social-aware utility-based radio resource management

12 Jul. 2011 THALES - SoComNets 1

Social-aware Utility-based Radio Social-aware Utility-based Radio Resource ManagementResource Management

Timotheos Kastrinogiannis

School of Electrical and Computer Engineering National Technical University of Athens (NTUA)

12 Jul. 2011 2THALES - SoComNets

At a glance…

Content Management Networks User’s social behavior; Content attributes, content management goals and new users’

satisfaction criteria.

Physical Networks (i.e., underlay networking paradigms) Network-centric resource allocation and QoS provisioning mechanisms; Network Utility Maximization (NUM) theory.

* Top-down Cross Layering….* Top-down Cross Layering….


Network Centric RRM (The Traditional View)

Rate

Control

Channel Estimation

Connectivity Management

Topology Control

Capacity Regions

Modulation & Coding

Schemes

Power Control

Spectrum Management

Users Assignment

Handover Coordination

Stochastic Control / Scheduling

Quality of Service & Fairness Management

Optimization Theory

Convex Optimization

Network Utility

Maximization

Linear Programming

Game Theory

Layering As

Optimization Decomposition

The Need….. The Evolution…..


A Mathematical Theory of Network Architectures…

The first unifying view and systematic approach Network: Generalized NUM Layering architecture: Decomposition scheme Layers: Decomposed subproblems Interfaces: Functions of primal or dual variables

(KellyMaulloTan98):


Designing a Utility ….

A utility function is based on three key components :

Indicators correlated to data transmission (π.χ. BER).

Indicators correlated to QoS prerequisites fulfillment Indicators correlated to preemption (e.g. real time aplications)

* * * *1 , 1 , ,( ,..., ,..., ,..., ) ( ,..., ) ( ,..., ) ( ,..., )i l m n TL i l SL i l m PE i m nU x x x x U x x U x x U x x


Fundamental Utility Functions

Non-real-time Services

Concave

SigmoidalConvex

Real-time Services


Methodology… (how to break the barrier?)

Content Management Networks

Social Networks (OSN)

Social-Aware Utility Functions

Integrated Heterogeneous Wireless Networks

Radio Resource Management

Overlay user-centric & content-centric network

Underlay user-driven communication network

Social Graphs Attributes

Content-Aware UtilityQoE-Aware Utility

Functions

Content Attributes Behaviourall Attributes

A novel Top-down Cross Layering

e.g., U( Service QoS, Centrality Metrics)

e.g., U( Service QoS, Content Attribute)

U( Service QoS, QoE)


User’s Behavioral Attributes in Content Management (Case I)

Motivation: “Minimizing Node Churn in Peer-to-Peer Streaming”[1]

In P2P networks suffer from highly transient peers who join and leave the system (churn) at their own will.

In P2P streaming services content management should be performed in short time scales.

C. Vassilakis, I. Stavrakakis, “Minimizing Node Churn in Peer-to-Peer Streaming Computer Communications”, Computer Communications Journal, Vol.33, Iss. 14, pp.

1598-1614, Sept. 2010.

Node ChurnAffects System Performance

Is Affected by Node’s Service Quality

Dealing withthe effects of node

churn

Protocols Design

Peers Selection Strategy

Dealing withits causes


Thread of Analysis

Peers Selection Strategy

Node Churn Probability

MOSOff-line function

f (total number of dropped (lost) and repeated (frozen) frames)

Short-term On-line Functionf (Average Achieved Goodput, Target Rates, QoS Requirement Fulfilment)

Utility-Based

Radio Resource Managament

Implicit Calculation

Explicit Calculation

Short-term Goodput QoE Utility (video)

Churn Probability

Can we improve Node’s Churn Probability via RRM ?


Corresponding Optimization*

,1

max1

max

max

max ( , , , )

. .

0 1,2,...,

0 1,2,...,

N

i i i iR P

i

N

ii

i

i i

U R P a b

s t P P

P P i N

R R i N

1, , min, , ,

,

,

( ) ( ) if ( ) ( 1)ˆ ( )

iWRT i i i RT i i RT i RT i

RT i

RT i

b A NodeChurn t b b t B t Tr Bb t

b otherwise

QoE-aware (Content Network-aware) RRM

Goal: Minimize Node’s Churn Probability

* *

, , , ,ˆ ˆˆ ˆ( , , ( )) ( , ( )) i iR R

RT i RT i RT i RT i RTU P a b t U P b t i S

QoS-Aware RRM

11

, ,1

( ) ( ) i

i

WWRT i RT i RT

k

B t t k i S


Networking Results

Correlation with overlay network’s benefits…..

System Av. Goodput (per cell) (kbps)

0

100

200

300

400

500

600

700

800

30 Users 35 Users 40 Users

RRM

RRM_SDUA

System Av. Throughput (per Cell) (kbps)

0

100

200

300

400

500

600

700

800


RRM

RRM_SDUA

System Churn Rate (departures/sec)

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4


RRM

RRM_SDUA


Beginning with a Simple Question….

“By what criteria do we evaluate a particular network architecture?”

Shenker

1995

VS

“The internet was designed to meet the needs of users, and so any evaluative criteria must reduce to the following question: how happy does this architecture make the users?”.


The Evolutionary Path of QoS Initially,Initially,

correlation of services or service classes (application layer) with several network metrics (e.g. threshold-based values for latency, jitter, packet loss, e.t.c)

design of dynamic resource allocation algorithms that aim at maintaining these metrics at acceptable levels.

Then,Then, utility functions define a formal mathematical vehicle towards expressing and quantifying

user’s degree of satisfaction with respect to their multi-criteria service performance.

the goal of network design can be restated as being, quite simply, to maximize the sum of utilities, leading to the establishment of

Network Utility Maximization (NUM)Network Utility Maximization (NUM)

“ “a concrete theoretic framework”a concrete theoretic framework”


Quality of Experience (QoE) (Case II)

Finally,Finally, Quality of Experience (QoE), defined as “a measure of the overall acceptability

of an application or service, as perceived subjectively by the end-user” ITU

How can we correlate QoS and QoE?

“E2E QoSE2E QoS” (enabled via monitoring and proper network reacting mechanisms);

QoE is enabled via the mappingmapping of users’ opinions for the quality of a service (in a normalized way e.g. MOSMOS)) to specific networking metrics (leading to proactive approaches);

Dynamic adaptationDynamic adaptation of a network’s operation and performance, in line with users’ dynamic requests (e.g. adaptive video resolution on demand).


In Reality … Experience is Experience is SubjectiveSubjective & & Context-dependentContext-dependent

Cultural BackgroundCultural Background

Socioeconomic StatusSocioeconomic Status

Personal Experiences Personal Experiences Mood Mood

BackgroundBackground NoiseNoise

Multimedia Multimedia ContentContent

……..

Social DistanceSocial Distance Social BehaviorSocial BehaviorContentContent

ManagementManagement


A Practical Idea

We envision the role of QoE as the vehicle that interconnects users/humans, applications and QoS-aware resource management mechanisms.

We propose a QoE framework that allows users to dynamically and dynamically and asynchronously express their (dis)satisfactionasynchronously express their (dis)satisfaction with respect to the instantaneous experience of their service quality at the overall network QoS-aware resource allocation process.


Design & User InterfaceDynamic Utility Adaptation

NUM – Optimization

Feasibility&

Policies

Resource Allocation Adaptation

Pricing

Inc (+0.03 $ / min)

Dec (-0.02 $ / min)

Current Performance Level

Max Performance Level

Stepwise Increasing Performance& Corresponding Service Cost ($/min) Inc.

Feasibility of the Request


QoE-aware Resource Allocation

Users’ 16, 17, 18 Achieved Goodput Cells’ Overall Goodput


Towards the Second Direction

Content Management Networks

Social Networks (OSN)

Social-Aware Utility Functions

Integrated Heterogeneous Wireless Networks

Radio Resource Management

Overlay user-centric & content-centric network

Underlay user-driven communication network

Social Graphs Attributes

Content-Aware UtilityQoE-Aware Utility

Functions

Content Attributes Behaviourall Attributes

A novel Top-down Cross Layering

e.g., U( Service QoS, Centrality Metrics)

e.g., U( Service QoS, Content Attribute)

U( Service QoS, QoE)


Social-aware Utility-based RRM (Case III)

Motivation: “Scalable Distribution of Content Updates over a Mobile Social Networks” Service: “Dynamic Content Distribution” Co-operation: subscribers to this service share their updates in an

opportunistic fashion Problem: “how the service provider can allocate its bandwidth

optimally to make the content at users as “fresh” as possible.”


System’s Behavior (Under Optimal Content Management)

μ : total injection rate allocated among different users

Users are indexed according to their contact rates, in decreasing order.

Observations: For low values of μ the “most social” user accumulates all the injected rate, thus acting as a global hub of all incoming information;

Under certain conditions, it is actually optimal to allocate no bandwidth to the most social users in the system.

Idea: Social-aware Utility Functions that expressA. User’s QoS Prerequisites B. User’s Priority on Available Radio Resources with Respect to his Role/Importance in the Mechanism Of the Underlay Content Management Network.

Social Users’ Normalized Priority Indicator:

Non-Social Users’ Normalized Priority Indicator:

2

( )

2,1 ( ) ( / sec)

( )

log( 1)

ci i Thr

i

e if CR t CR contactsI t

otherwise


Problem Setting… (initial experimentation…)

where

, , , min,ˆ ( ) ( ) ( )RT i RT i i i RT i ib t b A I t b b t

*

,1

max1

max

max

max ( , , , )

. .

0 1,2,...,

0 1,2,...,

N

i i i iR P

i

N

ii

i

i i

U R P a b

s t P P

P P i N

R R i N

* *

, , , ,ˆ ˆˆ ˆ( , , ( )) ( , ( )) i iR R

RT i RT i RT i RT i RTU P a b t U P b t i S

2

( )

2,1 ( ) ( / sec)

( )

log( 1)

ci i Thr

i

e if CR t CR contactsI t

otherwise


Thank you…Thank you…

social-aware utility-based radio resource management

Documents