1
A NEW MODEL FOR DYNAMIC QOS IN HETEROGENEOUS
NETWORKS USING ONTOLOGIES
Tarek M. Heggi1, Maryam M. Hazman
2, Fathy Aamer
3
1Ph.D. Student, Faculty of Computers and Information, Information Technology Dept., Cairo University, Egypt
2Researcher, Central Lab. for Expert Systems, Training, Evaluating, and Updating Expert Systems Dept., Egypt
3 Professor, Faculty of Computers and Information, Information Technology Dept., Cairo University, Egypt
ABSTRACT
The current network environments have two critical characteristics: heterogeneity
in networking technologies and the emerging of new applications. Since the 90’s a
lot of research efforts have been provided Quality of Services (QoS) for different
types of networks. Different visions are provided as: Protocol-based QoS, models,
and Ontologies. Protocol-based QoS are not able to operate as a standalone QoS
mechanism but they can collaborate with other network elements to achieve QoS
objectives. Some of these protocols are not standards and need specific equipment
to operate. QoS models have some drawbacks like shortage in evaluation and
scalability. Ontologies-based QoS, its models focus on using QoS ontology and
SLA ontology but they don’t provide an entire vision for end to end QoS including
all other components as traffic identifications. This paper presents a survey for the
different QoS approaches and introduces a new model for providing dynamic QoS
in heterogeneous networks. This model will address challenge providing dynamic
QoS in heterogeneous networks using standards methods that can share
information using Ontologies.
Keywords: Quality of Services, Service Level Agreement, Ontologies, Traffic
Identification.
1 INTRODUCTION
As a result of the rapid development of
communication technology and the continuous
growth in Internet usage, the Quality of Services
(QoS) becomes an important research issues. One of
the most important services that need QoS is the
healthcare services. A dedicated infrastructure has
been established by many countries for this purpose
[1][2]. The existing network environments have two
critical characteristics: heterogeneity in networking
technologies and the emerging of new applications.
The heterogeneity in networking can be represented
according to the technologies that are used by the
Service Provider either wireless technologies such
as: WiMAX or WSN or mobile ad hoc or Wired
technologies such as: MPLS or Ethernet.
The new emerging applications have different
characteristics which results in different QoS
requirements. New emerging applications like,
Internet TV, P2P TV, and P2P SIP. Along with these
two network characteristics, we have different
requirements from user’s perspective to get their
satisfactions. The existing technologies tend to
convergence of multiple services such as voice,
video, multimedia, e-Commerce, and traditional data
traffic on Internet [3].Each type of traffic has its
different characteristics in terms of parameters such
as delay, jitter, and bandwidth [3].QoS plays an
important role in our lives, according to conducted
surveys in [4], 90 percent of users will not complain
about a low quality service, and they will simply
leave the provider. Two most common terms are
currently used: QoS which relies on measurement to
maintain a certain level of performance whereas
Quality of Experience (QoE) which relies on user
expectation [5]. QoS depends on measuring of
bandwidth, latency packet loss, and availability
which are considered as an objective measure.
Whereas QoE depends on measuring the level of
user satisfaction either in subjective or objective
manner [6].QoS specifications can be characterized
by quantitative such as delay and bandwidth, and
qualitative such as CPU scheduling [7]. A
heterogeneous network can be defined as collection
of devices from different manufactures; all working
together as a single unit and providing end-to-end
QoS is challenge for this type of network [8]. Each
portion of the network may implement different
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QoS techniques, guarantying the required
performances at their level [8].
QoS methods can be classified into three
categories: protocol-based, models, and ontology.
These three categories will be discussed in this
survey. This paper surveys the recent approaches that
compromise among these challenges and avail
elasticity for users to have their exact requirements.
Also, this paper present a proposed a new QoS
model to address the exiting challenges in the current
methods.
The reminding of this paper is organized as
follows. Section 2 introduces a background about the
different definitions for QoS, and its usage. Section 3
indicates the characteristics of several classes of
applications that need to deploy QoS policies.
Section 4 shows the three categories for the
deployment of QoS in different type of networks.
Section 5 proposes a new QoS model for deploying
QoS in heterogeneous networks. The final section
represents the conclusion for the different among the
current approaches and emphasis on the need for a
new model to address the challenges in the current
approaches.
2 BACKGROUND
The term QoS can be defined as the totality of
features and characteristics of a product or service
that bears on its ability to satisfy implied needs to
provide priority including: dedicated bandwidth,
controlled latency, jitter, and improved loss
characteristics[9][8].
Wireless multimedia sensor networks have
drawn extensive interested recently [10-19] because
they can be used in many areas, such as video
surveillance, traffic monitoring and health care
setting.
A multi hop wireless ad hoc network
consists of a number of nodes that form a dynamic
topology [20]. These nodes are typically limited in
resources and there is an increasing in QoS
provisioning for evolving applications [20].
One of the difficulties to apply QoS in
Mobile ad hoc networking is the frequent changes in
network topology and the lack of resources [21]. In
WSN, some parameters such as: lack of bandwidth,
memory, and processing [22] represent challenges.
Third Generation (3G) network is not able
to support full motion video in terms of QoS [23].
While, Fourth Generation (4G) network provides
subscriber with a higher bandwidth [23] that can be
used to overcome such problem.
Applying QoS in WSN can be describe
using two perspectives [24][25] which are:
application specific QoS, in which parameters of
QoS vary with the applications and Network QoS
[25], in which each class of application has common
network requirements [25].
Each layer in the OSI model has its own QoS
metrics [26]. Application layer shows the QoS of the
user application, Network layer metrics indicates the
quality of the end-to-end path, and MAC layer
metrics represented by the link parameters [26].QoS
that depends on application is required in WSN [25].
Fig. 1 show a general QoS model for network which
is redrawn from [24].
Figure 1: General QoS model for network
There are three methods to evaluate
network performance: experiment, simulation, or
analytical approach [27].
3 APPLICATION CHARACTERISTICS
There are multiple types of traffic sources:
CBR, such as PCM coded voice, which generates
traffic at 64 Kbps and VBR, such as MPEG coded
video [28]. CBR traffic can be completely described
using the peak rate of the traffic, and Average Rate is
the “long-term” mean of the traffic rate for VBR
source.
Real time applications as voice need a low
latency network path and low bandwidth [29][26].
VoIP traffic consists of two components. The first is
control signaling such as call initiation, the ringing,
and disconnect. The second component is the audio
payload which is transmitted as RTP traffic [30].
The applications accompanied with the
emerging of cloud computing have a different
characteristics [31]. Two major parameters can be
used to get user satisfaction: the application’s
perceived QoE and the appropriateness of the
application to the user’s situation [32]. QoE is
evaluated in most cases using qualitative methods
which considered as a great challenge [33][32],
Whereas QoS quantified by measuring delay, jitter
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and network throughout [32].The level of QoS
provisioning is usually based on parameters or
constraints, often known as QoS metrics [34]. QoS
metrics can be defined in different layers of the OSI
model [34]. Real time applications such as Voice and
Video need Low loss rate, Low absolute delay in two
way situations, and low variation in delay [35].
Another type of applications is based on Web
services, which can be represented by static
functional attributes and dynamic non-functional
parameters and they need QoS [36]. The following
sub-sections give a brief about different types of the
real time applications.
3.1 Voice
Skype is VoIP client that use CODEC like
G.729, and EG.711A/U. Each codec has different
values for its attributes such as the frame size and the
bit rates [37].
VoIP applications don’t require high
bandwidth. Delay and packet loss have great impact
on the perceived quality and user satisfaction [27].
Fig. 2 indicates an end-to-end path as needed for
VoIP communication [27]. A delay between 0 and
100-150 ms ensures high interactivity, while a delay
between 100-150 and 400 ms provides an acceptable
level of interactivity [27].
Figure 2: End-to-end path for VoIP
communication
3.2 Video
It is not possible to estimate the exact network
bandwidth for Video traffic CODEC, for example
CIF video resolution 352 x 288 @ 15 fps needs 384
Kbps [38].
3.3 Web Services
Several services such as: Google, MSN and
Yahoo are commonly used and they have different
requirements as QoS [39].A web service which
represents a challenge in the recent years can be
considered as autonomous unit that depends on XML
standards such as SOAP, WDSL, and UDDI [40].
3.4 Healthcare Applications
The technological developments in the fields
of multimedia clinical applications and
communication networks require a specific
analysis to increase the efficiency of network
based healthcare services [2]. In order to optimize
the QoS in network based healthcare applications
and services, it is crucial to study the two
aspects: the parameters of the transmitted
biomedical information and the transmission
behavior of the communication network [41][42].
4 QOS APPROACHES
The QoS approaches can be categories to
protocols, models, and Ontologies which will be
discussed in the following sub-sections
4.1 Protocol Based QoS
4.1.1 Real-Time Control Protocol (RTCP)
RTCP is a companion protocol where
multimedia applications can use along with RTP.
Packets in RTCP are sent periodically between
sender(s) and receiver(s). These packets contain
statistics such as number of packets sent, number of
packets lost, and interarrival jitter [28]. The primary
function is to provide feedback on the quality of the
data distribution [43].
4.1.2 ConEx Protocol
Fig. 3 shows ConEx Protocol [44]. It is an
experimental protocol defined at IETF in which, it
allows the sender of a flow to convey the ECN
information back towards the network, and provide
routers with information about congestion
downstream [44].
Figure 3: ConEx Protocol Operation
4.2 Models
4.2.1 QoS-A
QoS-A is a layered architecture of services and
mechanisms in multiservice networks [45]. The
architecture includes the flows, service contract, and
flow management key notations: Flows in which
characterize a single media stream either unicast or
multicast; Service contract represents binding
agreement between users and providers. While, flow
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management provides for the monitoring and
maintenance of the contracted QoS levels [46]. QoS-
A consists of layers and planes as indicated in Figure
4 [46]. As shown in Fig. 4, the upper layer consists
of a distributed applications platform augmented
with services to provide multimedia communications
and QoS specifications [47][46]. After that, there is
an orchestration layer, which provides jitter
correction and multimedia synchronization services
[48][46].QoS-A uses isolate protocol profiles for the
control and media components, as they have
different QoS requirements [46]. Based on flow
monitoring, QoS managers maintain the required
level of QoS [46].This architecture haven’t any
evaluation for it that allows checking its features.
This model has been implemented in local ATM
network [49].
Figure 4: QoS-A
4.2.2 IntServ
It can be considered as a signalling protocol
designed to reserve resources before establishing
connection [50]. The sender in IntServ network
sends PATH message to the receiver then the
receiver sends RESV message [50]. All nodes along
the path maintain these control messages. The
disadvantages of IntServ is that it is dependent on
reserving resources per flow, therefore, the nodes
along end to end connection should support this
signalling protocol. Also, due to the reservation
process, this model has a limitation in its scalability.
The significant contribution of IntServ [51][46]is to
providing controlled QoS for multimedia
applications [46]. Fig. 5 indicates the architecture of
IntServ framework. The IntServ framework has
classified various applications into the categories
such as: Elastic application such as Telnet, FTP, and
News [28], Tolerant real-time applications such as
audio conference or video streaming are very
sensitive
Figure 5: IntServ Architecture
4.2.3 . Heidelberg QoS Model
QoS Architectures in Heidelberg QoS model
includes a continuous media transport systems, this
component provides mapping and media scaling
[52][46].Fig. 6 indicates Heidelberg QoS Model.
Figure 6: Heidelberg QoS Model
The Heidelberg QoS model is designed for
heterogeneous QoS demands from individual
receivers in a multicast group and to support QoS
adaptively via flow filtering and media scaling
techniques [46]. This model was evaluated in LAN
environment and needs routers to support QoS
filtering feature to be implemented in other types of
networks as referred in [49].
4.2.4 DiffServ
IETF proposed another framework, called
DiffServ, which could support a scalable form of
QoS [28]. The RFCs 2474 and 2475 define the
fundamental framework of the DiffServ architecture
[53][54].
All subsequent forwarding and policing are
performed on aggregates by DiffServ interior nodes.
Fig. 8 indicated the three essential components that
are used in DiffServ: Meter, Shaper and Dropper.
Meter is used to compare the incoming flow with the
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negotiated traffic profile and pass the violating
packets to the shaper and dropper or remark the
packet with lower grade service using a different
DSCP. Shaper introduces some delay in order to
bring the flow into compliance with its profile using
a limited buffer and the packets exceed this limit will
be dropped. Dropper drops the out of profile packets.
The bandwidth broker (BB) which indicated in Fig. 9,
is responsible for automating the process of SLS
negotiation [28].
Figure 8: Data Path Operation
Figure 9: Bandwidth Broker
4.2.5 Flexible QoS Model for Manet (FQMM)
FQMM applies a hybrid provisioning where both
IntServ and DiffServ scheme are used separately
[21].It has been proposed for ad hoc networks
[55][21]. The three types of nodes included in this
model are: an ingress node which sends date, an
interior node which forwards data to other nodes, and
an egress node which is a destination [21]. The
disadvantages for this model are that its results come
from NS simulator and there is no real
implementation to evaluate it. Also, it is tested for up
to 50 nodes only.
4.2.6 Cross-Layer QoS Model
Cross-Layer QoS Model defines and distributes
metrics at different layers such as: application layer
metrics, network layer metrics, and MAC layer
metrics [21].For example, it classifies application
requirements into three QoS classes, class I, class II,
&class III, and map them to appropriate metrics.
Class I corresponds to applications that have strong
delay constraints, such as voice. This class is mapped
to the delay metric at application layer metrics
(ALMs). Class II is suitable for applications
requiring high throughput such as video or
transaction-processing applications. Similarly, this
class is mapped to the throughput metric at the
ALMs. Finally, Class III has no specific constraints,
and it is mapped to best-effort at the ALMs [21]. Fig.
10 indicates the architecture of Cross-Layer QoS.
Figure 10: Cross-Layer QoS Architecture
Cross-layer QoS model is used in a mobile ad hoc
network which consists of a collection of mobile
nodes forming a dynamic autonomous net [21]. The
frequent changes in network topology and the lack of
resources represent a challenging task in Mobile ad
hoc networking [21]. The routing protocols must be
adaptive to cope with the time-varying low-capacity
resources [21]. Its purpose is to study the impact of
changes on physical layer on data link layer. QoS in
physical layer will not be sufficient to represent the
entire layers as there are other parameters for each
layer. This model focused on using metrics on three
layers to provide QoS in Manet as there should be
different model, due to the fact that it has particular
characteristics than the traditional network.
4.2.7 OverQoS architecture
OverQoS Architecture depends on providing
QoS using overlay networks [56]. OverQoS uses the
notion of a controlled loss link (CLVL) to provide
differential rate allocations, statistical bandwidth and
loss assurances, and enables explicit-rate congestion
control algorithms [56]. OverQoS doesn’t require
any modifications in data or control planes for the IP
router of the nodes [56].A virtual link is the
underlying unidirectional IP path connecting two
overlay nodes that carries traffic from entry
OverQoS node to exist OverQoS node [56]. Virtual
link is characterized by loss rate and capacity band,
CLVL uses address the control and bandwidth and
schedule resources issues to ensure that the aggregate
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rate doesn’t exceed certain value [56]. Several
methods are used to set the selected value of
bandwidth [56].OverQoS Architecture can be
implemented on different network scales but it is not
guaranteed to provide the required resources, there is
no framework that defines their components. The
relation between OverQoS node and the existing
network is not clear.
4.2.8 Lightweight QoS-Support for Networked
Mobile Gaming
Busse and his colleagues develop an approach to
provide QoS over networked gaming application;
they use a simple real-time game called GAV and
check traffic measurements over GPRS and UTM
networks [57]. They proved that there are a high end-
to-end delay and delay jitter in GPRS and this why
real-time games are not supported in GPRS network
whereas UTM doesn’t match all the requirements of
real-time games [57]. Their approach aimed to
reduce the delay and delay jitter for these types of
applications. It depends on a combination of
statistical multiplexing and QoS guarantees. The
main idea in this approach is to aggregate multiple
game flows and perform reservation for that
aggregate [57]. The prioritization is achieved using
one of the traditional means such as DiffServ, RSVP,
or MPLS [57].
4.2.9 Management of End-to-end Quality of
Service Across the Internet at Large
(MESCAL)
MESCAL framework scope was designed
to deliver end-to-end QoS over Internet [58].
MESCAL treats with the propagation of QoS-based
agreements among the set of providers [58].
MESCAL research points out the need of
new techniques required to propagate QoS-based
agreements among the set of providers involved
in the chain of inter-domain service delivery.
They address the problem of how current
agreements between ISPs should be enhanced to
propagate QoS information between domains, and,
in the absence of any form of central control,
how these agreements may be used together to
guarantee end-to-end QoS levels across all
involved domains of control/ownership[58].
Figure 11: ISP Relations
Fig. 11 indicates the relationship between two
network service providers (NSP) to accomplish the
agreements between each subscriber and its
ISP.MESCAL is a QoS framework. Its staring
point is the two exiting forms of distinct
relations between ISPs for traffic exchange, that
underline the business agreements: peer-to-peer
and transit (customer-provider) relations between
ISP’s at different levels of the three-tier model [58].
A novelty of MESCAL is the introduction of
the QoS-class (QC) concept. QC denotes a basic
network wide QoS transfer capability of a Provider
domain [59], as a set of attribute-value pairs. This
concept is further refined as following: in a single
domain MESCAL defines local-QoS-class (l-QC)
and that is the QoS transfer capability provided by
the provider itself [38]
.
Disadvantages of this framework are that it focuses
on the relations among different ISP as a business
model but it gives an interest to the internal QoS and
QoE parameters. The isolation between Service
Provide and IP Network Provider which in some
cases, they are the same in Egypt, the ISP owns the
IP network and can directly deal with the customer.
4.2.10 NIProxy deployment
A carrier-grade network is a complex system, in
which consists of heterogeneous domains and
support multiple types of QoS [60]. Networked
gaming can be considered as a case study to evaluate
both QoS and QoE [5]. Fig. 12 indicates the
deployment of NIProxy. The NIProxy element is
used for optimizing both QoS and QoE by using
methods such as managing bandwidth consumption
of distributed applications [5] and based on the QoE
measurement and user feedback NIProxy will be
adjusted [5].
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Figure 12: NIProxy deployment in the targeted
desktop gaming setting
4.3 QoS based Ontologies
4.3.1 QoSOnt Approach
QoSOnt provides a model that can be used
across multiple heterogeneous domains [61]. This
shared conceptualization facilitates
intercommunication regarding QoS in a
heterogeneous environment [61]. Its realization as an
ontology allows automated reasoning about the
concepts modelled [61]. The ontology is realized
using the OWL web ontology language and is
symbiotic with OWL-S [61]. This system consists of
base ontology and several small Ontologies. Metric,
Attribute and other basic QoS concepts are
defined in the base ontology [61]. Fig. 13 shows
the structure of QoSOnt [61]. Fig, 14 shows the The
advantage of QoSOnt is the ability to create
standards, while its disadvantage is the realization of
the system in real environment.
Figure 13: QoSOnt Structure
4.3.2 mOSAIC
mOSAIC is an European project aiming to
“build an application platform that negotiates
services as requested by their users” [62].It uses a
Cloud ontology to define requirement terms to
enable users to specify requirements, and to
implement a multi-agent brokering mechanism that
will support service discovery and matching and
possibly service aggregation [63]. Fig. 14 indicates
the structure of mOSAIC ontology, it consists of 4
Ontologies: main, domain, APIs/Services and
Requirements [64]
Figure 14: Main structure of mOSAIC Cloud
Ontology
If both interoperability and portability of applications
exist in heterogeneous Clouds, they allow the
automatic management for: discovery, negotiation,
and monitoring of Cloud resources which is the goal
of mOSAIC [65]. To get a QoS, Service-Level
Agreement (SLA) between customer and service
provider should be conducted.
SLA@SOI [65] is European project aims at
offering an open source based SLA management
framework. SLA@SOI results are extremely
interesting and offer a clear starting basis for the
SLA management in complex architectures.
SLA@SOI offer solutions to design Cloud services
with multi-level and multi-provider SLAs.
mOSAIC offers a new way to develop Cloud
Application, uses Cloud resources and exploits
Cloud Computing features [65]. The mOSAIC
solution is a framework composed of three
independent components: platform, cloud agency
and semantic engine [66]. The first one (mOSAIC
Platform) enables the execution of application
developed using mOSAIC API; the second one
(Cloud Agency) act as a provisioning system,
brokering resources from a federation of Cloud
Providers; the last one (semantic engine) offers
solution for reasoning on the resources needing and
the application needing. Fig. 15 indicates the
different components if SAL architecture from
mOSAIC architecture [66].
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Figure 15: mOSAIC SLA Architecture
5 QOS PROPOSED MODEL
Aiming to overcome some of the existing
drawbacks, a new QoS model is proposed. Our
proposed model aims to offer a well-defined model
that includes the essential components to deploy QoS
in heterogonous network environment. This model
includes three essential components: QoS ontology,
ontology based Classifier, and Measurement. QoS
ontology facilitates the communication among the
different existing QoS approaches and parameters
and easily allows adding new approach to our model.
Ontology based Classifier which uses different types
of classification techniques either traditional
approaches or Machine Learning approaches. This
classification is done based on predefined traffic
ontology. Measurement component includes agents
which measure and evaluate the QoS parameters. Fig.
16 illustrates the system components for the dynamic
QoS provisioning model. Also the model includes
database where all network traffic in a specified
periods are stored.
Fig. 17 indicates a sample for the QoS ontology that
will be developed and relations among the different
network entities.
The QoS ontology has two relations types. These
relations are “part-of” and “is-a”. The “part-of”
relation will be used to define the components
relation between, e.g. Routing is a part of the
computer communication. Whereas, the “is-a”
relation will be used to define the instance relation
between, e.g. MPLS is a QoS
Dynamic QoS
Ontology
DB Annotator
Private or Public Cloud
TV
Satellite Dish
Video Head End
Traffic Conversion Tool
DB
Traffic
Classifications
Ontology
Agent
Agent
Agent
Agent
Figure 16: Dynamic QoS Ontology Provisioning
Model
Computer
Communications
Switching
Queueing
Standard
Organizatrion
Network Traffic
Models
Network
Categories
Protocols
Routing
QoS
LLQ
CBWFQ
WFQ
PQ
FIFO
MPLS
ATM CBR
IntSrv
DiffSrv
Part-of
Is-a
Figure 17: QoS Ontology Sample
Sample in Fig. 17 represents Class named
“Computer Communications” has a “part-of
“relationship with multiple subclasses such as
routing and switching. The subclasses have “is-a”
relationship with second level subclasses as CBR or
MPLS.
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Traffic PCAP
Format
Online Attacks
Classification
Offline
Clustering
No Yes
QoS Policy
Point
Classified Security Policy
PointYes
No
Offline
Clustering
Online Application
Classification
Classified
Traffic
Classification
Ontology
Figure 18: Traffic Ontology based Classification Model
Fig. 18 shows the traffic Ontology based
classification model which includes flexible
elements to identify the different type of known and
new applications.
Traffic Identification Tree
ProbablisticDeterministic
OffLineOnLine
Pkt_Header
Pkt_Payload
BehaviouralBased Machine Learning
Supervised SemiSupervised ClusteringPortBased
PayloadBased
Coral reef
CRL Pay
BLINC
KNN
NBSVM
C4.5 RS
Exclusive
Overlapping
Figure 19: Traffic Identification Hierarchy
Ontology
Fig. 19 indicates a sample for the traffic
identification ontology. We use till now two types
of relationships: “Part-of” and “Is-a”. We use “Part-
of” if the subclass is a component of the class
whereas we use “Is-a” if the subclass represents an
instance of its main class.
6 CONCLUSION AND FUTURE WORK
Our discussion depends on comparing three
categories for deploying QoS in heterogeneous
network: Protocol-based QoS, models, and
Ontologies.
Protocol-based QoS, it cannot operate as a
standalone QoS mechanism but they can
collaborate with other network elements to
accomplish the required QoS objectives [28][44].
Some of these protocols are not standards and need
specific
equipment to operate e.g. ConEX [44]. In QoS
models has some drawbacks like, the shortage in
evaluation in some models as in QoS-A [46], the
inability to be scalable as in InServ, and the
proprietary of the model to a specific type of
networks as QoS-A. The Heidelberg QoS model
was evaluated in LAN environment and needs
routers to support QoS filtering feature to be
implemented in other types of networks as referred
in [49]. Some models as FQMM [21], its results
come from NS simulator and there is no real
implementation to evaluate it Also, it is tested for
up to 50 nodes only .Cross-Layer model [21]
focused on using metrics on three layers to provide
QoS in Manet as there should be different model
due to the fact that it has particular characteristics
than the traditional network. Some models as
OverQoS, the relation between its nodes and the
existing network are not clear [56]. MESCAL
focuses on the relations among different ISP as a
business model but it gives an interest to the
internal QoS and QoE parameters [58]. The
isolation between Service Provide and IP Network
Provider which in some cases, they are the same in
Egypt; the ISP owns the IP network and can
directly deal with the customer. Ontologies-based
QoS, its models focus on using QoS ontology and
SLA ontology but they don’t provide an entire
vision for end to end QoS including all other
components as traffic identifications.
Our proposed QoS models suggested to address
challenge providing dynamic QoS in heterogeneous
networks using standards protocols that can
communicate with sharing information using
Ontologies. Also, the dynamic in both stage
classifications and applying QoS policies are
proposed in this model which are not included in
the existing models. It includes three essential
components: QoS ontology, ontology based
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Classifier, and Measurement. As ongoing work, we
are implementing the proposed model on a real life
[network] and evaluate our work on it.
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KishoriLal Bansal: Research on Application of
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