A NEW MODEL FOR DYNAMIC QOS IN … · A NEW MODEL FOR DYNAMIC QOS IN HETEROGENEOUS NETWORKS USING ONTOLOGIES . ... growth in Internet usage, ... A consists of layers and planes as

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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

Ubiquitous Computing and Communication Journal ISSN 1992-8424

Volume 10, Issue 1 Page 1488

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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



4

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



10

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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[3] Sunay Tripathi, Nicolas Droux, Thirumalai

Srinivasan: Crossbow: A Vertically Integrated QoS

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[4] ETSI: Human Factors (HF); Quality of

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