Autonomic Quality of Experience Management
of Multimedia Networks
Steven Latré and Filip De Turck
Ghent University - IBBT - Department of Information Technology, e-mail: steven.latre@intec.ugent.be
Abstract—The proliferation of multimedia services over access
networks (e.g., IPTV or network-based Personal Video Recording) has introduced important new revenue potential for network
and service providers but has also complicated the management
burden. As a result, today’s management of multimedia networks
is often too static to cope with the increasing quality requirements
of multimedia services. A key point in these quality requirements
is the quality as perceived by the end users, denoted as the Quality
of Experience (QoE). In the thesis, we have introduced an autonomic management layer that optimizes the QoE of multimedia
networks. We have studied several QoE optimizing techniques
with respect to traffic adaptation, admission control and video
rate adaptation. All these QoE optimizing techniques exhibit
autonomic behavior as they continuously monitor the network to
optimize their configuration and consequently optimize the QoE.
Furthermore, we have investigated the coordinated deployment
of these QoE optimizing techniques by focusing on the exchange
of context between entities in the distributed autonomic management layer. Through extensive evaluation using both simulation
and emulation on a large-scale testbed, we have shown that the
proposed QoE optimizing techniques can successfully optimize
the QoE of multimedia services. This QoE optimization was
characterized in terms of metrics such as the number of admitted
sessions and video quality.
I. M OTIVATION
The management of multimedia networks is challenged by
three major trends in today’s Internet: (1) a drastic increase
in size of the current Internet and popularity of multimedia
services, which stresses the importance of scalable management approaches, (2) a larger heterogeneity of the devices
with the popularity of devices such as mobile phones, tablet
PCs, etc. and (3) the higher quality requirements of multimedia
services when compared to static services in terms of packet
loss, throughput and connection duration.
Throughout our work, we have focused on the management
of access networks of which multimedia services such as
IPTV and network-based Personal Video Recording are the
most important services. Current management of multimedia
access networks is still centered around a manual and reactive
process where actions are taken by an operator when network
anomalies occur. This manual management is not only timeconsuming and costly but also increasingly complex given
the growing scale of the Internet and complex multimedia
service’s quality requirements.
When characterizing the quality requirements of multimedia
services, the quality as perceived by the end users, commonly
denoted as the Quality of Experience (QoE), is of the utmost importance. Previous research has focused mainly on
978-1-4673-0269-2/12/$31.00 c 2012 IEEE
Higher QoE
requirements
Lower QoE
requirements
Fu
D
ll H
vid
eo
L
e
ow
ide
e
or
so
ion
lut
rv
QoE Managed
Network
Traffic flow
adaptation
Video rate
adaptation
Admission control
Fig. 1. The QoE in a multimedia network can be autonomically managed
through flow adaptation, admission control and video rate adaptation.
the design of accurate QoE metrics [1] and monitoring and
reporting tools [2]. Our work is the first that takes this QoE
information as an objective to steer the control algorithms of
an autonomic management framework. We do not focus on the
design of new QoE metrics but use QoE and QoS parameters
already available in today’s networks to optimize the QoE. As
shown in Figure 1, our work optimizes the QoE of multimedia
services using techniques on three different axes:
1) Traffic flow adaptation modifies the network delivery of
a traffic flow by changing the transport configuration or
by accompanying the application data with additional
(e.g., redundant) data.
2) Admission control does not modify the data of a flow but
admits or blocks new connections to avoid congestion.
3) Video rate adaptation alters the application data itself by
changing the actual video quality level that is transmitted
as part of the multimedia service.
These QoE optimizing techniques are not orthogonal and the
configuration of one technique may influence the configuration
of the other. Therefore, our work also focused on how context
generated by one technique can be exchanged with others, to
facilitate the coordination between these techniques.
The remainder of this article is structured as follows. The
concept of QoE is more thoroughly defined together with
autonomic management approaches in Section II. Section III
discusses the hypothesis and research questions we addressed
in the thesis, while Section IV presents our contributions.
Finally, Section V highlights some future research directions.
II. BACKGROUND
A. Quality of Experience
Brooks et al. [3] define QoE as follows: ’QoE is a measure of user performance based on objective and subjective
psychological measures of using a service or product’. They
describe the link QoE has with objective QoS and provide an
overview of the QoS parameters that contribute to the overall
QoE. Specific to multimedia (i.e., video-based) services and
products, the QoE is mainly impacted by the quality of the
source video and its delivery.
Although the QoE is inherently subjective, the authors of [3]
discuss the need for objective and quantitative metrics that
provide an estimation of the subjective QoE as this allows
comparing between products and services. Several types of
video quality metrics have been proposed in the past [1], which
differ in whether or not (a part of) the original video sequence
is required for the metric’s calculation. Full-reference metrics
have the highest correlation with the human visual system
and are therefore best suited for the off-line evaluation of the
performance of video delivery.
B. Multimedia networks
The increasing popularity of QoE demanding services has
resulted in the standardization of architectures such as Diffserv [4] and Intserv [5], which incorporate techniques for
classifying traffic and providing them with a differentiated
behavior based on the service type. These architectures can
provide Quality of Service (QoS) through mechanisms such
as admission control and resource reservation. However, these
architectures provide a very coarse grained QoS management
and are typically configured statically in which they overdimension the network considerably to provide the QoS. As
video services have the lion’s share of today’s access networks,
a lot of research is being carried out on the optimization of the
video delivery by introducing more video oriented mechanisms
into the access network. This has lead to a plethora of
video optimizing techniques being proposed of which specific
FEC codes [6], caching techniques [7] and retransmission
mechanisms [8] are only a few examples. However, these video
optimizing techniques are typically configured statically and
make important assumptions on the traffic characteristics of
the video they are optimizing.
C. Autonomic management
The static management of today’s networks introduces a
severe limitation on which QoE and QoS guarantees can
provided. Many authors have argued that a more flexible
management is required to address the dynamic requirements
of novel services such as multimedia services. Such a dynamic
management is often translated into the research towards
autonomic network management [9]. The term autonomic
refers to the autonomic nervous system, where an equilibrium
is maintained between several biological processes, without
the need for conscious directions. An autonomic network
is directed through high-level goals (i.e., policies), which
results in an automatic and sub-conscious translation of actual
configurations to reach that goal (e.g., the configuration of the
network). Jennings et al. [10] provide an elaborate discussion
on the challenges faced in the autonomic management of
communication networks. They discuss the need for intelligent
control algorithms that coordinate the different autonomic entities. This is achieved through control loops that dynamically
configure the network but are deployed in a loosely coupled
distributed environment, where multiple autonomic entities
work together to jointly manage the network. Furthermore,
they argue that semantic approaches such as ontologies are a
vital part of any future autonomic network and are required
because of the heterogeneity of today’s network devices and
management domains on one hand and a strive to more formal
mechanisms in reasoning on the other hand.
III. H YPOTHESIS AND RESEARCH QUESTIONS
The main objective of the thesis is to prove the validity of
the following hypothesis:
The QoE of multimedia services can be considerably
improved by introducing an intelligent and distributed
autonomic management layer, spanning multiple management
domains, which has the high-level goal of optimizing the
services’ QoE through the automated deployment and
coordination of QoE optimizing techniques.
To validate the above hypothesis, the following research
questions were addressed:
1) How much can the QoE delivery of multimedia services
be optimised by introducing more autonomic behaviour
into current management solutions?
In multimedia networks, a QoE guaranteed delivery
is complicated by the occurrence of several network
anomalies such as packet loss, throughput drops or fairness issues. The static configuration of today’s management techniques (e.g., congestion control and admission
control) in contrast with the growing heterogeneity of
today’s video services (e.g., multiple video quality levels, different burstiness) introduces severe limitations on
the effectiveness of the management. A more dynamic
and autonomic management is therefore needed.
2) Which QoE gain can be obtained by using application
specific actions, that alter the services’ QoE, in combination with traditional network specific actions, that
alter the network delivery? What is the corresponding
trade-off between more customers and obtained average
QoE score?
Video services are one of the few services that can deliberately change the services’ QoE (e.g., to allow more
users at a reduced quality) by modifying the application
data that is sent over the network. Although, at the video
encoding level, techniques exist to dynamically change
the video quality level, there is a need for network
Other root AEs
Other root AEs
Multimedia network's root AE
AE
AE
AE
Coordination of AEs
AE
AE
AE
QoE Optimizing Technique
video rate adaptation
QoE Optimizing Technique
admission control
QoE Optimizing Technique
traffic adaptation
Monitor:
Network load
Monitor:
Network load
Act:
Downscale
video
Monitor:
packet loss +
connection's
throughput
Act:
Change admission
control configuration
Act:
Add redundancy +
penalize misbehaving
clients
Fig. 2. Typical multimedia network and a conceptual overview of how the
algorithms on different axes are deployed on the network’s devices.
solutions that can decide when to scale to which video
quality level so that a network or service provider can
control the QoE levels he offers to the customers.
3) How can multiple management entities in a federation,
spanning multiple management domains, interact with
each other to form an end-to-end QoE optimising delivery framework?
Although the best management decisions can be made
if all knowledge is centralized, centralizing the management is not an option for scalability, overhead and
privacy reasons. Instead, different management entities,
spanning multiple management domains, need to cooperate with each other in a so-called federation. The
entities in a federation need to interact with each other
in order to form an end-to-end QoE optimization from
the service originator up to the end customer.
IV. C ONTRIBUTIONS OF THE DISSERTATION
Figure 2 illustrates the hierarchical autonomic layer, consisting of multiple management entities or Autonomic Elements
(AEs), that we have introduced on top of a typical access
network. While AEs between administrative domains may
have peer-relations (e.g., as part of a federation), we focus on
the AE functionality in the multimedia network itself, which
is hierarchical. Such a hierarchical approach has significant
advantages in terms of scalability and the delegation of management decisions, which we have described in [11].
At the bottom of the hierarchy, several QoE optimizing
techniques are deployed. These QoE optimizing techniques are
components which are able to restore QoE in a well-defined
scenario (e.g., congestion or lossy links). At higher layers,
these QoE optimizing techniques need to be coordinated
to ensure that the configuration of one technique does not
negatively influence the performance of another.
In our work, we have made several contributions to the field
of autonomic QoE management. To be able to accurately evaluate the QoE optimizing techniques, we have proposed both a
simulator (Section IV-A) and a management framework (Sec-
tion IV-B) for large scale testbeds that allows characterizing
the QoE of videos. Furthermore, we have proposed novel QoE
optimizing techniques that perform traffic flow adaptation for
both unreliable (Section IV-C1) and reliable (Section IV-C2)
data transfers, admission control (Section IV-D) or video
rate adaptation (Section IV-E). Finally, we have focused on
how context (e.g., monitor data, reasoning decisions) can be
exchanged between AEs (Section IV-F) through the design of
a context dissemination framework.
A. Design of a QoE enabled simulator
As the goal of this work was to optimize the QoE of
multimedia services, the evaluation methodology particularly
focused on accurately characterizing the QoE. As a single
QoS metric is not sufficient for grasping the subjective nature
of QoE, we used a full-reference video quality metric called
the Structural Similarity Index [12], throughout the evaluation
of all QoE optimizing techniques. This allows us to have an
accurate view on the improvement of each QoE optimizing
technique on the video quality as perceived by the end user,
which, for video services, is the most important part of QoE.
In our work, we have designed an NS-2 [13] simulator
that is able to incorporate these QoE oriented experiments
by emulating the transmission of real video sequences over a
simulated network [14], [15]. In this environment, the packets
that are being transmitted over the simulated network do not
contain real data but are dummy packets based on trace files
of the video. These trace files can be generated using a dump
of the generated traffic of the video streamer. On the receiving
end, the simulator reconstructs the received video sequences so
that the effect of simulated network anomalies can be visualized through the playback of the video. Furthermore, it allows
calculating any video quality metric to objectively quantify the
QoE. Through the proposed simulation environment, we were
able to perform large-scale simulations: simulating several
hours of video playback and transmission takes less than 2
minutes.
B. Experiment automation for large scale testbeds
Besides simulation, several of our contributions were also
evaluated on a large-scale test-bed facility of more than 100
nodes. Testbed facilities provide a means to set up large scale
network topologies but offer only a limited functionality in
managing the deployment of the experiment itself. In order
to address this, we have proposed a management framework
for automating the setup of experiments in [16], [17]. The
proposed management framework is generic but has been used
extensively in our work for setting up multimedia network
based experiments where QoE metrics are important. The
framework focuses on the emulation of user behaviour by
enabling the definition of behavioural profiles. These behavioural profiles define how a customer behaves over time,
meaning which services he accesses, with what frequency,
etc. As this behaviour heavily depends on the type of service
that is accessed, a pluggable approach is taken where it is
possible to define a service model for each type of service that
is accessed. Furthermore, the framework allows simplifying
the experiments both spatially and temporally. The spatial
reduction consists of a virtualization approach where multiple
virtual nodes are mapped onto one physical testbed node. For
the temporal reduction, the experiment can be sped up by a
speedup factor to reduce the time required to emulate: this
allows us to emulate the behavior of one day in one hour or
less. In [16], we have shown that the management framework
allows to map at most 50 virtualized nodes on a physical node
with a speedup factor of 50, without introducing a significant
loss in accuracy. As such, the management framework supports
experiment sizes of up to 5,000 nodes, which can be emulated
50 times faster than real time.
C. Traffic flow adaptation: improving the QoE by modifying
the network data
1) Unreliable data transfers, QoE optimization on the last
mile: For unreliable data transfers, packet loss either due to
congestion or a lossy link is the most important trigger for QoE
disruptions. In our work, we focused on the QoE optimization
on the last mile (i.e., the access network’s link just before the
home network) of UDP-based video services by deploying
a QoE optimizing technique on the access node. The QoE
optimizing technique combines an adaptive FEC mechanism
with a video quality switching algorithm based on monitoring
input from the home network. The reasoner dynamically tunes
the optimal redundancy level of the FEC mechanism but also
selects the maximum possible video quality level, given the
required redundancy, to avoid network congestion.
To decide upon which action to take, the reasoner uses
a monitoring layer called the Monitoring Plane [18], which
intercepts monitoring reports containing perceived packet loss
levels from the home network. For the design of the reasoner, we proposed three distinct algorithms: one based on
an analytical modeling of the problem and two based on a
neural network, which is either trained using a supervised or
reinforcement learning technique [19]. We showed how the
three reasoning algorithms have a similar performance: as an
increasing amount of packet loss is introduced on the last
mile, the redundancy level is increased accordingly to maintain
a perfect QoE level. Additionally, the reasoner accurately
identifies network congestion and adaptively lowers the video
quality level when needed. Therefore, all three algorithms are
able to achieve the best possible QoE level, given the network
disruptions. When comparing the three reasoning algorithms,
we showed how the neural network-based algorithms have
the additional advantage of self-learning [14]. Especially for
the reinforcement learning approach [19], we showed how
the reasoner can actually be automatically generated through
simulation and the definition of a reward function, if the
problem domain can be modeled analytically.
2) Reliable data transfers, enforcing network fairness between misbehaving receivers: For reliable data transfers, we
presented a QoE optimizing technique that focuses on the
robustness of congestion avoidance algorithms against misbehaving TCP receivers. As congestion avoidance techniques
rely on the integrity of the end hosts, they are prone to
abuse from TCP subscribers. TCP subscribers that deliberately
ignore congestion warnings (e.g., ECN messages) can obtain
a higher throughput at the cost of reliable TCP subscribers
as they gradually increase their rate when reliable TCP subscribers lower their rate as response to a congestion signal. We
designed a cognitive mechanism, which can be deployed on
an intermediate router and has the goal of detecting and penalizing such misbehaving TCP subscribers to restore network
fairness. The algorithm uses a combination of clustering and
outlier detection to divide the connections into two groups
based on flow-based statistics, derived from monitoring the
ECN packets of each flow. Misbehaving connections are penalized by assigning them to a different AQM category that drops
the ECN signals: as such, misbehaving connections are forced
to lower their throughput sooner than their responsive counter
parts. We have shown that (1) misbehaving TCP receivers are
able to establish a throughput which is 4 times higher than
well-behaving TCP receivers, (2) our cognitive mechanism is
able to detect these misbehaving receivers with an accuracy
of more than 80% if the throughput differences are small
and 100% when the throughput differences are substantial
and (3) the differentiated AQM treatment allows successfully
limiting the gain misbehaving connections achieve compared
to well-behaving connections to 2% and less, thus restoring
the network fairness.
D. Design of an admission control system, optimized for
protecting multimedia services
Although traditional Admission Control (AC) solutions have
already been widely deployed, the popularity of multimedia
services has introduced some novel challenges of deploying an
effective AC system in an IPTV environment. The standardized AC solutions such as those defined in the Diffserv [4]
and Intserv [5] architecture are too static to cope with the
high dynamics in the traffic characteristics of today’s videos.
The consequence is that they take a conservative approach
in dimensioning the amount of resources required. This dimensioning is based on the peak rate of the video, which
is a worst case scenario and therefore leads to a severe
underutilisation of the network. To address this, research
is currently moving towards Measurement Based Admission
Control (MBAC). In an MBAC system, the network load is
distributively measured at various demarcation points in the
network and these measurements are collected by specific
nodes that perform the admission decision. Currently, the
IETF is standardizing an MBAC system called Pre-Congestion
Notification (PCN) [20], which measures the network load at
each node and signals whether or not congestion is imminent
on that node, by comparing it with a threshold rate R, through
the marking of packets.
We have investigated PCN’s performance in protecting
multimedia services in [21]. We have shown that, although
using PCN for video services results in an increase in admitted
sessions, the configuration of the system is also very complex
and prone to either underutilisation or either over-admittance if
1000
E. A policy-based video rate adaptation mechanism
AC techniques are far more powerful if the traditional binary
decision of blocking or admitting a session is extended to
enable a graceful degradation of the QoE as the network load
increases. Video services are one of the few services that can
exhibit such a graceful degradation as they can intentionally
and willingly adapt their QoE through video rate adaptation.
At the video level, protocols such as Scalable Video Coding
(SVC) [25] are able to encode the video into different quality
levels and allow a smooth low-processing way of downscaling
the video. At the network-level, solutions are required to
ensure an optimized choice between these different qualities.
We designed an algorithm that allows a network provider
to control the QoE levels customers receive as a function
of the network load. Compared to the state of the art (e.g.,
the range of HTTP Adaptive Streaming (HAS) techniques
currently deployed such as Apple Live Streaming [26]) our
algorithm is specifically intended for network providers of an
IPTV environment: they can use the algorithm to define a
policy on which QoE levels are offered to the clients and as
such make a global optimization for their network. In contrast,
when using HAS, the clients are responsible for deciding on
which rate they want. As such, the operator does not have any
control on the QoE the clients receive and what is optimal
for the client might not be a global optimal for the network
in terms of revenue. The algorithm is deployed on any PCNenabled node and uses the measurement information generated
by the PCN system to locally adapt video quality levels that
are forwarded on that node. To control the received QoE levels,
the provider needs to define policies in the form of so-called
utility functions. These utility functions define the share of
a video quality as a function of the network load. What is
typically defined is that the share of the lower video quality
levels is gradually increased, while the share of the higher
video quality levels is gradually decreased, as the network
1
915
Excellent
QoE
900
0.9
700
651
0.8
600
500
0.7
400
0.6
300
SSIM Score (QoE estimation)
800
Number of admitted sessions
the traffic characteristics of the videos change or are not known
beforehand. To address this, and avoid the use of another
conservative configuration, we have augmented the standard
PCN mechanism by adding an autonomic layer on top that
automates PCN’s configuration based on the monitoring of the
underlying video traffic characteristics. The autonomic layer
consists of (1) a novel load measurement algorithm, which is
more robust against the burstiness of the video’s bitrate [21],
(2) an adaptive rate algorithm, which autonomically configures
the R threshold based on the variability of the traffic [22] and
(3) an optional buffering component which allows decreasing
the burstiness of the traffic at the expense of an increase
in delay [23]. We have evaluated both the standard and
augmented PCN mechanism and have shown that the introduction of the autonomic layer allows deploying PCN in an
IPTV environment and obtaining a higher network utilization
without needing to worry about the traffic characteristics of
the video. Moreover, based on the performance evaluation, we
have derived several guidelines which enable the configuration
of a PCN system for an IPTV environment [24].
Moderate
QoE
Poor
QoE
200
0.5
130
98
100
37
0
0.4
RACS
PCN
A-PCN
Scale50SD
Number of admitted sessions
Average SSIM Score
Maximum SSIM Score
ScaleHQWeb
Fig. 3. Influence of various admission control configurations on the network
utilisation and QoE.
load increases.
Evaluation results of different combinations of admission
control and video rate adaptation are shown in Figure 3. The
investigated combinations are: traditional admission control
mechanisms as used in the RACS layer of the TISPAN [27]
architecture, the standardized PCN mechanism (with and without the autonomic layer discussed in the previous section) and
the combination with two video rate adaptation configurations:
one case where, at a network load of 100%, 50% of the
videos are scaled to an SD resolution and 50% to a high
quality web video (Scale50SD) and another case where all
videos are scaled to a high quality web video (ScaleHQWeb).
The impact on the number of admitted connections and
QoE, estimated through the SSIM score, is shown for each
configuration. As shown, traditional admission control is able
to achieve a high SSIM score but the number of admitted
connections is very low (i.e., 37). Introducing an MBAC
approach such as PCN results in an increase in network
utilisation of 350%, but decreases the SSIM score to a poor
QoE as the traffic characteristics were not properly known
beforehand leading to a misconfiguration. The autonomic layer
(A-PCN) allows restoring this SSIM score again, with the
cost of a small reduction in network utilisation. Combining
this MBAC approach with the discussed video rate adaptation
algorithm can considerably increase the network utilization
with a limited QoE drop. Note that this drop still corresponds
with a moderate video quality. The difference with the QoE
drop caused by the traditional PCN configuration is threefold. First, a higher network utilisation can be achieved with
a smaller QoE drop. Second, the QoE drop is due to the
transmission of a lower video quality as opposed to data
loss, and finally, as the video rate adaptation algorithm only
downscales video if the network load is high, the maximum
experienced SSIM score is still the highest possible. As such,
the results show how, by setting a policy, the combination
of admission control and video rate adaptation allows for a
fine grained tuning of the QoE levels customers receive, while
avoiding any QoE deterioration due to congestion.
F. A context authoring process that automates the context
exchange between federated nodes
The proposed QoE optimizing techniques described above
all introduce a higher level of adaptiveness and intelligence
to optimize the delivery of video. However, the configuration
of one technique may influence the configuration of the other.
For example, as a FEC mechanism adds overhead there must
be a link with an MBAC system to ensure that admitted
connections are protected. There is thus a need for a distributed
coordination layer that is able to orchestrate the configuration
of these QoE optimizing techniques and is able to decide
which techniques need to be deployed when and where.
One of the first requirements of such a coordination layer
is the exchange of context. To be able to effectively coordinate the different QoE optimizing techniques, a distributed
coordination layer needs to have the right knowledge available
to base its coordination decisions on. Similarly, also the QoE
optimizing techniques require knowledge (e.g., the perceived
packet loss of the client) to perform their management tasks.
In our work, we have investigated the problem of context
exchange in federated environments from a generic perspective
and have applied it to both a multimedia network environment [28], [29] and a cloud environment [30]. Previous
work (e.g., Macedo et al. [31]) has argued the need for a
federated knowledge plane layer and presented the concept
of query adaptation techniques. In our work, we build further
upon this work by extending the query adaptation techniques
to more context-aware semantic approaches, which allow a
more greater flexibility in adapting the queries as they allow
reasoning on the context and thus deduce the most appropriate
context to exchange. In a typical publish subscribe system,
consumers of context subscribe to the context by defining filter
rules: only if the context matches the filter rules, the producers
forward the context to the consumers. We have proposed an
autonomic context exchange framework that automates the
generation of these filter rules. In the proposed framework,
the different entities define their contextual requirements in an
ontology. These contextual requirements can be made dependent on the context: through semantic reasoning a new set of
filter rules is constructed each time the context itself changes.
We have shown that this context exchange process enables a
highly dynamic environment where the context to transfer can
easily be defined through policies. Furthermore, we showed
that the filter rule generation takes less than 200 ms, which
makes it feasible to deploy in an on-line scenario.
G. Publications
The thesis is publicly available for download from [32]. The
results of this research have been published in several journals
and magazines: the contributions related to the QoE optimization on the last mile have been published in Elsevier Computer
Networks [14] and the contributions concerning admission
control and video rate adaptation have been published in IEEE
Communications Magazine [24] and Springer JNSM [33].
Our work has also been published in the proceedings of
well-known conferences and workshops in the community
such as IEEE CNSM [16], IEEE IM/NOMS [29], IEEE
Globecom [21], MACE [19], [28], IEEE ACNM [22], [34]
and others [15], [17], [35], [36]. Moreover, the thesis resulted
in significant contributions to publications on (i) QoE metric
design and monitoring [18], [37], [38], [39], [40], [41], (2)
semantic management [30], [42], [43], (3) architectural design [11], [44], [45], (4) queueing and scheduling [23], [46]
and (5) cloud management [47]. In summary, this Ph.D. resulted in 29 publications of which 7 in journals and magazines.
H. Collaborations
The results of our work have been performed in close
collaboration with partners from both industry and academia.
This work has been carried out in the context of 4 European
projects: FP6 IP MUSE, CELTIC RUBENS, FP7 STREP
ECODE and FP7 STREP OCEAN. Moreover, a few publications are the result of collaborations with Prof. John Strassner.
V. F UTURE PERSPECTIVES
The research presented in this work has introduced several
important new research directions. First, while we investigated
a first aspect of federated management being the exchange of
context between entities in the federation, additional research
towards the management of federations is needed to ensure
a QoE guaranteed delivery across administrative domains.
Several important challenges remain including the research
towards coordination algorithms between federated partners
and service matchmaking algorithms.
Second, although multimedia services have seen an increased popularity over the last years, a recent topic that
has experienced an important boost is the cloud computing
paradigm. We believe the concepts studied in the thesis can
also be applied to the management of cloud environments,
although likely new problems such as the overload of an
application server in the cloud can occur. Furthermore, a management layer for clouds will need to cooperate with a network
management layer. While a cloud environment was not studied
in this work, we believe an important research direction is the
translation of the obtained results to the paradigm of clouds
and the investigation of their performance on cloud-specific
cases such as server consolidation and application placement.
Third, as this work focused on the design of the lower layers
in the autonomic management architecture, being the QoE optimizing techniques, there is an increased need for distributed
control loops on the higher layers. Therefore, important research steps need to be taken to come to a reasoning process
with an emphasis on the distributed deployment. For this, it
will be necessary to strike a balance between introducing more
learning capabilities (e.g., through machine learning) and at
the same time providing sufficient insight in the details of the
reasoning process (e.g., through a semantic description of the
loop’s functionality). By combining these distributed control
loops with the results presented in the thesis, end-to-end QoE
guarantees can be made across management domains, which
is the ultimate goal of an autonomic management layer.
ACKNOWLEDGMENT
Steven Latré’s Ph.D. work is funded by grant of the Fund
for Scientific Research, Flanders (FWO-V).
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