THE QUEST FOR SELF-MODEL IN SELF-MANAGING NETWORKS

Burak Simsek

FOKUS - Fraunhofer Institute for Open Communication Systems - SATCOM

Schloss Birlinghoven, Germany

Hakan Coskun

∗

Department of Design and Test of Telecommunication Systems

Technical University of Berlin, Germany

Keywords:

Self-management, self-model, autonomicity, wireless networks, 802.11e.

Abstract:

Although autonomic networking has been discussed in related research activities in the last few years, there

is neither a commonly accepted model nor a common understanding of the necessary components of an au-

tonomic networking environment, which are also practically applicable. We present a simple model based on

the notion of self that is perceived by human beings and deduce its components from the originating point

which is the need for autonomic behavior. We also introduce three use cases that we implemented for solving

different problems of IEEE 802.11e networks.

1 INTRODUCTION

Autonomy and self-awareness are gaining momentum

in computer science, mainly because of the ever grow-

ing complexity of computer systems and networks.

New approaches tend to support or even eliminate the

human operator by moving the control intelligence to-

wards the inside of the system. Entities no longer are

’dummy’ objects or components that are completely

managed from ’outside’, they become self-aware and

even self-managing meaning that the own behavior is

understood and changed or adapted to meet certain

criteria respectively (Sterritt and Hinchey, 2005).

Started some years ago, research activities try

to answer the question, how computer systems and

networks have to be designed and build in order

to provide capabilities that fulﬁl the requirements

of existing but also emerging requirements, such

as ad-hoc structures, increasing management com-

plexity, dynamic on-demand service composition and

ever growing quality of service (QoS) expectations.

Big vendors like SUN, HP and IBM on the one

side and the research community on the other side

have come up with different answers to those prob-

lems. Autonomic computing and utility computing

∗

This work has been supported by the ”Krupp von

Bohlen und Halbach-Stiftung”

or autonomic communications respectively have been

proposed as solutions (Kephart and Chess, 2003),

(Smirnov, 2004), (Rappa, 2004). Although the is-

sues covered in these approaches are basically dif-

ferent (Abbas, 2003), their underlying concepts point

at the same direction; the increasing system com-

plexity can only be managed by making the systems

aware of their own operations. This is referred to self-

management. Therefore, it’s not surprising to see that

all novel principles are centered around ’self’, e.g.

self-conﬁguration, self-healing, self-organization.

Despite the similarity of the mentioned proposed

solutions, there exists neither a common understand-

ing of the notion of self nor a framework for the de-

velopment of self managing systems. Approaches

so far also do not explain the intersecting issues of

those systems with the already existing artiﬁcial intel-

ligence methods. Therefore, it is difﬁcult to ﬁgure out

the innovative sides of these approaches, except the

eight key elements of an autonomic-computing sys-

tem (Horn, 2001) which do not go beyond being re-

quirements for self managing environments. Hence,

although theoretically the concept of autonomic com-

puting has been stated expressly, practical issues are

still far from being mature.

Being aware of this issue, this paper is dedicated

to the presentation of a self model that we have used

for managing QoS over IEEE 802.11e networks. In

284

Simsek B. and Coskun H. (2007).

THE QUEST FOR SELF-MODEL IN SELF-MANAGING NETWORKS.

In Proceedings of the Second International Conference on Wireless Information Networks and Systems, pages 268-274

DOI: 10.5220/0002151302680274

 SciTePress

this way, we ﬁll in the empirical gap of autonomic

communications and enable focusing more on prob-

lem speciﬁc issues.

In the rest of the paper, we summarize the notion

of self as perceived by human beings and induce our

self model based on these perceptions. In doing this,

we take into account the requirements and charac-

teristics of today’s communication environments and

correspondingly reduce the self model down to a set

of sine qua non enabling high scalability and com-

prehensiveness, which are essential for practice. We

clarify points intersecting with the classical artiﬁcial

intelligence methods and explain how this self model

can be extended with respect to problem speciﬁc sit-

uations. In the third section, we present three prob-

lems of the IEEE 802.11e standard in order to illus-

trate the introduced properties of the model, and build

corresponding self managing systems which we al-

ready implemented using network simulation tools.

We conclude our discussion with the summary of the

handicaps we encounter during the modeling of auto-

nomic networks and our contribution for overcoming

those handicaps.

2 SELF MODEL

The dichotomy of self and non-self is the basis

of an entity which is said to be self-aware (Mul-

hauser, 1998). It is not easy to deﬁne what self is.

In philosophy and psychology ’self’ is the mental

and conceptual awareness an individual holds with

regard to his/her own existence. It refers to the

conscious, reﬂective personality determining his/her

identity (Tauber, 2002). Although we cannot de-

scribe the term ’self’ for non-human living things in

the same manner, we still impose on them the notion

of self based on our perception of their possessions.

Consequently, the notion of self has a relatively large

spectrum, from being self-aware or autonomous to

self-unaware or allonomous

. This is also the crucial

distinction between intelligence and unintelligence.

One can argue that there is no need for deﬁning the

so called ’self’s (Metzinger, 2003). The fact that we

call the entity being developed ’self’ is not the cru-

cial point here at all. The goal is also not the cre-

ation of ’self’ as in the case of some artiﬁcial intelli-

gence techniques, but building methods which would

decrease the complexities of the management and the

utilization of the systems that we already built up. In

these terms, the issue of ’self’ is rather deduced from

the need for autonomy. Considering this fact, we will

governed by external stimuli

try to ﬁgure out the structure of the self based on our

needs for autonomic behavior in the following sub-

sections.

2.1 Notion of Emotion

It is a wonder of the nature that each existence has its

reasons or more strongly said ’deﬁned targets’. The

target is intrinsically tied with the existence itself.

This makes it crucial to regard the target as the high-

est order feature of the ’self’. Based on this fact, most

of the artiﬁcial intelligence techniques are built on

the deﬁnition of targets (Ferber, 1999), (Keiblinger,

2000). Nevertheless, the introduction of targets poses

many obstacles.

The questions, how to tell the ’self’ about his tar-

gets and how these targets should be reached proved

to be extremely difﬁcult to answer leading to difﬁcul-

ties in the implementation of those techniques. For

this reason, ontology based languages are proposed

and corresponding evaluation algorithms are devel-

oped (Bratko, 2000), (Muggleton, 1999). Neverthe-

less, in practice the use of these languages also proved

to be extremely awkward (Scott, 2000).

If we have a look at the animal world, instead of

targets, we witness the existence of instincts that au-

tomatically tell animals what to do next as some trig-

gering factors emerge. In fact a leopard does not think

about his targets, which may be for him to survive.

Or how do his babies know where the milk is coming

from and why at all? Targets enriched with posses-

sions such as the ability to run or eat are already there

as they are born. This is a start-up process (bootstrap-

ping) in the lives of all living things.

In (Damasio, 2005) emotion is deﬁned as

‘ an intense mental state that arises auto-

matically in the nervous system rather than

through conscious effort, and evokes either a

positive or negative psychological response.’

Taking this deﬁnition of emotion as the basis, we

can say that the impulses occurring in the form of

emotions are directly coupled with all the actions

of the living things following their births (Damasio,

2005), (Glasser, 1999).

The initial emotions give them a taste of what a

satisfaction would mean and autonomic reactions to

the sensed emotions show the initial solution propos-

als to these impulses (pleasure and pain). With time

and experience the causalities behind the possessed

functions, new ways of satisfaction and how to get rid

of dissatisfaction are learned.

The phenomenon which results emotions is the

state being sensed by the ’self’. Targets are also de-

ﬁned as states. Therefore, targets of a self can be di-

THE QUEST FOR SELF-MODEL IN SELF-MANAGING NETWORKS

285

rectly coupled with a mechanism of emotion. If there

exists a measurable emotion for each state, on which

the behaviour of ’self’ depends and if it is also pos-

sible to manage the level of emotion at each state,

then it is also possible to lead the self to its targets

by only using emotion levels. This eliminates the

need for deﬁning targets separately, which would oth-

erwise require additional deﬁnition, semantics, rep-

resentation and interpretation mechanisms (Levesque

et al., 1998).

Consequently, it is possible to model the ’self’

without explicit targets, but with a well deﬁned emo-

tion map over a state space by introducing the primary

target as the willingness to maximize satisfaction.

2.2 Embedded Notion of Emotion

The sense of emotions is solely a triggering factor

for the functioning of the self. A deeper mechanism,

which exists in the core of the self is responsible for

the birth of the self. It has exclusively only the ca-

pability to ’trigger’ some functionality of itself with

respect to its feelings. In fact this entity or mecha-

nism is what the philosophers have been trying to ﬁnd

out (Glasser, 1999). Instead of speculating about what

this entity might be for different living things, we just

try to model its functional properties that are crucial

to the formation of the self. We call this mechanism

’Embedded Notion of Emotion’ (ENE).

Although ENE is the core element of the self

model, it is an complete abstract entity. Its program

is simply based on the desire to maximize its reward

function and the ability to trigger the possessed func-

tionalities. Its existence gives birth to a living self

but has no direct inﬂuence on the possessions of the

self. These possessions are rather given. It might be

the case that, ENE is enriched with possessions which

enable the self to reach higher rewards by gaining new

possessions. This however deﬁnes the intelligence

level of the self which is not a necessary character-

istic for its existence.

Creating intelligent systems is in itself very inter-

esting, but this sound should not deﬂect one from the

actuator point that we should try to minimize the com-

plexity within highly entangled systems. Although

intelligence is a desired characteristic, it is most of

the times not true that the intelligent systems decrease

complexity. For this reason what we are search-

ing for is autonomy more than intelligence. Auto-

nomic systems can be composed of system manage-

ment methodologies, which are deﬁned to be static.

However, the ’self’ model can also cover artiﬁcial in-

telligence methodologies in order to make ’learning’

possible so that knowledge and functions of the sys-

tem are adapted to new environments gradually and

become self-aware resulting in autonomous environ-

ments.

2.3 Sine Qua Non of Self

Based on the discussion of Sections 2.1 and 2.2, we

deﬁne three necessary components for the existence

of a ’self’. These are the reward mechanism deﬁned

over a state space, a primary triggering function re-

ﬂecting a targeted incentive running over the reward

mechanism (ENE) and a possession which can be an

abstract or a concrete one. Although these three com-

ponents are sufﬁcient to build the very basics of ’self’

regardless of what kind of possessions the self has or

how its reward map is designed, the notion of auton-

omy needs the deﬁnition of properties that enable the

perception and evaluation of the possessions by the

self. An illustration of this model can be seen in Fig-

ure 1 We deﬁne those properties in the following sec-

tion.

ENE

Observer

Function

Triggers

Emotion

Receives

“The self”

Figure 1: A simple illustration of ENE.

2.4 Observer

Self-awareness, the most fundamental requirement

for a self-managing system, is the continuous recog-

nition of the own possessions, their states and func-

tionalities abstracted from the environment in which

self exists. Correspondingly, the process of observing

the changes in own possessions, their states and func-

tionalities relative to the environment of existence is

called self-monitoring. During the process of self-

monitoring, the observer and the observed are one in

a process that recursively gives rise to each.

In the area of social sciences self-monitoring is

not a new issue. Human beings are self-aware by na-

ture. Psychology, sociology, medicine and even sport

deal with the monitoring of ’self’ But psychology and

sociology use the term ’moitoring’ with a different

meaning. Monitoring is not only dealing with measur-

ing and observing properties, according to the litera-

ture, self-monitoring involves three major and some-

what distinct grounds (Baron and Greenberg, 1990):

• the willingness to be the centre of attention

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286

• sensitivity to the reactions of others;

• ability and willingness to adjust behaviour to in-

duce positive reactions in others.

Along these points, monitoring does not only deal

with observation, but also includes behavioural con-

trol tendencies. However, in the technical literature

these are two separate issues. In a technical sense self-

management is rather perceived to be the subsequent

use of the recognized possessions for controlling, pre-

serving and changing the recognized states and func-

tionalities.

Entities performing self-monitoring are separated

into two distinct types. High-monitors are aware

of their own presentation within their environment,

keeping track of external cues to regulate their be-

haviour. In contrast to that, low monitors are sensi-

tive and aware of their ’inner’ states resulting in a low

degree of public awareness. They pay ’less attention

to appropriate behaviour in social situations and try

to maintain consistent behaviour across all situations’

(Snyder, 1986). High-monitors deal mostly with pub-

lic awareness. The environment is monitored more

than themselves. These entities are concerned about

each situation and how to ﬁt to it and choose the right

’face’. Low self-monitors focus on internal states and

cues as an indication of behaviour modiﬁcation. Pri-

marily, these care for a consistent behaviour across

different situations.

Although monitoring within ENE is not a must,

its existence is necessary during implementations in

order to generate corresponding emotion stimuli. Ad-

ditionally, in case of the need for behavioural intelli-

gence it is also necessary that the ’self’ is aware of

its state. Depending on the environment in which

the ’self’ exists, both high and low monitoring must

be made available. The generation of corresponding

emotion stimuli is a problem speciﬁc issue. Although

low monitoring takes a role in generating emotion

stimuli always, high monitors are generally used dur-

ing decision making as additional information. Only

in case of social ’self’s it might be required to include

high monitoring for emotion stimuli generation.

3 A SIMPLE SELF MODEL

USING IEEE 802.11E

The amendment IEEE 802.11e extends the existing

IEEE 802.11 standard by adding new functions tar-

geting both differentiated and integrated services. In

this way, IEEE 802.11e enables QoS enhanced ac-

cess points (QAP) to cope with real-time trafﬁc that is

delay-sensitive, jitter-sensitive and error-prone, such

as voice and video streams (see (Chalmers and Slo-

man, 1999) for a detailed overview).

New primitives of IEEE 802.11e, such as MAC

Layer Management Entity ADD Trafﬁc Stream re-

quest (MLME-ADDTS.request) allow negotiations

between a QAP and a QoS enhanced station (QSTA)

such as required maximum MAC Service Data Unit

(MSDU) size, data rate, burst size and average de-

lay. Additionally the QoS enhanced basic service set

(QBSS) load element being advertised within beacon

frames periodically gives information about the situa-

tion of an access point. This enables decision making

prior to attachment using parameters other than signal

strength. Furthermore, there is a number of new QoS

related parameters used by a central control mecha-

nism, the hybrid coordinator (See 1 for more detail

on those parameters). Hybrid coordinator (HC) is re-

sponsible for managing those parameters in order to

assure the best possible QoS over the QAP.

The number of available parameters that can be

reconﬁgured during run time, the deﬁnition of a clear

target, which is the maximization of QoS offered to

the attached mobile stations and the existence of cor-

responding negotiation and control mechanisms make

IEEE 802.11e a very good candidate for the deploy-

ment of a self managing wireless network. There-

fore we adapted our model ENE to the IEEE 802.11e

implementation of the ns2 network simulator. In

the following sections we introduce three problems

of IEEE 802.11e and corresponding ENE models to

solve those problems.

Within our simple scenario there exist a QAP, a

number of stations already connected with the QAP

and a QSTA which has a new trafﬁc stream to transfer.

There are three problems for the establishment and

preservation of the connection with the QSTA.

3.1 The Scenario

Within our simple scenario there exist a QAP, a num-

ber of stations already connected with the QAP and

a QSTA which has a new trafﬁc stream to transfer.

There are three problems for the establishment and

preservation of the connection with the QSTA.

1. The QSTA should decide if the QAP is the right

choice for making the connection in terms of QoS.

2. The QAP must decide on accepting the new re-

quest coming from QSTA or not.

3. During the data transfer from QSTA, QAP has to

make sure that its parameters are conﬁgured in a

way that the negotiated QoS level is preserved.

The ﬁrst two problems deal with the interac-

tion between two different ’self’s (high monitoring),

THE QUEST FOR SELF-MODEL IN SELF-MANAGING NETWORKS

287

whilst the last one only deals with a problem within

a self (low monitoring). In order to implement these

scenarios, we used network simulator ns2. We present

here only the models, since this paper aims at present-

ing the model ENE. Real implementations and their

results are referred after each problem.

3.2 Problem 1

In the ﬁrst problem, the QSTA has to decide whether

it would want to connect to the candidate QAP or not.

The choice of the QAP over which the QSTA sends

its frames is left to vendors within IEEE 802.11e.

However the unit responsible for making this decision

is the station management entity (SME). For making

this decision, the SME is supposed to use the QBSS

load element. It includes three parameters: station

count, channel utilization and available admission ca-

pacity. The station count is the total number of sta-

tions currently associated with the access point. The

channel utilization gives the percentage of the time

the channel is sensed to be busy using either the phys-

ical or virtual carrier sense mechanism of the access

point. The available admission capacity gives the

amount of time that can be used by explicit admis-

sion control. We refer to (Simsek et al., 2006a) for a

detailed study of the QBSS load element and its usage

during decision making.

As we described in Section 2.2, the ENE is an

abstract entity which has no direct inﬂuence on the

possessions of the self. These possessions are rather

given to it. Within the ﬁrst problem, the ENE is em-

bedded into the QSTA with four interfaces. One in-

terface is at the SME, where the decision about the

access point selection is given. The second interface

is for monitoring purposes. Parameters regarding the

characteristics (priority, burst rate, mean data rate and

delay bound) of trafﬁc stream are visible to the ENE.

ENE regards both the function responsible for can-

didate access point selection and the parameters of

the trafﬁc stream, hence the trafﬁc stream itself as its

own possessions. The third interface that the ENE has

is the point where the QBSS load element advertise-

ments are received by the QSTA. ENE regards QBSS

load element as the ’other’ which is the QAP.

ENE is responsible for making a decision with re-

spect to the state which it perceives as a result of the

monitoring at the second and third interfaces and ap-

plies its decision on the ﬁrst interface. The last in-

terface is indirectly bound with ENE. The QoS of the

trafﬁc being transmitted is observed and satisfaction

level is calculated using mean opinion score (MOS).

This MOS level is then given to the ENE as the satis-

faction mechanism.

As can be seen from ﬁgure 2, the original ns2 im-

plementation is not changed except the point where

the selection decision is given. This is true regard-

less of the problem. This property is the reason for

the name ’embedded notion of emotion’, since ENE

is embedded into an existing system partially or en-

tirely, without modifying the system. As described in

Sections 2.1 and 2.2, the functionality of ENE is sim-

ply based on the desire for having more satisfaction

and on the ability to trigger the possessed functional-

ities of the system in which it is embedded. We refer

to our previous study (Simsek et al., 2006b) for the

implementation of this model and its results.

ENE

SME

Monitoring

(others)

Traffic Stream

Characteristics

QBSS Load

Element

Decision

enforcement

Emotion

ENE

SME

Monitoring

(others)

Traffic Stream

Characteristics

QBSS Load

Element

Decision

enforcement

Emotion

Figure 2: Representation of ENE within Problem 1.

3.3 Problem 2

Following the decision of QSTA for initiating the

transmission with a QAP, the QAP has to decide

whether it should admit the request or not, depend-

ing on the characteristics and if existing on the cor-

responding service level agreements with the mobile

user. During the ﬁrst problem, the QSTA had only

limited information for making its decision, which is

the QBSS load element of the QAP. However, the

QAP has all the capacity related information (chan-

nel utilization of each priority and each trafﬁc stream,

corresponding QoS metrics such as delay and loss

rates of each priority) and also its own admission con-

trol policy (service level agreements done with other

mobile users) (See Figure 3). Although, both prob-

lems are very similar in nature, namely, if the QAP

will be able to satisfy the QoS requirements of the

trafﬁc stream or not, the amount of available informa-

tion in the second problem is signiﬁcantly higher and

the point of view is different. This allows us to com-

pare behavioural differences of both parties (QSTA

and QAP) for the same problem. This is especially

interesting when learning is enabled, since the expe-

riences of QSTA and QAP and also the monitoring of

the behaviours from different perspectives are com-

pletely different. Furthermore, we can also analyze

the consequences of using different amount of infor-

mation.

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ENE

Monitoring

Traffic Request

Characteristics

Decision

enforcement

QoS for each class, SLAs, QBSS

Load Element, Parameter

Configuration

Monitoring

(others)

Emotion

ENE

Monitoring

Traffic Request

Characteristics

Decision

enforcement

QoS for each class, SLAs, QBSS

Load Element, Parameter

Configuration

Monitoring

(others)

Emotion

Figure 3: Representation of ENE within Problem 2.

3.4 Problem 3

Different than the ﬁrst and the second problems,

where the QSTA and the QAP had to make decisions

following the request for a trafﬁc stream, the QAP is

responsible for preserving the promised QoS levels

for each trafﬁc stream in a continuous manner within

the last problem. This requires permanent monitoring

of QoS levels of each active trafﬁc and modiﬁcation

of own parameters respectively.

ENE

Parameter

Configuration

Monitoring

QoS / SLA of each

stream

Decision

enforcement

HCCA EDCA

Emotion

ENE

Parameter

Configuration

Monitoring

QoS / SLA of each

stream

Decision

enforcement

HCCA EDCA

Emotion

Figure 4: Representation of ENE within Problem 3.

As seen from ﬁgure 4, the IEEE 802.11e in its

own has two functions, EDCA (Enhanced Distributed

Channel Access) and HCCA (Hybrid Coordinator

Controlled Channel Access). These functions are

used by the hybridc coordinator (HC) for differenti-

ated and integrated services respectively. The HC has

full control over HCCA and its schedule, whereas it

has only limited inﬂuence on the functioning of the

EDCA. We are going to describe how different lev-

els of control over the possessions of the ’self’ can

be managed in our next study. Nevertheless, although

HC cannot control the transmissions over EDCA, it

can restrict the use of some of the four priorities by

requiring for explicit negotiation for those priorities.

Additionally, it can change the contention window

sizes of EDCA for each priority and also alter the

TXOP limit. Hence, the HC can indirectly affect the

QoS of trafﬁc using EDCA. Table 1 shows a list of the

most inﬂuential parameters of HCCA and EDCA that

we found to be most signiﬁcant for the functioning of

IEEE 802.11e.

Within the third problem, ENE has two interfaces

with the QAP. The ﬁrst interface is used for monitor-

ing the QoS levels of each trafﬁc by the use of which

satisfaction level is given to ENE. The second inter-

face is with the parameters of table 1. ENE alters

the parameters continuously with respect to the sat-

isfaction level it expects. This problem is also known

as algorithm conﬁguration problem. We are going to

present our implementation of the third problem and

its results within our next studies.

Table 1: List of HC conﬁguration parameters.

HCCA

% of time reserved for HCCA

service interval

EDCA

CWmin

CWmax

TXOP Limit

priority restriction

4 CONCLUSION

Although there are plenty of models proposed so far

for building the intelligent environments of the fu-

ture which should reduce system complexities, these

models either could not bear down the conceptual

planning phase like many bio-inspired models (Sim-

sek and Albayrak, ) or they proved to be inefﬁ-

cient in terms of performance like the agent tech-

nology (Keiblinger, 2000). In building such models,

the research community ignored the following crucial

points which constitute the igniting factors of our pa-

per.

• The effort to build and preserve the self manag-

ing components should be signiﬁcantly lower than

traditional solutions. As mentioned above, the

agent technology is a very good example for an

unsuccessful effort in bringing this facility. Al-

though in terms of conceptual planning the agent

technology proved to reﬂect many requirements

that one may expect from autonomic environ-

ments, its development, application and mainte-

nance became to be a greater obstacle in reducing

system complexity.

• The model for self managing components should

be system independent and work generically in or-

der to prove its ability to reduce system complex-

ity. Otherwise it is an additional load to software

developers and it makes more sense to use appli-

cation speciﬁc solutions. Therefore, the self man-

aging model should have the plug & play capa-

THE QUEST FOR SELF-MODEL IN SELF-MANAGING NETWORKS

289

bility which starts functioning after it is embed-

ded into the system being considered regardless

of the application type. However previous models

required the implementation of the systems from

scratch.

• There must be clear metrics for deﬁning self man-

aging components, their states, targets and be-

havioural directions. However there is so far no

such well deﬁned metric for self managing com-

ponents.

• It must be possible to deﬁne the behaviour of self

managing components in a formal manner so that

it becomes manageable. This behavioural man-

agement should be independent of the application

and easy to deﬁne so that the course of the actions

of the self managing components can be followed

in a causal manner during runtime.

The points mentioned above show how our de-

sire for a more intelligent computing environment can

easily become a fallacy in terms of application efﬁ-

ciency. Hence, during the development of models for

autonomic computing it is essential to consider the

practical aspect primarily. This is especially impor-

tant for the new trend ’autonomic networking’.

In this paper we presented a model for autonomic

networking which is simpliﬁed down to the very ba-

sic elements of the ’self’ as perceived by human be-

ings. In doing this, we especially paid attention to

our originating point, which is the need for autonomic

behaviour. We kept the model as simple as possible

by considering the above mentioned problems so that

the model is scalable and applicable within the seven

OSI layers even with scarce resources. For illustration

purposes, we also summarized three implementations

of ours using the introduced ENE model for solving

three different problems of IEEE 802.11e. In this way,

we opened the way for more problem speciﬁc studies

in autonomic networking.

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