum
efficiency
growth:
18% perinnovation.
year (2007
definition of a “cell site” were radically changed, in such a way that the number of
“sites” dramatically increased and the cost per unit of capacity (after adjusting for the
108%sites)
· 107%
· 118%
= 136%
inevitable lower utilisation of smaller
significantly
decreased?
Similar innovation
has occurred before in the cellular industry. Decades ago omni-directional sites were
sectorised. Operators began adding “down tilt” to their urban site designs. Operators
began introducing underlay and overlay sites.
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Background
Widening Supply-Demand Gap
Network capacity growth vs Data traffic growth
29% vs 66%
36%
vs.
61%
Slow network
capacity growth vs. Fast data traffic
growth
Network Capacity Data Traffic
Lin Gao (NCEL, IE@CUHK)
Mobile Data Offloading
May 2012 1/1
June 2014
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How to Narrow the Gap: “Hard” Approaches
Expanding the network capacity through technology innovations
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Acquiring new spectrum bands
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More efficient interference management through cooperations
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Developing high-frequency wireless technology
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Upgrading access technology (e.g., WCDMA → LTE → LTE-A)
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Building more pico/micro/macro cell sites
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...
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How to Narrow the Gap: “Hard” Approaches
Expanding the network capacity through technology innovations
I
Acquiring new spectrum bands
I
More efficient interference management through cooperations
I
Developing high-frequency wireless technology
I
Upgrading access technology (e.g., WCDMA → LTE → LTE-A)
I
Building more pico/micro/macro cell sites
I
...
Challenges: need to be cost effective and easy to deploy.
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How to Narrow the Gap: “Soft” Approaches
Reshaping the demand through economics and software
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Tired data pricing
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Capped or throttling (e.g., 128kbps if monthly usage >5GB)
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Time/Location/Congestion dependent pricing (e.g., delay coupons)
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Application specific optimization (e.g., network-friendly implem.)
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On device software client (e.g., “fuel gauge” meters)
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Content specific control (e.g., two-sided 1-800 pricing)
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...
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How to Narrow the Gap: “Soft” Approaches
Reshaping the demand through economics and software
I
Tired data pricing
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Capped or throttling (e.g., 128kbps if monthly usage >5GB)
I
Time/Location/Congestion dependent pricing (e.g., delay coupons)
I
Application specific optimization (e.g., network-friendly implem.)
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On device software client (e.g., “fuel gauge” meters)
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Content specific control (e.g., two-sided 1-800 pricing)
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...
Challenges: need to be user-friendly and network neutral.
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Today’s Focus: Mobile Data Offloading
Basic idea: deliver cellular traffic over Wi-Fi or Femtocell.
BS2
MU13
BS1
AP1
MU11
MU21
AP2
MU24
AP4
MU14
MU32
AP3
BS3 MU33
MU31
MU11 & MU21 →AP1 , MU24 →AP2 , MU31 & MU33 →AP3 , MU14 & MU32 →AP4 .
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A Reality Check
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Global mobile data traffic would grow at a CAGR of 65 percent instead of 61 percent. Offload volume is determined
by smartphone penetration, dual-mode share of handsets, percentage of home-based mobile Internet use, and
Global Mobile Data Offloading
percentage of dual-mode smartphone owners with Wi-Fi fixed Internet access at home.
Figure 14.
52 Percent of Total Mobile Data Traffic Will Be Offloaded by 2018
Mobile Traffic Offloading Prediction (source: Cisco VNI Mobile 2014)
The amount of traffic offloaded from smartphones will be 51 percent by 2018, and the amount of traffic offloaded
from tablets will be 69 percent by 2018.
Mobile offloading will increase from 45% in 2013 to 52% in 2018
A supporting trend is the growth of cellular connectivity for devices such as tablets which in their earlier generation
were limited to Wi-Fi connectivity only. With increased desire for mobility and mobile carriers offer of data plans
catering to multi-device owners, we find that the cellular connectivity is on a rise albeit cautiously as the end users
are testing the waters. As a point in case, we estimate that by 2018, 42 percent of all tablets will have a cellular
in 2013
(Figure
15).
Jianwei Huangconnection
(CUHK)up from 34 percent
Mobile
Data
Offloading
(Tutorial)
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Offloading Increases with Technology
Figure 16.
Mobile Data Traffic and Offload Traffic, 2018
Mobile and Offloaded Traffic from Mobile-Connected Devices (source: Cisco VNI Mobile 2014)
Trend 6: Comparing Mobile Network Speeds
Globally, the average mobile network connection speed in 2013 was 1,387 Kbps. The average speed will grow at
a compound
annualattract
growth rate of
13 percent, and willdevices.
exceed 2.5 Mbps by 2018. Smartphone speeds, generally
4G networks
will
high-usage
third-generation (3G) and higher, are currently almost three times higher than the overall average. Smartphone
The offloading
ratio
4Greaching
will 7be
speeds will nearly
double on
by 2018,
Mbps.the highest.
There is anecdotal evidence to support the idea that usage increases when speed increases, although there is
often a delay between the increase in speed and the increased usage, which can range from a few months to
several years. The Cisco VNI Forecast relates application bit rates to the average speeds in each country. Many
Jianwei Huangof the
(CUHK)
Mobile
Data can
Offloading
June
2014rates for
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trends in the resulting traffic
forecast
be seen (Tutorial)
in the speed forecast, such as the high
growth
Complementary Cellular Small Cells and Wi-Fi
Cellular small cells provide a uniform and reliable capacity layer and
better coverage.
WiFi provides a more powerful capacity boost.
Technology co-location reduces Capex and Opex for offloading.
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Case Study: AT&T
2.7B
1.23B
382.1M
85.5M
2009
2010
2011
2012
AT&T Annual Wi-Fi Connections (source: AT&T)
Statistics of 2012
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32, 000 Wi-Fi hotspots
2.7 billion Wi-Fi connections (80% from AT&T’s mobiles)
12.9 billion MB Wi-Fi data (5.2 billon MB from mobile devices)
Seamless offloading
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Auto-login of AT&T Wi-Fi hotspots
Auto-roaming to Fon Wi-Fi hotspots supported by Hotspot 2.0
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AT&T Small Cell Strategy
AT&T Small Cell Strategies (source: AT&, Senza Fili)
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Case Study: China Mobile
China Mobile Cellular and WiFi Traffic (source: China Mobile, Senza Fili)
4.2 millions Wi-Fi APs deployed in 2012
Reach 6 millions in the next three years
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Nanocell Architecture
China Mobile NanoCell Strategy
Access to Cellular Core network and WLAN Core Network
MME
HSS
LTE
Nanocell
Backhaul
Nanocell
GW
P-GW
S-GW
Internet
WLAN
WLAN
Portal
LTE and WLAN
Inference
Coordination
EPC
Nanocell
OAM
Nanocell
SoC Chip
Architecture
Flexible
Backhaul
AC
AAA
Trusted Secured
Environment
Unified
Authentication
(source: China Mobile)
6
Combine LTE femtocell and carrier-grade Wi-Fi in the same box
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Our Focus on Wi-Fi
White paper Carrier Wi-Fi® for mobile operators
fic for
en
ey do
ccess
scriber
om,
orks,
f Wi-Fi.
e less
hey rely
Wi-Fi
fload –
scriber
Figure 3. Cellular and Wi-Fi traffic from mobile devices.
FourSenza
Types
Source:
Fili of Wi-Fi (source: Senza Fili)
coffee
highly
ective
uch
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Cost Benefit of Wi-Fi
White paper Carrier Wi-Fi® for mobile operators
ct
he
MHz
The
ed
our
ue
sts
h is
, it
Per-bit TCO of different technology choices (source: Senza Fili)
er
Hz
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technology of choice.
Operator
Benefits
The Wi-Fi offload business opportunity map for mobile carriers
Defensive:
Cut costs
Seamless & automatic:
Value of everyday convenience
International roaming:
Lifestyle & lower costs
+
Opportunistic:
Boost revenues
Relieve congestion
Lower network CAPEX/OPEX
Retain customers
Reduce churn
Additional services
Attract new customers
Mobile operator’s business opportunities due to Wi-Fi offload (source: Aptilo)
NEW MASS-MARKET WI-FI OFFLOAD SERVICE TYPES
service provisioning point of view
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User Benefits
Faster connections
Lower battery drain (when close to AP)
Easier to use
Reduced TCP handshake
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Different Offloading Types
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Two Types of Data Offloading
User-initiated offloading
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User decides when and how to offload
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When automatic offloading is not possible or users’ judgements needed
Network-initiated offloading
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Mobile operator makes the offloading decision
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Seamless Wi-Fi
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Seamless Wi-Fi
Near-term: user transparence
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Automatic handover from cellular to Wi-Fi
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Automatic authentication by Wi-Fi
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Traffic reroute to local Internet
Long-term: carrier-grade Wi-Fi
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Large bandwidth and high throughout based on latest Wi-Fi standards
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Tight integration with cellular network through new standards
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Traffic reroute to cellular operator’s core network
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Cellular operator has control over quality and service experiences
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Goes beyond data offload
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Challenges of Wi-Fi Data Offloading
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Challenges of Wi-Fi Data Offloading
Maturity of Wi-Fi-cellular integrations
Pricing of cellular and Wi-Fi services
Quality of Wi-Fi experiences
Deployment of Wi-Fi hotspots
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Challenge 1: Maturity of Wi-Fi-Cellular Integrations
Manual Wi-Fi network selection and input of username/password
Tedious, time-consuming, and inconvenient
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Solutions
Operator-specific on-device configurations (AT&T)
Standards: HotSpot 2.0 (IEEE 802.11u), NGH, ANDSF in 3GPP
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Automatic Wi-Fi selection and login with strong security
Already supported in Apple iOS 7
Wi-Fi Sessions
the network of a commercially
network. Measurements show
of Wi-Fi sessions in the course
sessions grows steeply.
Increase
of SIM-based Wi-Fi Offloading (source: Aptilo)
3
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Challenge 2: Pricing of Cellular and Wi-Fi Services
Is Wi-Fi free? Flat-fee? Usage-based?
How is cellular charged?
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Several possibilities
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Combined cellular and Wi-Fi service with a total monthly data cap
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Volume-capped cellular service with unlimited free Wi-Fi access
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Low cost Wi-Fi data service for customers without a cellular data plan
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Session-based or subscription-based Wi-Fi service only
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Several possibilities
I
Combined cellular and Wi-Fi service with a total monthly data cap
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Volume-capped cellular service with unlimited free Wi-Fi access
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Low cost Wi-Fi data service for customers without a cellular data plan
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Session-based or subscription-based Wi-Fi service only
Key Questions: how should the cellular operator
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Jointly design cellular and Wi-Fi pricing plans?
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Balance additional revenue and offloading benefits of Wi-Fi?
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Challenge 3: Quality of Wi-Fi Experiences
Not all Wi-Fi hotspots are created equal
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802.11b (11 Mbps) vs. 802.11n (150 Mbps)
Neither are the cellular networks
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3G (EDGE Evolution, 1.6 Mbps) vs. 4G (HSPA/LTE, 300 Mbps)
Half of North American mobile connections on HSPA/LTE (2013)
Real world data rates vary based on time and location.
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Cellular can be more predicable or even sometimes faster than Wi-Fi
Also need to consider delay, consistency of delay, etc.
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Challenge 3: Quality of Wi-Fi Experiences
Not all Wi-Fi hotspots are created equal
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802.11b (11 Mbps) vs. 802.11n (150 Mbps)
Neither are the cellular networks
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3G (EDGE Evolution, 1.6 Mbps) vs. 4G (HSPA/LTE, 300 Mbps)
Half of North American mobile connections on HSPA/LTE (2013)
Real world data rates vary based on time and location.
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Cellular can be more predicable or even sometimes faster than Wi-Fi
Also need to consider delay, consistency of delay, etc.
Key Question: When and how to offload traffic to Wi-Fi considering
network conditions and application QoS requirements?
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Challenge 4: Wi-Fi Hotspots Develoyment
Historically mobile operators do not own large Wi-Fi hotspots
Approach 1: Direct deployment
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AT&T (in US), China Mobile, PCCW
Pros: Easier to control and integrate
Cons: Costly and time consuming to deploy, difficulty in finding
deployment locations, providing backhaul, and managing multiple
service provisions.
Approach 2: Collaborations with Wi-Fi operators
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AT&T and Boingo, T-Mobile and iPass, DT/BT and FON
Pros: Fast and flexible
Cons: Complicated to manage integration and revenue sharing
Approach 3: Dynamic network sharing and expansion
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Challenge 4: Wi-Fi Hotspots Develoyment
Historically mobile operators do not own large Wi-Fi hotspots
Approach 1: Direct deployment
I
I
I
AT&T (in US), China Mobile, PCCW
Pros: Easier to control and integrate
Cons: Costly and time consuming to deploy, difficulty in finding
deployment locations, providing backhaul, and managing multiple
service provisions.
Approach 2: Collaborations with Wi-Fi operators
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I
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AT&T and Boingo, T-Mobile and iPass, DT/BT and FON
Pros: Fast and flexible
Cons: Complicated to manage integration and revenue sharing
Approach 3: Dynamic network sharing and expansion
Key Question: How multiple cellular operators interact in a
competitive offloading market?
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Recent Results
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Recent Results
Technology issues:
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Delayed-aware offloading
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Congestion-aware offloading
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Predictive offloading
Economics issues:
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Operator bargaining
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Offloading market
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User-centric offloading and unloading
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Technology Issues for Wi-Fi Offloading
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Delayed Wi-Fi Offloading
Delay-tolerant applications (e.g., movie download and software
update): Tolerate certain delays without sacrificing user satisfactions.
Delayed Wi-Fi offloading: If a data transfer can be delayed until the
user enters a Wi-Fi hotspot, substantial gain can be achieved.
Exploiting human mobility and traffic diversity
Save network cost and device energy
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How Much Can Wi-Fi Offload?
A recent study in Korean (LLYRC, “Mobile Data Offloading: How
Much Can WiFi Deliver?” ToN’13)
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On-the-spot offloading without delay
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Save 65% of network capacity
Save 55% of energy
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Save 55% of energy
Offload with 100 secs delay (insignificant)
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Save additional 2-3% of network capacity
Save additional 3% energy
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Save 55% of energy
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Offload with 1 hour delay (significant)
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Save additional 29% of network capacity
Suitable scenarios: software update, large file transfer, ...
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Save 55% of energy
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Offload with 1 hour delay (significant)
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Save additional 29% of network capacity
Suitable scenarios: software update, large file transfer, ...
Key Question: How to optimize the delayed-based offloading?
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Delay Optimal WiFi Offloading
Joint work with Man Hon Cheung (CUHK)
IEEE WiOpt 2013
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System Model
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A total of L = {1, . . . , L} locations
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Cellular is available at all locations
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System Model
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Wi-Fi availability is location-dependent:
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L(1) = {4, 11, 13, 16}, L(0) = L\L(1) .
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System Model
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L(1) = {4, 11, 13, 16}, L(0) = L\L(1) .
Mobility pattern: User moves from location l to l 0 with prob p(l 0 | l).
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System Model
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L(1) = {4, 11, 13, 16}, L(0) = L\L(1) .
Mobility pattern: User moves from location l to l 0 with prob p(l 0 | l).
Deadline: A file of K bits must be sent by time T .
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Tradeoff
Objective: To achieve a good tradeoff between
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Reducing cellular usage: Wait till entering WiFi hotspots.
I
Satisfying user’s QoS requirement: Transmit through cellular now if not
meeting WiFi soon.
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Tradeoff
Objective: To achieve a good tradeoff between
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Reducing cellular usage: Wait till entering WiFi hotspots.
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Satisfying user’s QoS requirement: Transmit through cellular now if not
meeting WiFi soon.
Question: Given the user’s mobility pattern and the WiFi availability,
should the user remain idle, use cellular, or use Wi-Fi (if available) in
each time slot?
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Markov Decision Process
Decision epochs: t ∈ T = {1, . . . , T }.
State: s = (k, l)
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k: remaining file size
l: location index
Action: a
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a = 0 (idle), 1 (cellular), 2 (Wi-Fi).
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a ∈ A(l) = {0, 1},
I
if l ∈ L(0) (Wi-Fi is not available).
a ∈ A(l) = {0, 1, 2}, if l ∈ L(1) (Wi-Fi is available).
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Cellular usage cost at time t ∈ T
(
1, if a = 1 (using cellular),
ct (k, l, a) =
0, otherwise.
Penalty for incomplete file transfer at T + 1:
ĉT +1 (k, l) = h(k).
I
Nondecreasing in k with h(0) = 0.
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State transition probability: p (k 0 , l 0 ) | (k, l), a
p (k 0 , l 0 ) | (k, l), a = p(l 0 | l) p k 0 | (k, l), a ,
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Probability
(
0
p k | (k, l), a =
I
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1, if k 0 = [k−µ(l, a)]+ and a ∈ A(l) ,
0, otherwise.
µ(l, a): Data rate at location l with action a.
p(l 0 | l): Obtain based on the mobility pattern of the MU.
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Optimization problem: To minimize the expected total cellular
usage plus the penalty for incomplete file transfer
" T
#
X
π
π
π
π
min Es
ct (st , δt (st )) + ĉT +1 (sT +1 ) .
π∈Π
I
t=1
Policy π = (δt (k, l), ∀ k ∈ K, l ∈ L, t ∈ T ): Decision rules at all the
states and time slots.
We solve the problem using finite-horizon dynamic programming.
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Optimal Algorithm
We propose an optimal delayed Wi-Fi offloading algorithm.
It does not have closed-form in general.
Difficult to derive engineering insights.
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Special Case: Single Threshold Optimal Policy
Assume
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h(k) is a convex and nondecreasing,
The cellular and Wi-Fi data rates are location independent.
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Assume
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Theorem
The optimal policy π ∗ has a single location-dependent threshold in k:
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Assume
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Theorem
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At a location without WiFi: transmit using cellular if k ≥ kt∗ (l), otherwise
remain idle .
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Assume
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Theorem
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remain idle .
At a location with WiFi:
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Assume
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Theorem
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remain idle .
F
If Wi-Fi rate is smaller than cellular rate: transmit using cellular if k ≥ kt∗ (l),
otherwise transmit using WiFi.
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Assume
I
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Theorem
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remain idle .
F
If Wi-Fi rate is smaller than cellular rate: transmit using cellular if k ≥ kt∗ (l),
otherwise transmit using WiFi.
F
If Wi-Fi rate is larger than cellular rate: always transmit using WiFi.
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General: Multi-threshold Optimal Policy
20
20
18
18
Remaining File Size k (Mbits)
Remaining File Size k (Mbits)
Step penalty and location-dependent data rates
16
14
12
10
8
6
4
2
0
16
14
12
10
8
6
4
2
2
4
6
8
10
12
14
16
18
20
0
2
4
6
Time slot t
8
10
12
14
16
18
20
Time slot t
Idle (◦), cellular (•), Wi-Fi (+).
Left: Location without Wi-Fi: µ(l, 1) = 2.1 Mbps.
Right: Location with Wi-Fi: µ(l, 1) = 3.1 Mbps > µ(l, 2) = 2.1 Mbps.
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Performance Evaluations
Compare three schemes:
1
ODWO: Our proposed Optimal delayed Wi-Fi offloading.
2
On-the-spot offloading: Offload to the Wi-Fi network whenever Wi-Fi
is available.
3
Wiffler: prediction-based offloading [Balasubramanian MobiSys’10].
Setting:
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Cellular and Wi-Fi data rate: random with mean = 3 Mbps and
standard deviation = 1 Mbps.
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Probability that Wi-Fi is available = 0.7.
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Setting: Deadline T = 3 min, penalty h(k) = k 2 , ∀ k ∈ K.
120
2.5
ODWO
Wiffler
On−the−spot
On−the−spot
Wiffler
ODWO
File Transfer Efficiency
100
Total Cost
80
60
40
1.5
1
0.5
20
0
20
2
25
30
35
40
45
50
55
60
0
20
File Size K (Mbytes)
30
40
50
60
70
File Size K (Mbytes)
Define
File transfer efficiency =
probability of completing file transfer
average number of cellular time slots used
ODWO achieves the minimal cost and the highest file transfer efficiency.
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Summary
Algorithm design: General and monotone ODWO.
Analysis: Threshold optimal policy with convex penalty function and
location-independent data rates.
Performance evaluation: ODWO achieves the minimal total cost and
the highest file transfer efficiency.
Next Step: What about multiple users making decisions?
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Congestion-Aware
Network Selection and Data Offloading
Joint work with Man Hon Cheung and Richard Southwell (CUHK)
CISS 2014
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A Much More General Model
Multiple users
Wi-Fi availability:
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Location-dependent: Limited Wi-Fi coverage.
User-dependent: Different subscriptions and plans (e.g., Skype Wi-Fi).
Time-dependent: open or closed access mode at different time.
Network-dependent switching time and switching cost:
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Switching time: Delay during handoff.
Switching cost: Additional power consumption and QoS disruption.
Usage-based pricing
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System Model
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Users I = {1, . . . , I }, networks N = {1, . . . , N}.
Locations L = {1, . . . , L}, time slots T = {1, . . . , T }.
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System Model
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Users I = {1, . . . , I }, networks N = {1, . . . , N}.
Locations L = {1, . . . , L}, time slots T = {1, . . . , T }.
Cellular network is always available.
Wi-Fi availability is user/location/time dependent: M(i, l, t) ⊆ N .
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Resource Block
MU 1
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2
3
4
Time
Network 1
Network 2
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Time
Network 1
Network 2
Resource block: a network available at a particular time.
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Resource Block
MU 1
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Time
Network 1
Network 2
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Network 1
Network 2
Trajectory of a user determines its resource blocks (unshaded ones).
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Resource Block
MU 1
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Time
Network 1
Network 2
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Time
Network 1
Network 2
Trajectory of a user determines its resource blocks (unshaded ones).
Example:
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I
User 1’s trajectory: (14, 15, 16, 16) ⇒ six resource blocks.
User 2’s trajectory: (4, 8, 12, 16) ⇒ five resource blocks.
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Selection of Resource Blocks
1
MU 1
2
3
4
1
Network 2
2
1
MU 2
2
3
4
Time
2
1
Time
Network 1
1
Network 2
2
4
Time t
Route 1
MU 1
Route 2
Route 3
network n
1
Network 1
3
2
3
4
Time t
Route 1
Route 2
MU 2
network n
Selection of resource blocks ⇒ feasible route in the graph.
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Resource block: vertex in the graph
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Network selection between two time slots: edge in the graph
Examples: switching time = 1.
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User Suplus = Utility - Payment
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(n)
µ
Utility = a m+x
(n,t) (network throughput).
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µ(n) : Capacity of network n ∈ N .
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m: Congestion level.
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x (n,t) : Background traffic of network n ∈ N at time t ∈ T .
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a: Scaling weight.
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(n)
µ
Utility = a m+x
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I
I
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a: Scaling weight.
(n)
µ
Payment = γ(n) m+x
(n,t) ∆t (usage-based pricing).
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γ(n): Unit price of network n ∈ N .
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(n)
µ
Utility = a m+x
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I
I
I
a: Scaling weight.
(n)
µ
Payment = γ(n) m+x
(n,t) ∆t (usage-based pricing).
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γ(n): Unit price of network n ∈ N .
Surplus = utility - payment (in a single time slot):
σ (n,t) (m) = (a − γ(n)∆t)
I
µ(n)
.
m + x (n,t)
This depends on the resource block (n, t) ⇒ one vertex v in route ri
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User Payoff = Surplus - Switching Cost
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Switching Cost: computed based on edges in the route ri
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No channel switching: zero cost
Channel switching: positive cost
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Switching Cost: computed based on edges in the route ri
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No channel switching: zero cost
Channel switching: positive cost
Payoff = total surplus - total switching cost:
X
X
ρi (r) =
σ v mv (r) −
ge.
v ∈V(ri )
e∈E(ri )
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Surplus σ v (mv ): depends on vertex v , coupled across users in mv .
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Switching cost g e : depends on edge e, decoupled across users.
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Network Selection Game
2
1
3
1
2
network n
1
4
Time t
Route 1
MU 1
Route 2
Route 3
2
1
3
4
Time t
Route 1
Route 2
MU 2
2
network n
Players: users.
Strategies: feasible routes
Each player chooses a route to maximize its payoff
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Key Questions
How should a user make a route choice?
Will users’ choices converge to a network equilibrium?
How fast does convergence happen?
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How Should a User Choose its Route?
Better response update: a user chooses a new route to improve his
payoff, assuming that other users’ route choices are fixed.
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How Should a User Choose its Route?
Better response update: a user chooses a new route to improve his
payoff, assuming that other users’ route choices are fixed.
Result: The complexity of computing a better response is polynomial
in terms of number of users (O(I 2 )).
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Key idea: finding a better response update is equivalent of computing
a shortest path in a graph.
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Will Users’ Greedy Choices Converge?
We want to achieve the Pure Nash equilibrium (NE):
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A route choice profile r = {ri , ∀i ∈ I} where no user can perform a
better response update
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I
It would be nice to have the Finite improvement property (FIP),
where better response updates always converge to a pure NE.
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It would be nice to have the Finite improvement property (FIP),
where better response updates always converge to a pure NE.
Result: every network selection game has the FIP.
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Key idea: show that the game is equivalent to a congestion game.
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Total Number of Better Response Updates
How Fast Does Convergence Happen?
160
140
120
100
80
60
40
20
0
5
10
15
20
25
30
Total Number of MUs I
The convergence scales well with the number of users.
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Compare three schemes:
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NSG: Network selection game.
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On-the-spot offloading (OTSO): Offload to the Wi-Fi network
whenever Wi-Fi is available.
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Cellular-only: Use the cellular network all the time.
Setting:
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Grid topology with L = 16 possible locations.
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Cellular data rate: 300 Mbps (shared among L locations).
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Wi-Fi data rate: 54 Mbps (for one location).
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Cellular price > 0 and Wi-Fi price = 0.
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Switching time = 1.
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Impact of the Number of Users
Setting: Switching cost c switch = 1 and cellular price = US $3/Gbyte.
80
NSG
OTSO
Cellular−Only
Average Payoff per MU
70
60
50
40
30
20
10
5
10
15
20
25
30
Total Number of MUs I
Average payoff decreases with the level of contention.
Cellular-only performs well under low traffic load.
OTSO performs well under high traffic load and low c switch .
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Impact of the Wi-Fi Availability
Setting: I = 30 MUs and cellular price = US $6/Gbyte.
600
Average Payoff per MU
500
Class 1
Class 2
Class 3
400
300
200
100
0
50
250
450
650
850
Wi−Fi Data Rate (Mbps)
Class 1: Can access all the networks all the time.
Class 2: Can access the cellular network all the time, and a Wi-Fi hotspot
50% of the time.
Class 3: Can only access the cellular network.
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Summary
Network selection and data offloading: First study on the interactions
of multiple heterogeneous MUs.
Explicit modelling of user mobility, Wi-Fi availability, switching time
and cost, and pricing.
Network Selection Game Analysis: FIP ⇒ Convergence to a pure NE.
Key Question: what about incomplete network information?
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Delay-Aware Predictive Network Selection
in Data Offloading
Joint work with Haoran Yu, Man Hon Cheung (CUHK),
Longbo Huang (Tsinghua)
IEEE INFOCOM 2014 (Student Poster)
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Network Selection with Cost-Delay Tradeoff
An operator’s perspective: how to dynamically select networks for
users to balance the long-term operation cost and traffic delay?
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Carrier-grade WiFi
Challenge: limited information on system randomness
We consider two cases:
1
Only having current slot information
2
Having both current and predicted future information
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System Model
6
5
4
7
3
2
1
8
9
Multiple networks, locations, and users (similar as before)
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Network availability is location-dependent
Users randomly move across the locations with random traffic arrivals
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System Model
6
5
4
7
3
2
1
8
9
Multiple networks, locations, and users (similar as before)
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Network availability is location-dependent
Users randomly move across the locations with random traffic arrivals
Ql (t): the amount of user l’ unserved traffic (queue length)
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Case 1: Only Knowing Current Network Information
Operator only knows current user locations and queue lengths
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No statistic knowledge of users’ mobilities and traffic patterns
Queue length based optimization based on Lyapunov method
Intuition:
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When Ql (t) is small, suspending service does not lead to severe delay.
Strategy: wait till enter Wi-Fi area
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When Ql (t) is large, suspending service incurs severe delay.
Strategy: serve user l immediately even with a high operational cost
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Delay-Aware Network Selection (DNS)
Delay-Aware Network Selection (DNS) Algorithm
At each time slot t, the operator:
Chooses the network selection vector α (t) that solves
L
i
h X
Ql (t)rl (α (t)) + Vc (α (t))
minimize −
l=1
variables
αl (t) ∈ NSl (t) ∪ {0} , ∀l ∈ L.
Updates the queueing vector Q (t + 1) accordingly.
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Performance of DNS
Achieve [O (1/V ) , O (V )] cost-delay tradeoff (V : control parameter)
Conclusion: The operation cost can be pushed arbitrarily close to the
optimal value, but at the expense of an increase in the traffic delay.
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Case 2: Predictive Network Selection
How to improve DNS with predicted future information?
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When we know the statistics of user mobility and traffic patterns
Solution: propose a novel frame-based Lyapunov optimization
technique and design the GP-DNS algorithm
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230
ion vecαm (τ ),
0.4
0.6
GP-DNS Algorithm
0.8
1
1.2
1.4
1.6
Average Queue Length
1.8
2
2.2
2.4
4
x 10
Fig. 3: Cost-Delay Tradeoff of DNS and GP-DNS.
16000
ctor that
he same
m) ;
39.0%
14000
DNS
GP−DNS (T=5)
GP−DNS (T=10)
GP−DNS (T=25)
Average Queue Length
12000
10000
40.5%
8000
40.3%
6000
40.4%
4000
2000
0
240
250
260
Average Operation Cost
270
easible
Fig. Cost-Delay
4: Delay Tradeoff
Reduction
through
Prediction.
of DNS
and GP-DNS
This is
updating rule and the fact that the number of feasible network
grows
selection
vectorsimproves
is finite. the
Thecost-delay
details aretradeoff
given in [17].
Future
information
T . To
I
ropose
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onlyT = 25
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operation cost
250, GP-DNS
P-DNS
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computation-friendly
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window
saves 40.5%
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exity is
way of solving line 6 is also given in [17].
Data Offloading (Tutorial)
V. Mobile
N UMERICAL
R ESULTS
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Summary
Prediction can significantly improve network performance.
Need to carefully tradeoff algorithm complexity and network
performance.
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Economics Issues for Wi-Fi Offloading
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Bargaining-based Mobile Data Offloading
Joint work with Lin Gao & Duozhe Li (CUHK)
George Iosifidis & Leandros Tassiulas (University of Thessaly)
IEEE JSAC 2014
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Mobile Data Offloading
BS2
MU13
BS1
AP1
MU11
MU21
AP2
MU24
AP4
MU14
MU32
AP3
BS3 MU33
MU31
One mobile network operator (MNO) offloads to multiple Access
Point Operators (APOs).
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Key Problems
Efficiency: How to offload traffic efficiently?
Fairness: How to share the benefit among the MNO and APOs fairly?
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Bargaining-based Solution
Key Idea: The MNO bargains with each APO for the amount of
offloading traffic and the respective compensation to the APO.
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Bargaining-based Solution
Key Idea: The MNO bargains with each APO for the amount of
offloading traffic and the respective compensation to the APO.
Bargaining is useful for resolving situations where
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Players have a common desirable to reach a mutual agreement.
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Players have individual payoffs.
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Allowing disagreement: no agreement may be forced on any player.
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There is a conflict of interest among players about the agreement.
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A Simple Example
Scenario: Player 1 sells a book to Player 2 at a price p
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Problem: Two players bargain for the price p.
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The payoffs of players: u1 = p, u2 = 1 − p.
F
I
Suppose the book is worth 0 to player 1, and 1 to player 2.
The set of feasible agreements: U = {(u1 , u2 )|u1 + u2 = 1}
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The disagreement: D = (d1 , d2 ) = (0, 0)
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A bargaining solution is an outcome (v1 , v2 ) ∈ U ∪ D
What will be a proper bargaining solution?
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Nash Bargaining Theory
Nash bargaining theory: An axiom-based theory
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Pareto Efficiency
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Symmetry
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Invariant to Affine Transformations
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Independence of Irrelevant Alternatives
Nash bargaining solution
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Unique solution that satisfies the Nash’s 4 axioms.
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Nash Bargaining Solution
Nash Bargaining Solution (NBS)
Nash bargaining solution solves the optimization problem:
max (v1 − d1 ) · (v2 − d2 )
v1 ,v2
subject to (v1 , v2 ) ∈ U ∪ D
v1 ≥ d1 , v2 ≥ d2
Recall the previous example:
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When (d1 , d2 ) = (0, 0): NBS is (v1 , v2 ) = (0.5, 0.5);
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When (d1 , d2 ) = (0, 0.4): NBS is (v1 , v2 ) = (0.3, 0.7);
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System Model
One Mobile Network Operator (MNO)
I
I
Operating one or multiple macrocell base stations (BSs);
Serving many mobile users (MUs);
N Access Point Owners (APOs)
I
I
Each operating one WiFi or femtocell access point (AP);
APs are geographically non-overlapping with each other;
BS2
MU13
BS1
AP1
MU11
MU21
AP2
MU24
AP4
MU14
MU32
AP3
BS3 MU33
MU31
Example: N = 4 APOs.
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Decisions Variables
Variables:
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I
The traffic offloaded to each AP;
The payment to each AP;
Traffic Offloading Profile: x = (x1 , . . . , xN )
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xn : the traffic offloaded to AP n;
Payment Profile: z = (z1 , . . . , zN )
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zn : the payment to AP n;
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Payoffs
MNO’s Payoff: net cost reduction
U(x; z) = R(x) −
I
I
N
X
zn
n=1
R(x): the MNO’s operational cost reduction
PN
n=1 zn : the MNO’s total payment to APOs
APO’s Payoff: net profit increase
Vn (xn ; zn ) = Qn (xn ) + zn
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I
Qn (xn ): the APO n’s profit loss from its own traffic
zn : the APO n’s profit from serving the MNO
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Social Welfare
Social Welfare: sum of the MNO’s and all APOs’ payoffs
Ψ(x) = R(x) +
N
X
Qn (xn )
n=1
The payment between the MNO and each APO is internal transfer
and is canceled out.
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Key Problems
How much traffic should each APO offload for the MNO?
How much should each APO be paid for the offloading, considering
efficiency and fairness.
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Starting Point: One-to-One Bargaining
One MNO and one APO n
One-to-One Bargaining Problem
max U(xn ; zn ) · Vn (xn ; zn )
(xn ,zn )
s.t. U(xn ; zn ) ≥ U0 , Vn (xn ; zn ) ≥ V0n
I
I
U0 = 0: the disagreement of the MNO;
V0n = 0: the disagreement of the APO;
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Starting Point: One-to-One Bargaining
One MNO and one APO n
One-to-One Bargaining Problem
max U(xn ; zn ) · Vn (xn ; zn )
(xn ,zn )
s.t. U(xn ; zn ) ≥ U0 , Vn (xn ; zn ) ≥ V0n
I
I
U0 = 0: the disagreement of the MNO;
V0n = 0: the disagreement of the APO;
In this case, Social Welfare = MNO’s Payoff + APO’s Payoff
Ψ(xn ) = U(xn ; zn ) + V (xn ; zn )
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An Equivalent One-to-One Bargaining
Introduce a new variable πn = Vn (xn ; zn ) (APO’s payoff)
Then U(xn ; zn ) = Ψ(xn ) − πn
An Equivalent Bargaining
max (Ψ(xn ) − πn ) · πn
(xn ,πn )
s.t. Ψ(xn ) − πn ≥ 0, πn ≥ 0
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An Equivalent One-to-One Bargaining
Introduce a new variable πn = Vn (xn ; zn ) (APO’s payoff)
Then U(xn ; zn ) = Ψ(xn ) − πn
An Equivalent Bargaining
max (Ψ(xn ) − πn ) · πn
(xn ,πn )
s.t. Ψ(xn ) − πn ≥ 0, πn ≥ 0
Solving this allows us to see the relationship between APO’s payoff
(πn ) and the social welfare (Ψ(xn )) clearly.
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NBS for One-to-One Bargaining
One-to-One NBS
The NBS (xn∗ , πn∗ ) for the one-to-one bargaining is
xn∗ = xno = arg maxxn Ψ(xn ), and πn∗ =
1
2
· Ψ(xno )
xno = arg maxxn Ψ(xn ): Bargaining solution maximizes social welfare;
πn∗ =
1
2
· Ψ(xno ): APO gets half of the generated social welfare;
U = Ψ(xno ) − πn∗ =
social welfare;
1
2
· Ψ(xno ): the MNO gets half of the generated
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General One-to-Many Bargaining
Now consider a general network scenario with N APOs.
N coupled one-to-one bargaining
I
I
I
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Bargaining between the MNO and APO 1 for (x1 , z1 )
Bargaining between the MNO and APO 2 for (x2 , z2 )
...
Bargaining between the MNO and APO N for (xN , zN )
Bargaining Solution: {x, z} = {(xn , zn )}n∈N
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Two Bargaining Protocols
Sequential Bargaining: MNO bargains with all APOs sequentially (in
a predefined order).
Concurrent Bargaining: MNO bargains with all APOs concurrently.
Completed Bargaining
On-going Bargaining
Future Bargaining
MNO
MNO
AP 1
AP 5
AP 2
AP 3
(a) Sequential Bargaining
AP 1
AP 5
AP 2
AP 4
AP 3
AP 4
(b) Concurrent Bargaining
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APO Grouping
APOs can either bargain individually with the MNO, or
Form one or multiple groups bargaining with the MNO jointly.
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Sequential Bargaining
Bargain from user 1 to user N
Sequential Nash Bargaining Solution (NBS)
{x∗ , π ∗ } = {(xn∗ , πn∗ )}n∈N
Sequential NBS
The NBS {x∗ , π ∗ } under the sequential bargaining is
xn∗ = xno , πn∗ =
I
I
¯n
∆
, ∀n = 1, ..., N
2
xo = arg maxx Ψ(x): bargaining solution maximizes social welfare;
¯ n : the virtual marginal social welfare generated by APO n;
∆
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Virtual Marginal Social Welfare
Virtual Marginal Social Welfare generated by APO n
¯n =
∆
1
X
In+1 =0
1
X
∆n (In+1 ; ...; IN )
...
2N−n
IN =0
Represents average marginal social welfare by APO n, assuming
I
I
MNO has reached agreements with all APOs 1, ...., n − 1 (before n);
MNO will reach agreement with each APO in {n + 1, ..., N} (after n)
with a probability of 0.5.
Consider all possibilities (through the values of In+1 to IN )
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Impact of APO Ordering
APO: Early-Mover Advantage
Under the sequential bargaining, an APO will obtain a higher payoff if it
bargains with the MNO earlier.
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Impact of APO Ordering
APO: Early-Mover Advantage
Under the sequential bargaining, an APO will obtain a higher payoff if it
bargains with the MNO earlier.
MNO: Invariance to the Order
Under the sequential bargaining, the bargaining order of APOs does not
affect the MNO’s payoff.
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Impact of Grouping
Intra-Grouping Benefit
Under the sequential bargaining, group bargaining always benefits the
APOs in the group.
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Impact of Grouping
Under the sequential bargaining, group bargaining always benefits the
APOs in the group.
Inter-Grouping Benefit
Under the sequential bargaining, group bargaining
Improves the payoffs of all APOs bargaining before the group,
Does not affect the APOs bargaining after the group.
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Concurrent Bargaining
Bargain concurrently with user 1 to user N
Concurrent Nash Bargaining Solution (NBS)
{x∗ , π ∗ } = {(xn∗ , πn∗ )}n∈N
Concurrent NBS
The NBS {x∗ , π ∗ } under the concurrent bargaining is
xn∗ = xno , πn∗ =
I
I
en
∆
, ∀n = 1, ..., N
2
xo = arg maxx Ψ(x): bargaining solution maximizes social welfare;
e n = Ψ(x∗ , x ∗ ) − Ψ(x∗ , 0): the actual marginal social welfare
∆
−n n
−n
generated by APO n;
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Property of Concurrent NBS
Concurrently Moving Tragedy
The payoff of each APO under the concurrent bargaining equals to the
worst-case payoff that it can achieve under the sequential bargaining (as
the last APO).
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Impact of Grouping
Under the concurrent bargaining, grouping of APOs always benefits APOs
in the group.
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Impact of Grouping
Under the concurrent bargaining, grouping of APOs always benefits APOs
in the group.
No Inter-Grouping Benefit
Under the concurrent bargaining, grouping of APOs does not affect the
APOs not in the group.
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Simulations
S equential Bargaining
16
h5i denotes the merged
group: {5,6,7,8,9,10}.
14
12
Payoff of APO
Payoffs of APOs
Concurrent Bargaining
10
h5i
8
6
4
4
3
2
2
1
0
1
2
3
4
5
6
7
Groupin g Structure
8
9
10
C Bargaining
AP 1
16
16
14
14
12
12
10
10
8
8
6
6
4
4
2
2
0
1
2
3
4
5
6
7
Groupi ng Structure
8
AP 2
AP 3
AP 4
AP 5
AP 6
AP 7
AP 8
AP 9
Group
0
9
10 10
123456789
Group Structure
Left figure: Payoffs of APOs under sequential bargaining
I
I
Early-mover advantage
Positive intra-grouping effect, positive inter-grouping effect.
Right figure: Payoffs of APOs under concurrent bargaining
I
I
Concurrently moving tragedy
Positive intra-grouping effect, no inter-grouping effect.
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Summary
Study a general mobile data offloading market with one MNO and
multiple APOs.
Propose two one-to-many bargaining protocols.
Achieve efficient offloading solution and fair benefit division (among
the MNO and APOs).
Analyze the impact of bargaining protocols and grouping structure.
Next Step: What happen if there are multiple MNOs?
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An Iterative Double Auction Mechanism
for Mobile Data Offloading
Joint work with Lin Gao (CUHK)
IEEE WiOpt 2013 (Best Paper Award)
June 2014
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Mobile Data Offloading Market
BS2
MU13
BS1
AP1
MU11
MU21
AP2
MU24
AP4
MU14
MU32
AP3
BS3
MU33
MU31
Multiple MNOs and multiple APOs
Each MNO can lease multiple APOs
Each APO can offload for multiple MNOs
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Key Problems
From the MNO’s Perspective: How much traffic should each MNO
offload to each APO, and how much to pay?
From the APO owner’s Perspective: How much traffic should each
APO offload for each MNO, and how much to charge?
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System Model
Each MNO is represented by one Base Station (BS)
M , {1, ..., M}: the set of BSs
I , {1, ..., I }: the set of APOs (or simply APs)
BS2
MU13
BS1
AP1
MU11
MU21
AP2
MU24
AP4
MU14
MU32
AP3
BS3 MU33
MU31
Example: M = {1, 2, 3} and I = {1, 2, 3, 4}.
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For Each BS m ∈ M
xmi : offloading request to AP i
xm , (xmi , ∀i ∈ I): offload request vector to all APs
Jm (xm ): the utility (cost reduction) function of BS m
I
Positive, increasing, and jointly strictly concave
I
AP-specific: depending on xm , not just the total traffic
I
Intuition: offloading cell-edge traffic will lead to more cost reduction
P
i∈I
xmi
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For Each AP i ∈ I
yim : offload admission for BS m
yi , (yim , ∀m ∈ M): offload admission vector for all BSs;
Vi (yi ): the cost function of AP i
I
Positive, increasing, and jointly strictly convex.
I
Can be BS-specific
Ci : capacity constraint
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BS Utility Jm and AP Cost Vi
Utility and Cost
Functions
Jm (xm)
4
3
Vi ( yi)
2
1
Amount of
Offloaded
Data
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Market Outcome
A feasible market outcome is where BSs and APs reach an agreement:
xmi = yim ,
∀m ∈ M, ∀i ∈ I.
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A Benchmark Problem
Social Welfare Maximization (Efficiency)
maximize
X
m∈M
subject to
variables
Jm (xm ) −
X
Vi (yi )
......Social Welfare
i∈I
(i)
P
(ii)
xmi = yim , ∀m ∈ M, i ∈ I, ......Feasibility
m∈M yim ≤ Ci , ∀i ∈ I,
......Capacity constraint
xm , yi , ∀m, ∀i.
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Socially Optimal Solution
Socially Optimal KKT
∂Jm (xm )
∂Vi (yi )
− µmi = 0, (A2) :
− µmi + λi = 0,
∂xmi
∂yim
X
(A3) : λi ·
yim − Ci = 0, (A4) : µmi · (yim − xmi ) = 0,
(A1) :
m∈M
(A5) : xmi = yim .
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Challenge: Information Asymmetry
The utility function Jm (xm ) is the private information of BS m:
I
Not known by other BSs, APs, and any market coordinator
The cost function Vi (yi ) is the private information of AP i:
I
Not known by other APs, BSs, and any market coordinator
It is difficult to achieve efficiency (social welfare maximization).
I
Conflict of interests: BSs want to offload more traffic with less
payment, while APs want to admit less traffic with more payment.
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A First Throught
A traditional approach: Two-sided Market → Double Auction
I
A market controller or broker acts as the auctioneer;
I
BSs and APs act as bidders;
I
The auctioneer decides the allocation and payment rules such that all
bidders truthfully disclose their private information.
Direct application of double auction does not work here
I
Every bidder may have infinite amount of private information due to
the continuity of the utility/cost function.
I
According to [Myerson’1983], there does not exist a double auction
that possesses all the following properties:
F
F
F
F
Efficiency
Individually rationality
Incentive compatibility
Budget balanced
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Our Approach: Iterative Double Auction (IDA)
Our proposed approach: Iterative Double Auction
I
Conducts one double auction in each round.
Next round
Auctioneer
Auctioneer
Payment rule
Allocation rule
Updating Payment rule
Allocation rule
Bidder
Bidder
Disclore all private
information
Signaling his private
information
Fig. Double Auction vs Iterative Double Auction
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How IDA Works?
Key Steps
Step 1 (Rules): The auctioneer broadcasts the payment rule hm (·)
to every BS m and the reimbursement rule li (·) to every AP i;
Step 2 (Bidding): Every BS m determines his bids pmi to every AP
i. Every AP i determines his bid αim to every BS m. Both aim at
maximizing their respective objectives.
Step 3 (Allocation): The auctioneer determines the allocation xmi
and yim between every BS m and AP i, aiming at maximizing a public
auxiliary objective function:
X X
αim 2 W (x, y) ,
pmi log xmi −
y
.
2 im
m∈M i∈I
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How IDA Works?
Key Steps
Step 1 (Rules): The auctioneer broadcasts the payment rule hm (·)
to every BS m and the reimbursement rule li (·) to every AP i;
Step 2 (Bidding): Every BS m determines his bids pmi to every AP
i. Every AP i determines his bid αim to every BS m. Both aim at
maximizing their respective objectives.
Step 3 (Allocation): The auctioneer determines the allocation xmi
and yim between every BS m and AP i, aiming at maximizing a public
auxiliary objective function:
X X
αim 2 W (x, y) ,
pmi log xmi −
y
.
2 im
m∈M i∈I
Question: What are the conditions that guarantee us to achieve the
social optimal allocation?
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Analysis of IDA
Step 3: Allocation
Auctioneer Optimal KKT
()
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Analysis of IDA
Step 2: Bidding
Individual Optimal Bids
()
Socially Optimal Bids
*
Step 3: Allocation
()
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Analysis of IDA
Step 1: Rules
Socially Optimal Rules
*
Step 2: Bidding
Individual Optimal Bids
()
Socially Optimal Bids
*
Step 3: Allocation
()
June 2014
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Social
to APs and BS
execute IDA al
100
IDA - Convergence
50
Convergence of IDA 00 20 40 60 80 100 120 140
Step − t
The IDA algorithm converges to the socially optimal solution.
Fig. 3.
Evolution of social welfare produced by the IDA.
0.4
0.2
Gap y − x
0
−0.2
BS 1, AP 1: y11 −x11
BS 1, AP 2: y21−x12
−0.4
BS 2, AP 1: y −x
12
−0.6
21
BS 2, AP 2: y22−x22
−0.8
−1
−1.2
0
20
40
60
80
100
120
Step − t
of the
gapgap
between
i.e.,admitted
yim − xdata
mi and y(x)
im , and
mi . (y).
Fig.Evolution
4. Evolution
of the
between xrequested
[1] Cisco, “Cisco
Forecast Upda
[2] Bloomberg: “A
[3] FemtoForum, “
[4] AT&T Press R
Additional Cit
[5] BT Wifi Press
Customers”, Ju
[6] Cisco, “Makin
[7] Republic Wire
[8] Spectrum bridg
[9] R. B. Myerso
Bilateral Tradi
[10] R. P. McAfe
Economic The
[11] P. Maille, B.
Pricing of Inte
[12] D. P. Paloma
Network Utilit
1451, 2006.
[13] F. P. Kelly, A
Networks: Sha
of Oper. Res. S
[14] L. Johansen, “
[15] K. Lee, I. Rh
How Much Ca
[16] N. Ristanovic
Efficient Offlo
ρ11 = 0.74. Finally, the payments of the BSs 1, 2 and 5 are
p21 =Mobile
6.29,Data
andOffloading
p51 = (Tutorial)
6.63, respectively. Notice
June 2014
11 = 7.3,
Jianwei Huang p
(CUHK)
116 / 146
Properties of IDA
Properties of IDA
Efficient
I
The IDA mechanism achieves the social welfare maximization;
Weakly Budget Balanced
I
I
The auctioneer does not lose money by organizing an IDA;
If there is no capacity constraint, the auctioneer neither lose money nor
gain money by organizing an IDA (strongly budget balanced);
Incentive Compatible
I
All bidders (price-taking) act in a truthful manner;
Individually Rational
I
All bidders achieve non-negative utilities.
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Summary
Consider multiple MNOs offloading to multiple APs.
Iterative double auction mechanism that satisfies all desirable
properties.
Next Step: Do we always offload traffic from cellular to Wi-Fi?
June 2014
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Enabling Crowd-Sourced Mobile Internet Access
Joint work with Lin Gao (CUHK)
IEEE INFOCOM 2014
June 2014
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Imbalance of Mobile Internet Access
Different users have different access technologies and access speeds:
3G/4G, femtocell, Wi-Fi.
Different networks have different congestion levels even at the same
time and location.
How to effectively take advantage of and integrate heterogeneous
network access capabilities?
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Crowd-Sourced Mobile Internet Access
3G/4G
Wi-Fi
Femtocell
Share the best mobile internet connection(s) among users.
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Commercial Cases
Open Garden (http://opengarden.com)
M-87 (http://www.m-87.com/)
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Key Problems
How to achieve an efficient and fair network resource allocation?
I
I
Who will download data for whom, and how much?
Who will route data from each host to each client, and how much?
How to encourage the user participation and cooperation?
I
how to compensate the hosts and the relays for their efforts?
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Internet
3G Base Station
WiFi Router
4G
3G
4G
WiFi
4G Base Station
WiFi
Bluetooth
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Internet
3G Base Station
WiFi Router
4G
3G
ta
ta
Relay
Gateway Data
(Host)
Da
Da
4G
WiFi
Bluetooth
Data
Gateway
(Host)
Data
WiFi
4G Base Station
Client
Client
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Key Features
Three types of roles
I
Host (Gateway): Downloading data from Internet
I
Relay: Forwarding data for others
I
Client: Consuming data
I
A mobile user may have multiple roles
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Key Features
Three types of roles
I
Host (Gateway): Downloading data from Internet
I
Relay: Forwarding data for others
I
Client: Consuming data
I
A mobile user may have multiple roles
User-provided networking
I
Mobile users can access internet through the hosting of other users.
Multi-hop accessing
I
Mobile users can access internet through the relay of multiple devices.
Access bonding
I
Mobile users can access internet through multiple access links.
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System Model
A set of mobile users: I = {1, 2, ..., I }
For each user i ∈ I:
Internet
Ci
ei
pi
WiFi
Bluetooth
I
Cij, e s
ij
User i
Cji, e r
ij
User j
ci , cij , cji , j ∈ I: link capacity;
I
ei , eijs , eijr , j ∈ I: unit energy consumption;
I
pi : usage-based pricing for accessing Internet.
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Client Model
When user i ∈ I is a client.
Internet
y1(i)
User 1
User 2
(i)
y1
I
I
I
yi(i)
y2(i)
y2
(i)
User i
(client)
... yI(i)
yj(i)
User j
(i)
yj : the data downloaded via host j for client i;
P
(i)
y (i) = j∈I yj : the total data consumed by client i;
Ui y (i) : the utility function of client i.
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Host Model
When user i ∈ I is a host (gateway).
Internet
yi(1)
yi(2)
...
yi(I)
yi(2)
yi(1)
User 2
User 1
yi(3)
User i
(host)
yi(j)
User j
yi(4)
User 4
User 3
I
I
I
I
(j)
yi : the data downloaded via host i for a client j;
P
(j)
yi = j∈I yi : the total data downloaded via host i;
ei · yi : the total energy consumption for downloading data;
pi · yi : the total payment for downloading data;
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Host Model
When user i ∈ I is a host (gateway).
Internet
yi(1)
yi(2)
...
yi(I)
yi(2)
yi(1)
User 2
User 1
yi(3)
User i
(host)
yi(j)
User j
yi(4)
User 4
User 3
I
I
I
I
I
(j)
yi : the data downloaded via host i for a client j;
P
(j)
yi = j∈I yi : the total data downloaded via host i;
ei · yi : the total energy consumption for downloading data;
pi · yi : the total payment for downloading data;
Downloading capacity constraint: yi ≤ ci .
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Relay Model
When user i ∈ I is a relay.
Internet
xij (n)
, n=1,...,I
User i
(relay)
(n)
n∈I :
I
xij,
I
ejir ·
P
I
eijs ·
P
xji (n)
, n=1,...,I
User j
the data relayed from user i to user j, for client n;
(n)
n xji :
total energy consumption for receiving data from user j;
(n)
n xij :
total energy consumption for sending data to user j.
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Relay Model
When user i ∈ I is a relay.
Internet
xij (n)
, n=1,...,I
User i
(relay)
(n)
n∈I :
I
xij,
I
ejir ·
P
I
eijs ·
P
I
I
xji (n)
, n=1,...,I
User j
the data relayed from user i to user j, for client n;
(n)
n xji :
total energy consumption for receiving data from user j;
(n)
n xij :
total energy consumption for sending data to user j.
P (n)
P (n)
Relay capacity constraints:
n xij ≤ cij ,
n xji ≤ cji
P (n)
P
(n)
(n)
Flow balance constraint: j xji + yi = j xij , n ∈ I
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User Payoff
Payoff of each user i ∈ I:
Ji (xi , yi ) = Ui − Pi − Ei
(n)
I
yi = {yi }n∈I : Downloading matrix;
I
xi = {xij }j,n∈I : Relaying matrix;
I
Ui : Utility of user i (as a client);
I
Pi : Total payment of user i (as a host for internet access);
I
Ei : Total energy consumption of user i (as a host and/or relay);
(n)
To maximize the payoff, each user only wants to be a client, but not
as a host or relay.
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Our Goal
Mechanism design to address incentive, efficiency, and fairness issues
I
Encouraging the user participation and cooperation;
I
Achieving an efficient and fair network resource allocation.
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Incentive Design Challenges
Users may not want to participate in the crowd-sourced system
I
For example, those without a demand for Internet access;
Users may not want to download or relay data for others
I
For example, user i may not want to download data for user 4.
Internet
yi(1)
yi(2)
...
yi(I)
yi(2)
yi(1)
User 2
User 1
yi(3)
User i
(host)
X
yi(j)
User j
User 4
User 3
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Solution: Virtual Currency
Key idea: User pays certain virtual currency to those who send data
to him (I give you money, you give me data).
User j
xji(1)
...
xji(n)
...
xji(I)
zji(1)
...
zji(n)
...
zji(I)
User i
(n)
zji : the virtual price that user i pays j for receiving data (of client n);
P
(n)
n zji
(n)
· xji : the total virtual money that user i pays j
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Modified Payoff with Virtual Currency
Modified payoff of each user i ∈ I:
Ji (xi , yi , zi ) = Ui − Pi − Ei + Vi
I
I
(n)
zi = {zij }j,n∈I : Virtual payment matrix;
Vi : Total virtual currency evaluation of user i;
Modified payoff maximization takes care of incentive issues.
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Efficiency and Fairness Issues
I
Efficiency: The aggregate payoff of all users is maximised.
I
Fairness: Every user achieves a satisfactory payoff;
Our Solution: Nash Bargaining Solution
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Nash Bargaining Solution
Nash Bargaining Problem (NBP)
max
xi ,yi ,zi ,∀i
Πi∈I (Ji (xi , yi , zi ) − Ji0 )
s.t., (a) Ji ≥ Ji0
(Ji0 : disagreement point)
(b) Capacity constraints;
(c) Flow balance constraint;
(d) Virtual current budget constraint.
The NBP problem has a unique optimal solution.
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Nash Bargaining Implementation
Centralized Implementation
I
A central control node collects all the required network information,
and computes the Nash bargaining solution.
Decentralized Implementation
I
Iterative updating: Users update their individual decisions sequentially
and repeatedly, and signals to neighbors until convergence.
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Simulation
An example with 6 nodes
I
I
Blue Bar: Downloading/relaying data;
Red Bar: Consuming data;
2
1.242
2
1.242
0.0
1
5
1.242
1.242
2.295
2.295
1.143
2.295
2.295
0.059
1.880
0.059
1
2.295
6
2.295
0.059
6
4
51
2.245
0.059
0.059
3
0.059
3
0.059
Standalone (Independent)
Standalone (Independent)
Operation
Operation
Left: Independent Operation.
2 1.240
1.240
0.0
2.295
2.295
0.097
46
0.337
2
0.239 0.097
5
1
2.247
0.337
0.239
1.1431.704
0.304
1.880
0.366 2.245
6
0.350
3
0.410
2.247
0.304
0.350
4
0.366
0.046
0.059
5
1.704
4
0.046
3
0.410
0.044
0.044
UPN Bargained
Operation
UPN Bargained
Operation
Right: Crowd-sourced Operation.
June 2014
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Summary
We study the crowd-sourced mobile internet access system, in
particular, we answer
I
I
How to encourage the user participation and cooperation?
We propose a Nash bargaining solution with virtual currency, which
addresses the incentive, efficiency, and fairness issues.
June 2014
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Going Beyond Offloading
June 2014
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Blurring the Cellular/Wi-Fi Boundary
Mi-Fi turn cellular signal into Wi-Fi signals
Social bandwidth trading: Karma
June 2014
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Why Wi-Fi Complements Cellular?
Wi-Fi may be the primary access technology with cellular as a
coverage supplement
June 2014
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Where Will Wi-Fi Go in The Future?
June 2014
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Where Will Wi-Fi Go in The Future?
It’s Up to You and Me.
June 2014
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Related Publications
L. Gao, G. Iosifidis, J. Huang, L. Tassiulas, and D. Li, “Bargaining-based Mobile
Data Offloading,” IEEE Journal on Selected Areas in Communications, accepted
May 2014
G. Iosifidis, L. Gao, J. Huang, and L. Tassiulas, “Enabling Crowd-Sourced Mobile
Internet Access,” IEEE INFOCOM, May 2014
L. Gao, G. Iosifidis, J. Huang, and L. Tassiulas, “Hybrid Data Pricing for
Network-Assisted User-Provided Connectivity,” IEEE INFOCOM, May 2014
H. Yu, M. Cheung, L. Huang, and J. Huang, “Delay-aware Predictive Network
Selection in Data Offloading,” IEEE INFOCOM Student Poster, May 2014
M. Cheung, R. Southwell, and J. Huang, “Congestion-Aware Network Selection
and Data Offloading” (invited), CISS, March 2014
G. Iosifidis, L. Gao, J. Huang, and L. Tassiulas, “An Iterative Double Auction for
Mobile Data Offloading,” IEEE WiOPT, May 2013 (Best Paper Award)
M. Cheung and J. Huang, “Optimal Delayed Wi-Fi Offloading,” IEEE WiOPT,
May 2013
June 2014
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More Information
NCEL.ie.cuhk.edu.hk
June 2014
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Mobile Data Offloading: A Tutorial

Transcription

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