online service management algorithm for cellular/waln multimedia networks

Online Service Management Algorithmfor Cellular/WALN Multimedia Networks

SOFSEM 2007

Sungwook Kim

Sogang UniversityDepartment of Computer ScienceSeoul, South Korea

Internet Communication & Control Lab.2

Introduction

Efficient network resource management - key to enhance network performance & QoS Next generation networks - support heterogeneous multimedia services Support heterogeneous multimedia data - while ensuring QoS for higher priority traffic services Traffic pattern is difficult to predict - online approach is essential Adaptive network management - while maintaining a well-balanced network performanc

e


Online Algorithm

Online algorithm - dealing with the online computation problem Online computation problem - based on past events without future information - make decisions in real time Many QoS problems in network management - online computation problems The online resource management & control algorithm - natural candidate for multimedia network operations


Traffic Service

Traffic services new and handoff call services in cellular network - give higher priority to handoff services class I (real-time) and class II (non real-time) call

services in multimedia communication networks - class I data service : Voice telephony, Video-

phone - class II data service : E-mail, ftp, Data on

demand, etc

: give higher priority to class I call services


Bandwidth Reservation

The traffic window size can be adjustable. If CDPclass_I is higher (lower) than Pclass_I, - traffic window size is increased (decreased)

- in steps equal to unit_time. Bandwidth reservation amount is estimated dynamically

- the sum of requested bandwidth by class I calls

during the traffic window

Res (B N )B i ii Wclass I

=

_


Group II Time Window ( t win_II )

Group I Time Window ( t win_I )

unit_time

real time data

Iwintt _0 IIwintt _0 current_time ( )

requested hand off service

group Ireservation

pool

group IIreservation

pool

multimediadata type

t0

non-real time data


Buffer Management

Active Queue Management algorithm : network router is responsible - for detecting network congestion - for notifying end hosts of congestion to adapt their

sending rates

RED and BLUE algorithms - avoid global synchronization - adjust the packet dropping probability in response to

congestion - pushing most of the complexity and state of

differentiated services : to the network edges


RED Algorithm (1)

The RED (Random Early Detection) Algorithm - queue length is used as threshold to detect network

situation - try to maintain an average queue length under congestion Based on recent buffer history - drops incoming packets in a random probabilistic manner - provide a more equitable distribution of packet loss - improve the utilization of the network Major problem - heavily depend on the system parameter values - average queue length is only the index for network

situation


RED Algorithm (2)

for each incoming packet - calculate the average queue length (Avg) : exponential weighted average if Avg < MINh - do nothing if MINh < Avg < MAXh - calculate packet dropping probability Pa - mark packets with probability Pa if MAXh < Avg - mark packet


Blue Algorithm (1)

Recently developed simple algorithm - retain all the desirable features of RED algorithm Main indices of network congestion - directly on packet loss and current link utilization Queue overflow and idle event - update the packet marking probability - learn the correct rate and send back congestion notificatio

n Major problem - queue length variation for bursty traffic changes : difficult to control temporal traffic fluctuations


Blue Algorithm (2)

For each packet loss: if ((now – last_update) > freeze_time ) - Pm = Pm + Di

- last_update = now For link idle event: if ((now – last_update) > freeze_time ) - Pm = Pm - Dd

- last_update = now


Orange (Online range) Algorithm (1)

Three parameter values for QoS and congestion control : adaptive decision by online manner bandwidth range for the reservation (RESb) queue range (Qr) packet marking probability (Mp) Main issue - adaptive range adjustment for bandwidth and buffer control Orange (Online range) control algorithm - adaptive online control for service differentiation - to provide a ‘better effort’ service for class II traffics while ensuring QoS for the admission controlled class I service

s



Adjusts system parameters - in adaptive online fashion Bandwidth reservation range (RESb) Queue range (Qr) - unused reserved bandwidth can be temporarily allocated for buffered class II service - same as the RESb to maximize network performance Packet marking probability (Mp) - decided proportional to the current queue length - adaptively characterized by threshold values



If L < Qr - congestion free : no arriving packets are dropped L > T - all arriving class II data packets are dropped Qr < L < T - class II data packets can be marked with probability Packet marking probability Mp

- L : current queue len

gth - T : maximum buffer

size r

rp2 QT

QLM


Simulation Model

Consists of 7 clusters, each cluster consists of 7 micro cells In the even traffic situation, new call arrivals are Poisson

with rate (0-3 calls/s/cell), which is uniform in all the cells In the uneven traffic situation, the arrival rate of hot cell is

Poisson with rate 3 Capacity of each cell is C (=30Mbps) One base station per cluster is selected randomly as the

faulty base station and this occurs at a random time Mobiles can travel in one of 6 directions with equal

probability with three cases of user velocity Eight different data groups are assumed based on call duration, bandwidth requirement and class of service Durations of calls are exponentially distributed with

different means for different multimedia data types


Simulation Results

Fig.1 Call Blocking Probability Fig.2 Call Dropping Probability

0 0.5 1 1.5 2 2.5 30

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Offered Load (Call Arrival Rate)

Cal

l Blo

ckin

g P

roba

bilit

y

Our Framework

RMI Scheme

ALBCA Scheme

0 0.5 1 1.5 2 2.5 30

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Offered Load (Call Arrival Rate)C

all D

ropp

ing

Pro

babi

lity

Our Framework

RMI Scheme

ALBCA Scheme


Concluding Remarks

Development of efficient bandwidth management - for QoS sensitive multimedia networks Proposed integrated online approach - provides excellent network performance while ensuring QoS guarantees under widely different traffic scenarios On-line decisions based on real time estimates - mutually dependent each other - adaptable and quite flexible to traffic changes Strike the appropriate balanced network performance - among contradictory QoS requirements while other existing schemes cannot offer such an attractive trade off


.

Internet Communication Control (ICC) Research Lab.

Prof. Sungwook Kim


Internet

Differentiated Services (DiffServ) Complexity & Scalability - easy to implement - no state information is needed in the core routers does not suffer from the scalability problems - concentrates on packet forwarding using appropriate queue management Major problem QoS control - not to provide guaranteed QoS for higher priority traffic servi

ces : growing interest in Internet QoS


Bandwidth Reservation (1)

guarantee QoS for class I data traffic services maintain the reserved bandwidth close to the optimal

value on-line estimate by traffic window

- based on real time measurement

- keeps the history of class I task

- learn the pattern of coming requests

- close to the optimal value

- partition the time axis into equal interval

: unit_time



Time Window ( )

unit_time

real time data

current_time ( )

requested hand off services

multimediadata type

tC

non-real time data

FTwint _

tc - FTwint _

FsetW _



The traffic window size can be adjustable. If CBPclass_I is higher (lower) than Pclass_I, - traffic window size is increased (decreased)

- in steps equal to unit_time. Bandwidth reservation amount is estimated dynamically

- the sum of requested bandwidth by class I calls

during the traffic window


=

_


Online management for Internet Guarantee QoS for class I data traffic services maintain the reserved bandwidth close to the optimal val

ue on-line estimate by traffic window - based on real time measurement

ABlink =

MABpath(i,j) =


=

_

linklink UBRB

)(min),(

linkjipathlink

AB


Call Admission Control (1)

CAC is responsible to decide - granted, declined or renegotiated Two system parameters are used:

One-way packet Delivery Time (ODT)

: packet delay time of setting path

the Acceptance Threshold (AT)

: the predefined bit sending rate Network probing - to determine if all routers along the path have

available bandwidth


Call Admission Control (2)

For a new class I request, - a probing packet estimates the available network bandwidth

SR bits/sec ( = BU × ) ≥ ATi bits/sec

For a new class II request, - a probing packet only estimates the unused network bandwidth

SR bits/sec ( = BU × ) ≥ M_ATj bits/sec

Guarantee QoS for class I data traffic services

currentODT

1

currentODT

1


Internet

The rapid growth of data communication network - Internet Protocol (IP) : Internet - QoS sensitive multimedia data services : based on different priority Major Problem - difficult to support guaranteed QoS : bounded delay & minimum throughput for higher priority real time applications


Intserv Model

Integrated Services (IntServ) - in order to provide QoS in Internet. - signal to the network through a reservation request ReSerVation Protocol (RSVP) - end-to-end signaling protocol - receiver-oriented protocol for setting up resource reservation

s - reservations have to be refreshed periodically Major problem Complexity & Scalability - router has to keep state information on all reservations


Diffserv Model

Differentiated Services (DiffServ) Complexity & Scalability - easy to implement - no state information is needed in the core routers does not suffer from the scalability problems - concentrates on packet forwarding using appropriate queue management Major problem QoS control - not to provide guaranteed QoS for higher priority traffic servi

ces : growing interest in Internet QoS


AQM Algorithms

Active Queue Management algorithm : network router is responsible - for detecting network congestion - for notifying end hosts of congestion to adapt their

sending rates RED and BLUE algorithms - avoid global synchronization - adjust the packet dropping probability in response to

congestion - pushing most of the complexity and state of

differentiated services : to the network edges


RED Algorithm (1)

The RED (Random Early Detection) Algorithm - queue length is used as threshold to detect network

situation - try to maintain an average queue length under congestion Based on recent buffer history - drops incoming packets in a random probabilistic manner - provide a more equitable distribution of packet loss - improve the utilization of the network Major problem - heavily depend on the system parameter values - average queue length is only the index for network

situation


RED Algorithm (2)

for each incoming packet - calculate the average queue length (Avg) : exponential weighted average if Avg < MINh - do nothing if MINh < Avg < MAXh - calculate packet dropping probability Pa - mark packets with probability Pa if MAXh < Avg - mark packet


BLUE Algorithm (1)

Recently developed simple algorithm - retain all the desirable features of RED algorithm Main indices of network congestion - directly on packet loss and current link utilization Queue overflow and idle event - update the packet marking probability - learn the correct rate and send back congestion notificatio

n Major problem - queue length variation for bursty traffic changes : difficult to control temporal traffic fluctuations


BLUE Algorithm (2)

For each packet loss: if ((now – last_update) > freeze_time ) - Pm = Pm + Di

- last_update = now For link idle event: if ((now – last_update) > freeze_time ) - Pm = Pm - Dd

- last_update = now


Online Control in Internet

Basic idea of the cellular network management - can be applied to Internet Online strategy based on real time measurements - due to the uncertain network environment : do not require advance knowledge or prediction Major advantage of an online approach - adaptability, flexibility, responsiveness to current traffic c

onditions Online algorithm based on DiffServ model - provides QoS guarantees for higher priority calls while accommodating as many call connections as possi

ble


Multimedia Internet Management

Online management algorithm the QoS provisioning mechanism - guarantee QoS based on call admission control : for class I data service the congestion control mechanism - adaptive bandwidth allocation for higher network performanc

e : for class II data services Integrated online approach - both mechanisms act cooperatively : in order to simultaneously satisfy the conflicting require

ments


Orange (Online range) Algorithm

Three parameter values for QoS and congestion control : adaptive decision by online manner bandwidth range for the reservation (RESb) queue range (Qr) packet marking probability (Mp) Main issue - adaptive range adjustment for bandwidth and buffer control Orange (Online range) control algorithm - adaptive online control for service differentiation - to provide a ‘better effort’ service for class II traffics while ensuring QoS for the admission controlled class I serv

ices


Online Control Algorithm for Internet

QoS guarantee for higher priority service - no reduction in network capacity Ability to adaptively congestion control - to maximize network performance Low complexity - practical for real network implementation Ability to respond to current network traffic conditions - for the appropriate performance balance between contradictory QoS requirements


QoS provisioning mechanism (1)

During network congestion - QoS provisioning problem is further intensified Admission control management - provide good QoS in Internet Link bandwidth is shared dynamically - between class I and class II data services - each service has different operational requirements Different admission control rules - strict admission control rule for class I data services - non-controlled admission rule for class II data services


QoS provisioning mechanism (2)

Bandwidth is partitioned by range - some part is reserved for higher priority traffic service - partition range can be movable Bandwidth range (RESb) for reservation - adaptive adjustment by traffic window online computational problem Admission decisions for class I traffic services - controlled by the moving range : get the benefit from reservations for QoS guarantees


Congestion control mechanism (1)

On-line control for network congestion : unable to optimally control the network congestion

exactly try to close to optimal network performance - responsive to current traffic changes in link

loads - adaptive balance between traffic history

and recent traffic changes Dropping packet rate - provide feedback information : the congestion level of the gateways through the

path



Adjusts system parameters - in adaptive online fashion Bandwidth reservation range (RESb) Queue range (Qr) - unused reserved bandwidth can be temporarily allocated for buffered class II service - same as the RESb to maximize network performance Packet marking probability (Mp) - decided proportional to the current queue length - adaptively characterized by threshold values



If L < Qr - congestion free : no arriving packets are dropped L > T - all arriving class II data packets are dropped Qr < L < T - class II data packets can be marked with probability Packet marking probability Mp

- L : current queue len

gth - T : maximum buffer

sizeT

LM p1

r

rp2 QT

QLM



Recent traffic patterns reflect effectively the current condition - during recent unit_time [ tc - unit_time, tc] Traffic management in next interval - adaptively control packets during [tc, tc + unit_time] L < Qr - packet queuing rate (Ip_r) in current interval : packet incoming rate - packet clearing rate if (T – Qr ) < Ip_r then Mp1 Qr < L < T if (0 < Ip_r ) then Mp2

if (Ip_r < 0) & | Ip_r | > (L – Qr) then no packet drop


Online Control Practical Applications Dynamic QoS priority control in multimedia networks - call priority can be changed based on online requests an

d current network conditions Main concept of this dissertation integrated online approach based on real-time measurem

ent - develop other adaptive control algorithms - inter-process communication, disk and memory file and I/O systems, CPU scheduling, power control, distributed operating system


Concluding Remarks

QoS guarantee for higher priority service - no reduction in network capacity Ability to adaptively congestion control - to maximize network performance Low complexity - practical for real network implementation Ability to respond to current network traffic conditions - for the appropriate performance balance between contradictory QoS requirements

online service management algorithm for cellular/waln multimedia networks

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