Klicken Sie, =
um die
Formate des Vorlagentextes zu bearbeiten
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Dritte Ebene=
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Fünfte =
Ebene
Klicken Sie, um das Titelformat zu
bearbeiten
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D. Hetzer 2005
Seite ‹Nr.›&#=
13;
QORE<=
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D. Hetzer 2005
Seite 1
QORE=
QoS and Resource Optimisation in the Internet
T-Systems, TZD
Head of Department Media and Voice Solution,
Dirk Hetzer
Berlin 15/06/05
=3D=
=3D=3D=3D=3D=3D!"§=
;=3D=3DSystems=3D
D. Hetzer 2005
Seite 2
QORE
Struct=
ure
=
=
n Motivation
<=
/span>
n Introduction
n QoS provision in Internet
n Internet Bandwidth Allocation
=
;
n Advance Resource Reservation
<=
/span>
n Optimisation Techniques
n Measurement Based QoS and Resource
planning Framework
n Conclusion and Challenges for Futu=
re
Work
=3D=
=3D=3D=3D=3D=3D!"§=
;=3D=3DSystems=3D
D. Hetzer 2005
Seite 3
QORE
Requests for next gener=
ation
networks fix/mobil
<=
span
style=3D'font-family:Tele-GroteskNor'>n QoS All-IP Network (QoS Signalling, IPv6,
Intser/DiffServ Basis)
<=
span
style=3D'font-family:Tele-GroteskNor'>n Middelware Plattform for mobile
Applications ( open API’s)
Motiva=
tion
=3D=
=3D=3D=3D=3D=3D!"§=
;=3D=3DSystems=3D
D. Hetzer 2005
Seite 4
QORE
![](3D"slide0168_image022.gif")
HDR Access
Cellular
HAPS
Satellite
ITS
System Handover
System Handover
System Handover
SkyNet
System Handover
Switch
基地ì=
16;
<=
/span>
Broadba=
nd Fixed Backbone Networks
QORE
Motivation
=
=
Network Components in 4.
Generation
=3D=
=3D=3D=3D=3D=3D!"§=
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D. Hetzer 2005
Seite 5
QORE
![](3D"slide0175_image028.gif")
=
=
• =
Intera=
ctive
user terminal (vs. burglar alarm or webcam)
• Personal
terminal (vs. car, office, home)
• "Real
reality" (vs. virtual reality)
• 100 kbits
everywhere (vs. 100Mbits somewhere)
• Several vi=
sual
input types (one-handed, pen, keyboard)
• Multiservi=
ce
terminals (PS+CS, purse, GPS, FM radio, …)
• Extensions=
to
cellular (WLAN, BlueTooth, …)
Tren=
ds
mobile Communication 1(2)
=3D=
=3D=3D=3D=3D=3D!"§=
;=3D=3DSystems=3D
D. Hetzer 2005
Seite 6
QORE
Isolated service sets
<=
span
style=3D'mso-special-format:bullet;position:absolute;left:-2.04%'>•<=
/span> sequential service for cellular (p=
hone,
fax, …) and IP (WAP, Web, email, ...)
<=
span
style=3D'mso-special-format:bullet;position:absolute;left:-2.04%'>•<=
/span> terminal: unified user interface,
isolated stacks
<=
span
style=3D'mso-special-format:bullet;position:absolute;left:-2.04%'>•<=
/span> infra: radio capacity sharing,
user-initiated IP connectivity, cellular push
•
Integrated service sets
<=
span
style=3D'mso-special-format:bullet;position:absolute;left:-2.04%'>•<=
/span> parallel service for cellular and =
IP
(e.g. WAP and GSM voice)
<=
span
style=3D'mso-special-format:bullet;position:absolute;left:-2.04%'>•<=
/span> integrated service for cellular an=
d IP
(e.g. WTA and GSM voice)
<=
span
style=3D'mso-special-format:bullet;position:absolute;left:-2.04%'>•<=
/span> terminal: "pre all-IP" u=
ser
interface
<=
span
style=3D'mso-special-format:bullet;position:absolute;left:-2.04%'>•<=
/span> infra: radio coordination, PS cont=
rol
plane for CS user plane
•
All-IP service set
<=
span
style=3D'mso-special-format:bullet;position:absolute;left:-2.04%'>•<=
/span> IP traff=
ic
evolution: bursty + streaming + real-time voice&video
• key
requirements: fast session set-up, push, QoS (handover, delay, jitter)
Tren=
ds mobile Communication 2(2)
=3D=
=3D=3D=3D=3D=3D!"§=
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D. Hetzer 2005
Seite 7
QORE
Motivation – need for QoS optimisation derived
from:
§QoS tuning and adaptation for application and
traffic classes
§QoS/SLAs specification and monitoring of
applications or traffic QoS parameters
•QoS based
adaptive resource control for different traffic classes
•Bandwidth
Brokerage (BB) for QoS and resource mapping
•DiffServ
traffic classes and corresponding SLAs and QoS requirements
=3D=
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D. Hetzer 2005
Seite 8
QORE
Usage areas for QoS and resource optimisation
=
=
<=
span
style=3D'font-family:Tele-GroteskNor;font-size:83%'>•Operational performance management
•-> Optimisation of resource allocation for QoS enabled
applications and Value added Virtual Private Networks at router access poi=
nts
(border router, IXP, peering points,
core´=
and edge router)
<=
span
style=3D'font-family:Tele-GroteskNor;font-size:83%;visibility:hidden'>•<=
/span>
<=
span
style=3D'font-family:Tele-GroteskNor;font-size:83%'>•Network planning
•-> Simulation of different strategies for
resource reservation based on operation research techniques considering different ki=
nds
of QoS requirements (elastic application levels, cost restrictions, delay=
and
timing dependencies, intelligent mobile components)
=3D=
=3D=3D=3D=3D=3D!"§=
;=3D=3DSystems=3D
D. Hetzer 2005
Seite 9
QORE
![](3D"slide0225_image029.gif")
=
<=
span
style=3D'font-family:Tele-GroteskNor;font-size:91%'>nOptimal resource reser=
vation
in advance
nrequired =
for QoS
based applications to improv=
e QoS provision and Internet bandwi=
dth usage
=
;
<=
span
style=3D'mso-special-format:bullet;color:#FF0066;mso-color-index:3;positio=
n:
absolute;left:-3.88%;top:.3em;font-family:Wingdings;font-size:75%'>nkeypoint =
of QoS
and resource planning in Internet
nApproach
(1) Goal: end-to-end
QoS planning and bandwidth optimisation
(2) Integrat=
ion of bandwidth
optimisation techniques for resource reservation in advance w=
ith QoS monitoring and analysis
technologies
-> promptly reactio=
n to
changes of QoS due topology and traffic
-> planning with fe=
edback
from QoS parameter measurement data
Theoretical background - the focus of presentation <=
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QoS
resource reservation in advance
=3D=
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D. Hetzer 2005
Seite 10
QORE
Struct=
ure
=
=
n Motivation
n QoS and Resource Optimisation Goals&=
#13;
n QORE Optimisation Issues
n QORE architectural design and
scenarios
n Ongoing Work – Intermon
=
n Summary
n
=3D=
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D. Hetzer 2005
Seite 11
QORE
![](3D"slide0144_image030.gif")
Network
Planning and QoS Optimisation
=
=
Optimisation of QoS and resource reservation levels of
elastic applications i.e. applications with different levels of QoS support and resourc=
e requirements
<=
/div>
=
=
Elasicity and
Rate type
=
=
Descriptions
=
=
Stream
=
=
Predictable delivery at a
relatively constant bit rate (CBR). For example, although their rates often fluctuat=
e,
audio and video data streams are considered CBR because they have=
a
quantifiable upper boundary.
=
=
Burst
=
=
Unpredictable delivery of
"blocks" of data at a variable bit rate (VBR). Applications like file transfer move data in
bulk that can increase data rate to use all available bandwidth (no upper
bound).
=3D=
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Seite 12
QORE
Operat=
ive
Performance Management and Optimisation
=
=
=
=
QoS and resource optimisation combined with operative
control
=
=
• effective
bandwidth
•<=
/span> management of bandwidth (Bandwidth
Brokers)
•<=
/span> measurement based admission contro=
l
•<=
/span> traffic and QoS=
model
predictions
=
=3D=
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D. Hetzer 2005
Seite 13
QORE
![](3D"slide0226_image031.gif")
nQoS - collective measure of the level of service
delivered to the customer
nQoS standardisation
nIETF - Integrated Services
(IntServ) of the Internet Engineering Task Force (IETF) is a significant contributi=
on to
providing controlled QoS for multimedia applications [RFC 1633]
nISO - QoS support for open systems interconnection (OSI)
communications
nCORBA emerging
distributed object-oriented technology d for managing the QoS of distributed
multimedia services and applications
<=
/div>
QoS
provision in Internet (1)
=3D=
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Seite 14
QORE
![](3D"slide0227_image032.gif")
<=
span
class=3DBB style=3D'position:absolute;left:-3.42%;top:.3em'>nQoS provisioning
technologies in Internet used separately and in combination
nIntegrated Services and Resource
Reservation Protocol (RSVP) -> resource reservation before data are trans-mi=
tted,
the applications must first set up paths and reserve resources using RSVP
(signaling protocol for setting up paths and reserving resources)
nDifferentiated Services (DS) -> packets are marked differen=
tly to
create several packet
classes and thus receive different services.
nQoS based (constraint-based) routing=
->
find routes that are subject to some QoS constraints, such as bandwidth or delay requirement.&=
#13;
nBandwidth Overprovisioning (BO)
->provision of more bandwidth
QoS
provision in Internet (2)
=3D=
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D. Hetzer 2005
Seite 15
QORE
![](3D"slide0228_image033.gif")
=
nQoS parameter
=
nMeasurement
methodology in Internet (RFC2330)
n=
sampling
techniques, clock accuracy, resolution and skew, wire time ver=
sus host
time, error analysis
=
nQoS
Measurement architectures
=
nactive
and passive measurement of QoS parameters
=
nintra-domain
and inter-domain performance monitoring tools,
=
nmeasurement
data base
n=
integration
of tools for topology discovery and traffic measurement to
detect performance problems
=
nintegration
of modelling, pattern analysis and simulation tools
n Examples:
AQUILA DMA, INTERMON QoS measurement architect=
ures
![](3D"slide0228_image034.gif")
QoS
feedback by measurement data base&=
nbsp;
QoS
provision in Internet (3)
=3D=
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D. Hetzer 2005
Seite 16
QORE
![](3D"slide0230_image035.gif")
network topology by controlling the network tra= ffic in order to satisfy performance
objectives of application service (SLA, QoS), taking into account the performance
and/or QoS that can be provided by the network.
measurements, forecasting, planning, dimensioning, control and perfor-
mance monitoring of the traffic.
and scienti= fic principles to the measurement, characterization, modeling,
and control of Internet traffic”
Gateway Protocols (IGP) IS-IS [LS 01] and OSPF = [KYK 01], and to signaling
protocols RSVP [RFC 3209] and LDP [RFC 3212].
consisting of multi-vendor equipment.
Traffic Engineering
QoS
provision in Internet (4)
=3D=
=3D=3D=3D=3D=3D!"§=
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Seite 17
QORE
![](3D"slide0231_image036.gif")
Traffic Engineering - functional comp=
onents
![](3D"slide0231_image038.gif")
QoS
provision in Internet (5)
=3D=
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Seite 18
QORE
![](3D"slide0232_image039.gif")
nAdmission
control is a methodology,
whether or not new flows requesting resources can be accepted while
maintaining QoS.
<=
nobr>nAdmission
control algorithms cf. Knightly and Shroff :
·Average and peak rate
combinatorics
·Additive effective
bandwidths
·Engineering the ”l=
oss
curve”
·Maximum variance approac=
hes
Admission Control
QoS
provision in Internet (6)
=3D=
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Seite 19
QORE
![](3D"slide0233_image040.gif")
=
nImmediate reservation (for instance RSVP) a=
re
requested and granted just when
the resources= are actually needed -> Versus:
the resources= are actually needed -> Versus:
nAdvance Reservation, i.e. resources are reserved in advance to =
the
actual usage
phase -> Benefits:
phase -> Benefits:
=
nFlexible handling of
applications by decoupling the starting time of the service
from the time the servi= ce request is made
from the time the servi= ce request is made
Objective and benefits
=
Reso=
urce
Allocation in Advance (1)
=3D=
=3D=3D=3D=3D=3D!"§=
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D. Hetzer 2005
Seite 20
QORE
![](3D"slide0234_image041.gif")
=
=
nBandwidth reservation for Grid Computing :
Advance Reservation API ->
Scheduling Working Group
Scheduling Working Group
nScheduling for multimedia applications and
performance critical systems ->
video on demand, distributed multimedia deliveryQoS
![](3D"slide0235_image042.gif")
n ReRA (Resource Reservation in Advance)
=
n Differentiated Services Bandwidth Broker
n Admission control
n Network calculus
n Effective bandwidth
![](3D"slide0235_image043.gif")
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QoS
QoS
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QoS
![](3D"slide0147_image044.gif")
![](3D"slide0147_image045.gif")
QoS
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QoS
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QoS
![](3D"slide0237_image046.gif")
n Find one route for each demand in a
network, so that the bandwidth requirements of
dem= ands are satisfied within resource capacities of the bidirecional links of the = network
QoS
![](3D"slide0238_image048.gif")
video on demand, distributed multimedia delivery
nHard real time and embedded System applicat=
ions
(Worst Case delay
guarantee)
guarantee)
nQoS based Virtual private networks ->
adaptable resource reservation for
optimal QoS provision &= #13;
optimal QoS provision &= #13;
nMobile and wireless services -> reservat=
ion
in advance
to continuously keep
the communication service alive
the communication service alive
nBest effort and elastic applications
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n
<=
/div>
Application requirements
=
Reso=
urce
Allocation in Advance (2)
=3D=
=3D=3D=3D=3D=3D!"§=
;=3D=3DSystems=3D
D. Hetzer 2005
Seite 21
QORE
nProtocols
=
o=
on top
of RSVP: evaluation and selection of an appropriate call admission control strategy, internal
management of advance reservations
<=
span
style=3D'font-family:Tele-GroteskNor'>o=
mapping
to ATM
nTheoretical models
nFurther reading : Theories and Mo=
dels
for Internet Quality of Service -> Victor Firoiu, Jean=
-Yves
Le Boudec, Don Towsley, and Zhi-Li Zhang
<=
/div>
Internet Protocols and background=
Reso=
urce
Allocation in Advance (3)
=3D=
=3D=3D=3D=3D=3D!"§=
;=3D=3DSystems=3D
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Seite 22
QORE
Operation research methods for QoS and resource optimistaion
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=
=
=
Goal function
optimal QoS level of
applications and cost efficient resource assignment
Communication jobs with restrictions on
<=
span
style=3D'font-family:Tele-GroteskNor'>•Durations (time characteristics and synchronisation),
=
<=
span
style=3D'font-family:Tele-GroteskNor'>•Multiplexing
<=
span
style=3D'font-family:Tele-GroteskNor'>•Timing dependencies
<=
span
style=3D'font-family:Tele-GroteskNor'>•levels of QoS and resource reservation
<=
span
style=3D'font-family:Tele-GroteskNor'>•cost
=3D=
=3D=3D=3D=3D=3D!"§=
;=3D=3DSystems=3D
D. Hetzer 2005
Seite 23
QORE
Application of
optimisation techniques and algorithms for QoS and resource optimisation
=
=
=
Use of different operation research
methods to solve the problem=
=
of optimal resource assignment of QoS enabled applications=
=
considering specific
resource and QoS restrictions of communication jobs
•<=
/span>Assignment problem,
•<=
/span>Critical path method,
•<=
/span>Max flow problem,
•<=
/span>Shortest path,
•<=
/span>Transportation, cost flow problems
=3D=
=3D=3D=3D=3D=3D!"§=
;=3D=3DSystems=3D
D. Hetzer 2005
Seite 24
QORE
Timi=
ng and
cost restrictions
=
=
=
=
Core router 1
=
=
Stream A
=
=
Stream A
=
=
Core router 2
=
=
Cost A
=
=
Cost B
=
=
Cost D
=
=
Cost C
=
=
Framework
- =
;
IP based accounting and QoS based routing RFC 2386
-
Cost dependency of timely usage of resources
=
=
Cost vector
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-
characterising resource requirements of QoS based applicat=
ion
-
Minimisation of cost based resource usage
Cost A: Cost A1, Cost
A2,…… Cost An dependent on the period T1, T2, …. Tn<=
/span>
Cost B: Cost B1, Cost
B2,…… Cost Bn dependent on the period T1, T2, …. Tn<=
/span>
Cost C: Cost C1, Cost
C2,…… Cost Cn dependent on the period T1, T2, …. Tn<=
/span>
Cost D: Cost D1, Cost
D2,…… Cost Dn dependent on the period T1, T2, …. Tn<=
/span>
=
=
=3D=
=3D=3D=3D=3D=3D!"§=
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Seite 25
QORE
Struct=
ure
=
=
n Motivation
n Goals
=
;
n QORE Optimisation Issues
n QORE
Architectural Issues
n Ongoing W=
ork
– Intermon
n Summary&#=
13;
n<=
/span>
=3D=
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Seite 26
QORE
Optimisation consider=
ing
timing requirements
=
=
Optimal resource assignment for QoS based applications with
different time requirements
Time requirements of multimedia and
distributed application:
•same start, end and duration: multiplexed
multimedia applications – multimedia conferences
<=
span
style=3D'font-family:Tele-GroteskNor;font-size:83%'>•start / end&nb=
sp;
/ duration relationships: distributed applications
<=
span
style=3D'font-family:Tele-GroteskNor;font-size:83%'>•flexible start time and same end time: time
dependent content applications
<=
span
style=3D'font-family:Tele-GroteskNor;font-size:83%'>•flexible start / end: VoIP, data transfer
<=
span
style=3D'font-family:Tele-GroteskNor;font-size:83%'>•flexible start /fixed end: multimedia
synchronisation
=3D=
=3D=3D=3D=3D=3D!"§=
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Seite 27
QORE
QORE Optimisation -- Goal Function
=
=
Maximi=
sation
of the total efficiency metric E as sum of efficiency metric of particular
jobs
fficiency metric is defined to consider QoS,
resource levels and cost vector requirements of jobs (applications)
=
=
E =3D Sum of Ei ->max i=3D 1…..N
Where Ei =3D (Di x Qi) / (Ci x Ri)
Ei – efficiency metric of job i
Di – duration of job i
Qi – QoS level of job i
Ci – Cost level of job i
Ri – Resource level of job i
N – Number of jobs
Where the global resource restriction GR is
fulfilled
- R =3D Sum of (Ri x
Di) < =3D GR
=3D=
=3D=3D=3D=3D=3D!"§=
;=3D=3DSystems=3D
D. Hetzer 2005
Seite 28
QORE
=
<=
span
style=3D'font-family:Tele-GroteskNor;font-size:91%;color:red'>nBandwidth pa=
cking
problem
nassigning calls to paths in a capaci=
tated
graph, such that capacities are not violated
and= the total profit is maximized.
and= the total profit is maximized.
nUsage of Tabu Search Method with
adaptation of k-shortest path
<=
span
style=3D'font-family:Tele-GroteskNor;font-size:91%;color:red'>nOptimal Reso=
urce
Allocation In Networks with routing (RAIN)
dem= ands are satisfied within resource capacities of the bidirecional links of the = network
nNP-hard for demands subject to multip=
le
additive or multiplicative QoS criteria
<=
span
style=3D'font-family:Tele-GroteskNor;font-size:91%;color:red'>nKnapsack
problem
ncapacity of edge corresponds to the =
size
of knapsack and the calls correspond to
obj= ects to be packed in the knapsack -> NP-hard
obj= ects to be packed in the knapsack -> NP-hard
<=
span
style=3D'font-family:Tele-GroteskNor;font-size:91%;color:red'>n Call Admission Control
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nBCA (Batch Call Admissio=
n):
all calls request to be established at the same time.
In particular, BCA includes the ca= se where all call instances have the same starting-time.If alternatives are not allowed, BCA=
is
equivalent to the unsplittable flow problem
In particular, BCA includes the ca= se where all call instances have the same starting-time.
nGCA (general call admiss=
ion),
every request specifies a starting time and duration.
nGCA for Advance Reserva=
tion
Requests with Alternatives - T. Erlebach ETH Zürich
Operation research problems for Optimal Resource Allocation
Opti=
misation
techniques (1)
=3D=
=3D=3D=3D=3D=3D!"§=
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Seite 29
QORE
=
nScheduling problem for allocating scarce resources to activit=
ies
over a period
of time.
of time.
=
=
na discrete optimization
problem and therefore very hard to solve in practice
(most of the scheduling problems are NP-Hard).QoS
![](3D"slide0239_image050.gif")
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QoS
![](3D"slide0240_image052.gif")
![](3D"slide0240_image053.gif")
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nMost general of the optimization approac=
hes
<=
nobr>ncan solve the broadest class of
problems, muliple objective optimisation
<=
nobr>nDivided into stages with a decision required at each
stage.
nBenefits for Resource and QoS planning<=
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(most of the scheduling problems are NP-Hard).
=
=
nEfficient solutions including Mixed Integer Linear
Programming,
Branch-and-Bound
Branch-and-Bound
nConstraint satisfaction problem
![](3D"slide0238_image049.gif")
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=
ndefined by a triple (X=
, D,
C), where X =3D {x1,, ..., xn} is a set of variables, D =3D
{D1,, ..., Dn} a set of finite domains associated with the variables,= and C =3D {C1,, ..., Cm} a set of constraints
{D1,, ..., Dn} a set of finite domains associated with the variables,= and C =3D {C1,, ..., Cm} a set of constraints
=
=
nThe question is whether
there exists an assignment of values to variables,
so that all the constraints are satisfied
so that all the constraints are satisfied
=
=
nUsing constraints to r=
educe
the computational effort needed to solve com-
binatorial problems <= /div>
binatorial problems <= /div>
Operation research problems for Optimal Resource Allocation
Opti=
misation
techniques (2)
=3D=
=3D=3D=3D=3D=3D!"§=
;=3D=3DSystems=3D
D. Hetzer 2005
Seite 30
QORE
=
nIntelligent allocation based on scheduling heuris=
tics
n scheduling heuristics according request classes
n nonlinear function optimisation problems
n problem can be expressed as such that natural evolution&#=
13;
n the broad applicability of GA techniques, a broad applica=
tion
domain
=
nbandwidth allocations to the requests depending on their
class, relative
wor= ths of their priority levels, and the network capacities available for that
per= iod
wor= ths of their priority levels, and the network capacities available for that
per= iod
nEvolutionary programming and Genetic Algorithms
(GA)
<=
span
style=3D'font-family:Tele-GroteskNor'>nprovide an attractive paradigm for implementing general
nonlinear searches
n
Artificial intelligence approaches for Optimal Resource <=
/span>Alloc=
ation
Opti=
misation
techniques (3)
=3D=
=3D=3D=3D=3D=3D!"§=
;=3D=3DSystems=3D
D. Hetzer 2005
Seite 31
QORE
=
nCongestion pricing models for bandwidth allocation
=
npropose
a traditional economic solution to the problem of heterogeneous
demand
=
nthe
collection of network resources is treated as a market, and price <=
span
style=3D'position:absolute;top:38.75%;left:17.22%;width:73.78%;height:4.25=
%'>their
use accordingly
=
napplications
of auction theory, fixed rate pricing mechanisms, game theory
nBandwidth Allocation by Using Generalized Vickrey Auction
=
nA generali=
zed
Vickrey auction (GVA) is known as an effctive
auction protocol for allocating multiple items and multiple units in a distributed manner
<=
span
style=3D'font-size:85%;visibility:hidden'>n
Market bas=
ed
approaches
Opti=
misation
techniques (4)
=3D=
=3D=3D=3D=3D=3D!"§=
;=3D=3DSystems=3D
D. Hetzer 2005
Seite 32
QORE
=
<=
span
style=3D'mso-special-format:bullet;color:#FF0066;mso-color-index:3;positio=
n:
absolute;left:-3.96%;top:.3em;font-family:Wingdings;font-size:75%'>nEach
stage has a number of states associated with it.
<=
span
style=3D'mso-special-format:bullet;color:#FF0066;mso-color-index:3;positio=
n:
absolute;left:-3.35%;top:.3em;font-family:Wingdings;font-size:75%'>nThe
decision at one stage transforms one state into a state in the next stage.
=
nGiven
the current state, the optimal decision for each of the remaining states <=
/span>does
not depend on the previous states or decisions.
=
nEfficiency – based on Heuristics which could be applied=
the
optimal solution could be calcula=
ted in
smaller time
=
nFlexible to support different optimisation goals based on the
definition of reward function =
and
policies
<=
span
style=3D'mso-special-format:bullet;color:#FF0066;mso-color-index:3;positio=
n:
absolute;left:-3.96%;top:.3em;font-family:Wingdings;font-size:75%'>nAdaptable to the environment using Reinforment Learning
=
Neural
dynamic programming framework
Meas=
urement
based Resource and QoS Planning (1)
=3D=
=3D=3D=3D=3D=3D!"§=
;=3D=3DSystems=3D
D. Hetzer 2005
Seite 33
QORE
=
nMachine learning theory that derives its roots from control principles
effectively used to solve
problems with in communication networks
=
nLearning agent=
has
to formulate a policy, which determines the appropriate action to take in each state in order to maximize the expected
cumulative reward over time
=
nThe reward is
derived from how favorable the outcome is of the action taken by the agent=
in a particular state
nControl mechanism to change the planned
optimal bandwidth allocation considering actual QoS analysis in an operational environment<=
/span>
=
nI=
n case
of QoS congestion, the current optimal advance allocation plan is adapted =
(recalculated) with a strategy for la=
ter
allocation of the advance resource reservation
ReinforcementLearning for adaptable bandwidth
planning using measurements
Meas=
urement
based Resource and QoS Planning (2)
=3D=
=3D=3D=3D=3D=3D!"§=
;=3D=3DSystems=3D
D. Hetzer 2005
Seite 34
QORE
Integration of Neuro-dynamic framework with QoS monitori=
ng data base
Meas=
urement
based Resource and QoS Planning (3)
=3D=
=3D=3D=3D=3D=3D!"§=
;=3D=3DSystems=3D
D. Hetzer 2005
Seite 35
QORE
Bandwidth optimisation with feedback from monitoring and =
analysis of QoS,=
traffic
and topology
=
=
nData base of QoS measurement <=
/span>and topology tools of INTERMON provide end-to-end information per =
required time scale for QoS and resource planning
<=
/div>
nIntegration of INTERMON data <=
/span>base in a reinforcement learning =
strategy for QoS and resource =
planning of inter-domain =
connections
Meas=
urement
based Resource and QoS Planning (4)
=3D=
=3D=3D=3D=3D=3D!"§=
;=3D=3DSystems=3D
D. Hetzer 2005
Seite 36
QORE
QORE system for bandwidth optimisation based on QoS monitoring and m=
odelling
data - design
=
=
nBandwidth optimisation <=
span
style=3D'font-size:91%'>strategies for different kind of applications =
considering Grid, VoIP and streaming multimedia
nOptimisation with feedback from QoS measurement and patterns
Meas=
urement
based Resource and QoS Planning (5)
=3D=
=3D=3D=3D=3D=3D!"§=
;=3D=3DSystems=3D
D. Hetzer 2005
Seite 37
QORE
QORE system for bandwidth optimisation based on QoS monitoring and m=
odelling
data - resource reservation
requests
=
nCost efficient resource assignment <=
span
style=3D'position:absolute;top:33.75%;left:6.17%;width:44.0%;height:3.75%'=
>considering application characteristics and QoS patterns for feedback
nApplication characterised by resource reservation requests:
=
nDurations (time
characteristics and synchr=
onisation),
<=
span
style=3D'font-size:85%'>nMultiplexing &=
#13;
=
nResource reser=
vation
alternatives based =
on
different QoS layers
<=
span
style=3D'font-size:85%'>nCosts <=
span
style=3D'mso-special-format:lastCR;display:none'>
Meas=
urement
based Resource and QoS Planning (6)