Download Online Service Management Algorithm for Cellular/WLAN

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

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
2
Internet Communication & Control Lab.
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
3
Internet Communication & Control Lab.
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
4
Internet Communication & Control Lab.
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
ResB =
 (B  N
i
i
)
i  Wclass _ I
5
Internet Communication & Control Lab.
requested hand off service
group I
reservation
pool
real time data
multimedia
data type
group II
reservation
pool
non-real time data
unit_time
t0  t win _ I
t0  t win _ II
current_time (
Group II Time Window (
Group I Time Window (
6
t
win_I
t
win_II
t0
)
)
)
Internet Communication & Control Lab.
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
7
Internet Communication & Control Lab.
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
8
Internet Communication & Control Lab.
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
9
Internet Communication & Control Lab.
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
10
Internet Communication & Control Lab.
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
11
Internet Communication & Control Lab.
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
12
Internet Communication & Control Lab.
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
13
Internet Communication & Control Lab.
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
M p2 
14
L  Qr
T  Qr
- L : current queue length
- T : maximum buffer size
Internet Communication & Control Lab.
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
15
Internet Communication & Control Lab.
Simulation Results
1
1
0.9
Our Framework
RMI Scheme
ALBCA Scheme
0.7
0.6
0.5
0.4
0.3
0.7
0.6
0.5
0.4
0.3
0.2
0.2
0.1
0.1
0
0
0.5
1
1.5
2
Offered Load (Call Arrival Rate)
2.5
3
Fig.1 Call Blocking Probability
16
Our Framework
RMI Scheme
ALBCA Scheme
0.8
Call Dropping Probability
Call Blocking Probability
0.8
0.9
0
0
0.5
1
1.5
2
Offered Load (Call Arrival Rate)
2.5
3
Fig.2 Call Dropping Probability
Internet Communication & Control Lab.
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
17
Internet Communication & Control Lab.
Internet Communication Control (ICC)
Research Lab.
.
Prof. Sungwook Kim
18
Internet Communication & Control Lab.
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
19
Internet Communication & Control Lab.
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
20
Internet Communication & Control Lab.
Bandwidth Reservation (2)
requested hand off services
Wset_F
real time data
multimedia
data type
non-real time data
unit_time
t c - t win _ FT
Time Window ( t win _ FT
21
current_time ( t C )
)
Internet Communication & Control Lab.
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 amount is estimated dynamically
- the sum of requested bandwidth by class I calls
during the traffic window
ResB =
 (B  N
i
i
)
i  Wclass _ I
22
Internet Communication & Control Lab.
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
 (B  N
ResB =
i
i
)
i  Wclass _ I
ABlink
RBlink  UBlink
=
MABpath(i,j) =
min
linkpath( i , j )
23
( ABlink )
Internet Communication & Control Lab.
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
24
Internet Communication & Control Lab.
Call Admission Control (2)

For a new class I request,
- a probing packet estimates the available network bandwidth
1
SR bits/sec ( = BU ×
) ≥ ATi bits/sec
ODTcurrent

For a new class II request,
- a probing packet only estimates the unused network bandwidth
1
SR bits/sec ( = BU ×
) ≥ M_ATj bits/sec
ODTcurrent

Guarantee QoS for class I data traffic services
25
Internet Communication & Control Lab.
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
26
Internet Communication & Control Lab.
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
27
Internet Communication & Control Lab.
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
28
Internet Communication & Control Lab.
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
29
Internet Communication & Control Lab.
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
30
Internet Communication & Control Lab.
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
31
Internet Communication & Control Lab.
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
32
Internet Communication & Control Lab.
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
33
Internet Communication & Control Lab.
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
34
Internet Communication & Control Lab.
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 performance
: for class II data services
Integrated online approach
- both mechanisms act cooperatively
: in order to simultaneously satisfy the conflicting requirements
35
Internet Communication & Control Lab.
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
36
Internet Communication & Control Lab.
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
37
Internet Communication & Control Lab.
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
38
Internet Communication & Control Lab.
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
39
Internet Communication & Control Lab.
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
40
Internet Communication & Control Lab.
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
41
Internet Communication & Control Lab.
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
M p1 
42
L
T
M p2 
L  Qr
T  Qr
- L : current queue length
- T : maximum buffer size
Internet Communication & Control Lab.
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) then no packet drop
43
Internet Communication & Control Lab.
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
44
Internet Communication & Control Lab.
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
45
Internet Communication & Control Lab.