online service management algorithm for cellular waln multimedia networks n.
Download
Skip this Video
Loading SlideShow in 5 Seconds..
Online Service Management Algorithm for Cellular/WALN Multimedia Networks PowerPoint Presentation
Download Presentation
Online Service Management Algorithm for Cellular/WALN Multimedia Networks

Loading in 2 Seconds...

play fullscreen
1 / 45

Online Service Management Algorithm for Cellular/WALN Multimedia Networks - PowerPoint PPT Presentation


  • 103 Views
  • Uploaded on

Online Service Management Algorithm for Cellular/WALN Multimedia Networks. SOFSEM 2007 Sungwook Kim Sogang University Department of Computer Science Seoul, South Korea. Introduction. Efficient network resource management - key to enhance network performance & QoS

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'Online Service Management Algorithm for Cellular/WALN Multimedia Networks' - onslow


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
online service management algorithm for cellular waln multimedia networks

Online Service Management Algorithmfor Cellular/WALN Multimedia Networks

SOFSEM 2007

Sungwook Kim

Sogang University

Department of Computer Science

Seoul, South Korea

introduction
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 performance

online algorithm
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 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
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 amountis estimated dynamically

- the sum of requested bandwidth by class I calls

during the traffic window

buffer management
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
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
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
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 notification

  • Major problem

- queue length variation for bursty traffic changes

: difficult to control temporal traffic fluctuations

blue algorithm 2
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
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 services

orange online range algorithm 2
Orange (Online range) Algorithm (2)
  • 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

orange online range algorithm 3
Orange (Online range) Algorithm (3)
  • 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 length

- T : maximum buffer size

simulation model
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
Simulation Results

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

concluding remarks
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

slide18

.

Internet Communication Control (ICC) Research Lab.Prof. Sungwook Kim

internet
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 services

: growing interest in Internet QoS

bandwidth reservation 1
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

bandwidth reservation 3
Bandwidth Reservation (3)
  • 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 amountis estimated dynamically

- the sum of requested bandwidth by class I calls

during the traffic window

online management for internet
Online management for Internet
  • 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

ABlink =

MABpath(i,j)=

call admission control 1
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
Call Admission Control (2)
  • For a new class I request,

- a probing packet estimates the available network bandwidth

SRbits/sec ( = BU ×)≥ ATi bits/sec

  • For a new class II request,

- a probing packet only estimates the unused network bandwidth

SRbits/sec ( = BU ×)≥ M_ATj bits/sec

  • Guarantee QoS for class I data traffic services
internet1
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
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 reservations

- reservations have to be refreshed periodically

  • Major problem

 Complexity & Scalability

- router has to keep state information on all reservations

diffserv model
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 services

: growing interest in Internet QoS

aqm algorithms
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 11
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 21
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 11
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 notification

  • Major problem

- queue length variation for bursty traffic changes

: difficult to control temporal traffic fluctuations

blue algorithm 21
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
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 conditions

  • Online algorithm based on DiffServ model

- provides QoS guarantees for higher priority calls

while accommodating as many call connections as possible

multimedia internet management
Multimedia Internet Management
  • Online management algorithm

 the QoS provisioning mechanism

- guarantee QoS based on call admission control

: for class Idata service

 the congestion control mechanism

- adaptive bandwidth allocation for higher network performance

: for class II data services

  • Integrated online approach

- both mechanisms act cooperatively

: in order to simultaneously satisfy the conflicting requirements

orange online range algorithm
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 services

online control algorithm for internet
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
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
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
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

congestion control mechanism 2
Congestion control mechanism (2)
  • 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

congestion control mechanism 3
Congestion control mechanism (3)
  • 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 length

- T : maximum buffer size

congestion control mechanism 4
Congestion control mechanism (4)
  • 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) thenno packet drop

online control practical applications
Online Control Practical Applications
  • Dynamic QoS priority control in multimedia networks

- call priority can be changed based on online requests and

current network conditions

  • Main concept of this dissertation

 integrated online approach based on real-time measurement

- develop other adaptive control algorithms

- inter-process communication, disk and memory

file and I/O systems, CPU scheduling, power control,

distributed operating system

concluding remarks1
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