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From Service Level Agreements (SLA) to Experience Level Agreements (ELA): The challenges of selling QoE to the user

From Service Level Agreements (SLA) to Experience
Level Agreements (ELA): The Challenges of Selling
QoE to the User
Martín Varela, Patrick Zwickl, Peter Reichl, Min Xie, Henning Schulzrinne§
VTT Technical Research Centre of Finland, Oulu, Finland
Research Group Cooperative Systems (COSY), University of Vienna, Austria
Email: {patrick.zwickl|peter.reichl}
Telenor Research, Trondheim, Norway
§Columbia University, NY, USA
Abstract—In contrast to the rather network-centric
notion of Quality of Service (QoS), the concept of Qual-
ity of Experience (QoE) has a strongly user-centric per-
spective on service quality in communication networks
as well as online services. However, related research
on QoE so far has largely neglected the question of
how to operationalize quality differentiation and to
provide corresponding solutions tailored to the end
users. In this paper, we argue that the introduction of
Experience Level Agreements (ELA) as QoE-enabled
counterpiece to traditional QoS-based Service Level
Agreements (SLA) would provide a key step towards
being able to sell service quality to the user. Hence,
we investigate various ideas to exploit QoE awareness
for improving SLAs (ranging from internal aspects
like SLOs by service providers to completely novel
definitions of ELAs which are able to characterize
QoE explicitly), and discuss important problems and
challenges of the proposed transition as well.
Index Terms—Quality of Experience; Service Level
I. Introduction
For a long time, the question of how to define, provide
and measure service quality for end users has been of
utmost interest for network operators, application and ser-
vice providers, as well as their customers. With the advent
of packet-based communication, this has led, already in the
early nineties, to several attempts at thoroughly defining
Quality of Service (QoS) [1], [2]. In the two decades to fol-
low, the primary research directions have followed a rather
technology-driven understanding of QoS [3], [4], leading
among other things to the definition of clearly specified
network parameters as a prerequisite for arranging service-
related binding contracts between providers and users, i.e.,
Service Level Agreements (SLA).
However, in recent years a remarkable focus shift could
be observed in the industry as well as the research commu-
nity, (re-)establishing a more user-centric perspective on
service quality around the notion of Quality of Experience
(QoE) as an augmentation of QoS1. The reason for this
can be found in the realization that QoS measures do not
trivially translate into quality as experienced by the users.
Pursuing this idea, recent research has achieved remark-
able progress on corresponding metrics and measurement
methodologies as well as the relationship between QoS and
QoE for a broad variety of services and applications [7],
In this paper, we argue that the time has come to apply
those results also on the business domain. Therefore, we
propose to complement the mentioned paradigm change
from QoS to QoE by the analogous step from Service Level
Agreements (SLA) towards a novel type of contracts be-
tween providers and end users which take the user-centric
perspective on service quality into explicit account, and
which we propose to call ”Experience Level Agreements”
Such a concept, to the best of our knowledge, so far has
been only vaguely mentioned in very few rather specific
contexts, for instance facility management2and cloud
computing services3. We posit that the idea of guaranteed
service levels has to be prominently introduced to the area
of QoE, where it can play a key role for bringing QoE
into the networks and services. On the other hand, we
identify two main issues with currently available SLAs,
from the end-user’s point of view (be they consumer or
business users). Firstly, SLAs are often non-existing, or
when available, very IT services-oriented (ticket response
times, recovery times, availability), but do not convey
1In fact, while for instance ITU-T defines QoE as “the overall
acceptability of an application or service, as perceived subjectively by
the end user” and further mentions that this includes the complete
end-to-end system effects and may be influenced by user expectations
and context [5], other researchers went even further, for instance
characterizing QoE as “the degree of delight or annoyance of the user
of an application or service…” [6]
3cf [9] mentioning the need for “experience-oriented SLA”
much in terms of how well the service actually performs
for the user. Secondly, when SLAs are present and service
performance is a part of them, they mostly deal only on
low-level metrics, which do not (except in some specific
cases) easily relate to the quality experienced by the users.
We therefore introduce the notion of a QoE-oriented
SLA — an Experience Level Agreement — motivating
it from a business perspective, and discussing the main
challenges associated with implementing them in different
types of services.
We will start with a short overview of classical SLAs
(Sec. II), and how QoE can interact with them in cer-
tain contexts. We will then introduce and motivate their
QoE-driven ELA counterpart (Sec. III), following with a
discussion of the challenges, both technical and business-
related for making them operational (Sec. IV). Finally,
we will give concluding remarks and an outlook on ELA
in Sec. V.
II. A Brief History of SLAs
Service Level Agreements (SLAs) are a broad and well-
studied topic with a long history in the ICT domain. In
this section we will briefly cover the basic concepts and key
references related to the ideas presented later in this paper,
and will especially focus on the case of telecommunications
and online (over the top) services.
A. SLA Definition and Related Concepts
SLAs have been defined by ITIL [10] as an “agreement
between an IT Service Provider and a Customer. The SLA
describes the IT Service, documents Service Level Targets,
and specifies the responsibilities of the IT Service Provider
and the Customer. A single SLA may cover multiple IT
Services or multiple Customers”. The TM Forum [11]
provides an alternative definition as “a formal negotiated
agreement between two parties. It is a contract that exists
between the Service Provider (SP) and the Customer. It is
designed to create a common understanding about Quality
of Service (QoS), priorities, responsibilities, etc. SLAs
can cover many aspects of the relationship between the
Customer and the SP, such as performance of services,
customer care, billing, service provisioning, etc. However,
although a SLA can cover such aspects, agreement on the
level of service is the primary purpose of a SLA”.
Both definitions convey the same basic ideas; SLAs pro-
vide, among other things, an agreed-upon understanding
of the performance targets of a service. SLAs can cover a
wide variety of service aspects, ranging from performance
(e.g., network QoS) to maximum response times for service
tickets, and can also be applied to non-ICT services.
An SLA commonly has a set of Service Level Objectives
(SLO4) associated with it. These are the targets for the
4Note that our usage of the term SLO is in line with most of related
work, except for ITIL, which has defined the concept of SLR (Service
Level Requirement) for this purpose, while SLO is used with a rather
different meaning.
service level to be attained, and are often measured by a
set of Key Performance Indicators (KPI).
In general, SLAs can be characterized by:
A set of KPIs for the service in question, often aver-
aged values over a time period (e.g., monthly packet
loss averages), or dependability metrics (MTTF,
MTTR, etc.) [12], [13]
A clear way to measure those KPIs by either the
customer or the provider (or both).
Penalties for the cases where violations occur (e.g.,
service refunds, or fines).
Common KPIs used in SLAs are related to the availabil-
ity of the service (e.g., mean time to failure, mean time to
recovery), or to technical QoS parameters in the case of
network services, for example. However, those KPIs can
only be related to the end users perception of the system
performance, much less to its actual QoE. Hence, recently
the term Key Quality Indicators (KQI) has been used
to describe user-perceivable quality aspects of a service
via certain KPIs that directly affect the perceived quality
(e.g., packet losses for IP telephony services) [14]. KQIs
are in some cases very close to KPIs (e.g. the number of
sessions in which the start up delay of the service is higher
than a certain threshold), but for some services (notably
media) the can also be estimates of perceptual quality (e.g.
listening MOS for VoIP, or some estimation of audiovisual
quality for video) [15]. These latter KQIs would provide a
good basis for an ELA (cf. TM Forum recommendations
for KQIs [16]). There is a plethora of literature related to
SLAs, both regarding research and best practices. A recent
survey of European research efforts related to SLAs [9]
provides an excellent overview of on-going work in the
domain (with a focus on cloud services), as well as a meta-
model for an SLA life-cycle. With a more general focus,
the TM Forum has produced a comprehensive handbook
covering basic notions and concepts of SLAs [11] and SLA
management [13].
B. SLAs and QoE
Within the technical domain of SLAs, QoE models can
be a valuable tool for service providers, for example to use
as SLOs, e.g., ensuring that the MOS of a given service
remains above a given acceptability threshold. Having
sufficiently accurate parametric models for QoE [17]–[19]
or even less accurate dimensioning models [20], allows
in some cases to derive performance bounds for some of
the QoE-affecting service parameters, enabling for exam-
ple the choice of optimal (e.g., in terms of cost/quality
ratio) SLAs. In [21], the authors present a scenario in
which quality models provide an optimal choice of SLAs
between a SaaS provider and its upstream (IaaS, network)
providers, in order to attain the desired performance levels
to ensure the users’ QoE is sufficient, considering budget
QoE-based SLOs can also be used as part of inter-
carrier SLOs, or for OTT services, agreements between
content providers and network providers, for instance by
setting quality targets over a set of agreed upon (e.g.,
standardized) quality models for different service types.
III. ELA: User-centric Quality Level
When buying a service today, consumers commonly face
two issues: firstly, the service is provided on a best-effort
basis with no guarantees of any kind (a typical example
of this would be an ISP’s tiered data plans, which are
sold, e.g., as “100Mbps”, followed by copious amounts
of small print that indicate that what it really means is
“up to 100Mbps, under optimal circumstances, which will
probably never occur in practice”). In other cases (e.g.,
cloud services), performance or dependability guarantees
of any kind are rarely made. Secondly, when performance
or dependability are described to the end user, they are
described in technical terms that are not really relatable to
the quality experienced by the user when using the service.
On the other hand, for any user paying for a service,
there is an expectation that the service should work
reliably and properly, which is not currently addressed by
most service providers. This lack of quality guarantees in
most services, together with recent significant advances in
QoE modeling, opens an opportunity for service providers
to differentiate themselves from their competition and
increase their margins by offering customers minimum
QoE guarantees, or different types of guarantees based
on tiered subscription models. Depending on the nature
of the service in question (e.g., network connectivity vs.
online media vs. cloud services), the options available
to the providers in terms of what they can promise to
customers can vary significantly. In the case of media
services, for example, the tiers can include different base
quality levels based on resolution and encoding, but also
different assurances on the delivered quality itself. For
non-media services, the tiers could be based on guaranteed
resource allocation or response times.
For the successful selling of any kind of product, the
information disclosure process is key, whether it refers to
advertisement activities or clearly conveying the essence of
the product itself to the customer. Today’s network and
service market is dominated by figures and notions that
are difficult to communicate or even measure. In common
SLAs, network performance aspects may be conveyed in
terms of QoS metrics or may even only be specified by
aggregate bandwidth estimates or best effort rates. These
are not terms that end users understand or necessarily care
about, as they are not easily relatable to their experience
when using a given service. So far, there are no means
to market services with QoE guarantees to end-users.
We propose the concept of Experience Level Agreements
(ELAs) to both enable the effective communication of the
QoE to be expected for a set of services and to foster
new business practices based on providing a minimum
QoE guarantees to the users in terms they can actually
A. Definition
In line with the SLA definitions given above, we can
define an ELA as a special type of SLA designed to establish
a common understanding of the quality levels that the
customer will experience through the use of the service,
in terms that are clearly understandable to the customer
and to which he or she can relate.
Syntactically the ELA can be very similar to a common
SLA5, i.e., a product in a defined quality — availability,
consistency of performance, or resources — and price
is sold for specified period of time from a provider to
a customer. The existing frameworks for defining SLAs
should suffice to formally specify ELAs as well. However,
whereas SLAs comprise a set of low-level performance
metrics (e.g., QoS, availability), the ELA conveys the
performance of the service in terms of QoE (and QoE
only); possibly as a set of QoE indicators to which the
user can readily relate. These could be, for example, some
representation of MOS scores as star ratings, though as we
will discuss later, it is likely that new means for conveying
QoE information to users will be needed.
B. Scope
It is important to distinguish between consumer and
professional customer markets (expert users, wholesale
customers, corporations, etc.). ELAs, as proposed herein,
provide a clear way to convey the complex nature of
network and service quality to consumers and some
enterprise-type customers, and their exploration may indi-
rectly assist other commercial users through the availabil-
ity of the QoE monitoring tools or experience simulation
facilities required to operationalize ELAs.
Another restriction concerns the general applicability of
QoE-differentiated services and thus ELAs. Due to the
complexity and risk when providing service experience
guarantees between any destination pair of a certain ser-
vice type (e.g., due to transit service agreements), the
service usage needs be geographically narrowed down, e.g.,
a small region involving a limited number of ISPs.
This line of thought also leads to focusing on services for
which we are capable of both measuring the QoS and/or
QoE (e.g., active monitoring) at the user premises at peak
times for this service, and simulating the effect on the
QoE through “preview” capabilities, i.e., translating QoS
parameters to an experience. The actual ELA is then
handled via the client software used and is specific to this
service. Thus from the current point of view, a focus on
specifically selected Over-The-Top (OTT) services eases
the transition towards QoE marketisation via explicit
ELAs—see Fig. 1(a). More generic arrangements may be
5In what follows we will refer to thos SLAs that are not ELAs
simply as SLAs
provided by
Access ISP
Network service
content ELA
(a) Specific service contracts
arranged in the background
based on aggregate trac
demands, forecasts, geographical
distribution of services etc.
(b) Service-independent agreements
Fig. 1. The ELA ecosystem making use of SLA and QoE concepts.
enabled at later points in time (cf. Fig. 1(b)), as the
challenges are overcome.
C. ELA vs. SLA
ELA and SLA need to coexist in an end-to-end system,
where the SLA is the interface with the service and
content provider whereas the ELA is the interface with
the end users (cf. Fig. 1). The relationship between ELA
and SLA is analogous to that between QoE and QoS,
forming a chain from users to their back-end realization.
One commonly-studied research question in QoE relates
to the creation of mappings to translate QoS to some
dimensions of QoE (usually perceptual ones) and vice-
versa. An analogous mapping will be needed to derive SLA
parameters from the ELA, and conversely, to bound ELAs
based on the SLA parameters. In this sense, ELAs cannot
in general directly involve QoE (i.e., as experienced by the
user and including emotional, socio-economic and other
user factors) but rather an objective representation of it,
agreed upon by both providers and users.
D. In Operations
Operationally, experience levels need to be captured and
transferred to a contractual form. This could for exam-
ple be achieved by experience simulators that allow to
measure the user’s quality sensitivity for different service
types. Based on this assessment, users could choose their
desired experience level on a quality scale depicting the
available quality tiers (olympic model, ACR-5, star rat-
ings, or other), price, and typical service usage scenarios.
This would yield an ELA choice reflecting both quality
sensitivity and service preferences, which would then be
automatically translated to QoS parameters, via the QoE
models used, as done for instance in [21]. Whenever
service-aware QoS parameters cannot be explicitly defined,
QoS indicators may provide descriptors for aggregate QoS
bounds—e.g., peak bandwidth up to 10Mbps, latency
smaller than 150ms for the specified set of services at the
tested location. QoS parameters are essentially required in
the core network and across network domain borders due
to absence of direct customer contact and the requirement
to aggregate the demands of individual usage flows. For
the business side of ELAs, both customers and ISPs will
require the certainties about the imminent contract, for
which several strategies may exist: Firstly, ISPs may not
only aim at assisting customers to understand the product
offer (e.g., via experience simulators), but they may also
do active network measurements in order to understand
the network at the customer premises, i.e., probing the
QoS at peak times. By studying the performance of the
underlying network and infrastructure, a set of services
can retrieved in this way for which QoE guarantees can
be provided. The validation of an ELA, moreover, requires
reliable and trustworthy information accessible to both
contract parties. In practice, the measured QoS could be
reconstructed for a service to a QoE level estimate (i.e.,
an implicit QoE monitor) on whose account the contract
satisfaction is assessed.
Secondly, ELA validation and QoS-to-QoE transition
could be treated based on the closest access speed and QoS
measurements and crowd-sourced QoE ratings (by actual
customers). On that account, a less cost-intensive solution
may be constructed. For example, ISPs, regulators or an-
other objective third party could use existing network QoS
monitoring infrastructure in order to publish aggregate
results (possibly with regional granularity) for QoS at peak
times. The results would then automatically mapped to
aggregate QoE levels for commonly used services, e.g.,
YouTube, Netflix, Skype, and may feed ELA validation
mechanisms. Particular solutions for the operationaliza-
tion of ELAs are beyond the scope of this paper.
In any case, penalties, as compensations for not match-
ing the agreed QoE standards and the lost free or working
times, may then be issued on the basis of monthly pay-
backs or vouchers for future service usages. Those refunds
may (partially) be covered by insurances or may directly
affect the business figures. It is implicit in this framework
that on average, the added costs for operators, related
to penalties, will be offset by adequate pricing strategies.
That is, more demanding ELAs will carry heftier prices,
and possibly larger expected margins than best-effort
service tiers.
E. But, Why ELAs?
As discussed so far, and further in the following Sec-
tion, it seems pertinent to address the main reasons for
introducing the concept. A large majority of connectivity
options and services marketed today to consumers share
one of both of the following characteristics: they are
provided on a best-effort basis, and they are mostly sold
on a flat-rate pricing model6.
This has led, in many services, to a “race to the bottom”
effect in terms of pricing, which in the long run does not
benefit service providers, who see lower margins, or cus-
tomers, who are stuck with whatever quality of experience
the provider is able to deliver on this pricing model. On the
other hand, some studies [22] indicate that a non-trivial
percentage of customers are indeed willing to pay more for
better quality, with varying degrees of enthusiasm, ranging
from conservative spending, to higher levels of spending
which may even seem irrational. This, in principle, enables
new business opportunities for the service providers, who
can better address different segments of the market by
offering different QoE levels at suitable price points.
It stands to reason, then, that if such type of pricing
differentiation is put in practice, there will be a need for
users to make sure that they get their money’s worth of
QoE, and for providers to be accountable when they don’t.
This is precisely what an SLA is meant to do. However,
the traditional approach to SLA definition and monitoring
is not necessarily a good match for end-users, hence the
proposed ELA concept.
IV. The Challenges of ELAs
The idea of integrating QoE into SLAs, either implicitly
or as user-facing ELAs seems, as argued above, like a nat-
ural progression in the same vein as how QoS has evolved
towards QoE. There are, however, a number of open issues
that need to be worked out before this transition can
take place. In this section we discuss the main research
challenges and questions we have identified in this area.
A. Framework
Today, SLAs for communication services are not widely
spread for consumer-level use. Because of this, ELAs can
not yet build upon an existing and sufficient infrastructure
involving consumers, all involved ISPs, and potentially
also content providers. In particular, automatic mecha-
nisms for simplifying the contractual negotiations and
agreements cannot be assumed to be present. In 2013,
the EU FP7 project ETICS7has concluded with the
proposition of an automated end-to-end QoS agreement
concept based on SLAs [23], that does, however, not
6Network services do often have pricing tiers based on speed or
data transfer caps; higher speeds and higher transfer caps are more
costly. However, these tiers provide bounds as to how well the
connection can perform, rather than guaranteeing that it will perform
“at least this well” for any particular service.
7, last accessed: 2015-01-29.
include consumers. Despite this restriction, the complex-
ity of the proposed mechanism potentially explains the
limited endeavors for adopting similar concepts in the
industry. Apart from this, services and their customers
are in general spread around the globe, thus introducing
location considerations and requiring fine-grained end-
to-end service quality monitoring in order to attribute
contract breaches to subcontracting ISPs.
This entire range of SLA issues are very likely inherited
by ELAs, which mainly differ in their parameter selection
and semantic interpretation. Proper mechanisms have to
both understand the background transactions required
to enable ISP cooperation (binding contracts, revenue
sharing etc.) and their automated translation to consumer-
facing contracts. For this reason, ELA frameworks should
be based on automatic mechanisms (for end-to-end
be based on agreed-upon, measurable, technically
valid, communicable, and understandable metrics;
be resistant to regional usage variations, e.g., switch-
ing service caches or service providers requires statis-
tical modeling or other kind of treatment;
come with a cooperation framework among providers
as envisioned by [23].
Likewise, a standard set of APIs and a suitable mon-
itoring architecture is also needed, in order to simplify
the inter-domain (not only between carriers, but also be-
tween carriers and service/content providers) interaction
required. For the QoE monitoring aspects, an architecture
such as the one recently proposed by ETSI [24] could be
a good starting point.
B. Language
It is challenging to describe ELA in a single language
that can express technical quality requirements (e.g., QoS)
while being easily understood by customers. ELAs should,
thus, be
be expressed both formally and in terms understand-
able by customers, the latter in terms of QoE. This
poses some non-trivial questions on how to convey
what certain quality (e.g., a score of 4 on a 5-point
ACR scale) actually feels like.
convenient to measure by both service providers and
customers, i.e., it should be able to be quantified,
guaranteed, validated and maintained (e.g., in order
to also reduce complaint management efforts);
consistent across users and platforms, i.e., it should
be applicable to a range of user profiles in the service
domain, and to all the devices with which they access
the service in question8.
C. Marketing
One significant challenge in implementing ELAs lies
not in the technical aspects, but rather on the marketing
8This may actually vary depending on what service is under
side. The prevailing “best effort / flat rate” approach to
selling online services and network connectivity creates a
strong inertia, which may prove difficult to overcome. For
example, while sophisticated pricing schemes for connec-
tivity with differentiated QoS have been studied for a long
time [25], [26], their implementations remain elusive.
Similar issues are likely to occur when considering differ-
entiated QoE levels, unless effective marketing strategies
can be developed to address this problem.
V. Conclusions and Future Work
In this paper we have introduced the concept of Expe-
rience Level Agreements (ELA), as a QoE-oriented aug-
mentation of SLAs, with the aim of enabling new business
models based on providing different QoE guarantees for
users of online services. While the concept is easy to
motivate from the business / economic perspective, and
some studies suggest that users are indeed willing to pay
more for better quality, we describe several significant
challenges — both technical and business-related — to
address before ELAs can become operational.
Future work on this domain will address the challenges
explored in this paper, as well as expand the scope of ELAs
to business applications, e.g., for SaaS-type use cases.
Part of this work has been funded by the Euro-
pean Community’s 7th Framework Programme under
grant agreement no. 611366 (PRECIOUS); see www. for further details. M. Varela’s work
was partly funded by Tekes, the Finnish Funding Agency
for Technology and Innovation, in the context of the Celtic
Plus QuEEN project.
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... Similarly, SSIM values range from 0 to 1, with 0 being worst and 1 being best rating. Studies done in [19,20] concluded that PSNR value above 37 and SSIM value over 0.9 means the video/image is of good quality. illustrates frame by frame PSNR and SSIM comparison for correlated and non-correlated 'delay-loss' QoS metric when loss was 0.5%. ...
Conference Paper
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Most of the existing studies on mapping of Quality of Service (QoS) to Quality of Experience (QoE) ignores any correlation between the QoS metrics. Network emulators such as NetEm that are used to study various network situations, have similar limitations. NetEm's inability to emulate realistic long-tail packet latency and correlation between QoS metrics may have already resulted in doubtful QoS to QoE functions. In this paper, we analysed real-time cellular QoS metrics data, and observed that there is significant correlation between several QoS parameters. We used them to create bespoke distributions for NetEm using different statistical methods such as maximum likelihood estimation and cumulative distribution functions. These distributions were used to modify NetEm to address the lack of representative packet waiting-time distributions. In addition, the bespoke distributions were also used to provide a way to emulate correlated packet delay and loss in NetEm. This bespoke emulator was then used to observe the impact of correlated delay-loss metric on end user QoE of video streaming applications using PSNR, SSIM and VMAF objective evaluation techniques. The emulation results showed that bespoke emulator gives better QoE compared to default NetEm configuration for same Packet Loss Ratio (PLR).
... From a stakeholder perspective (e.g., service providers and owners), service quality evaluation is an indispensable business goal for harmonizing the end-users' experiences (QoE), agreed upon through a service level agreement (SLA). 3 The area of QoE deals with the understanding of end-users' perception of the services offered by the stakeholder. 4,5 However, conventional QoE techniques 5 mainly target multimedia communications and do not consider the performance and impact of Machine-to-Machine (M2M) communication and autonomous processes on QoE. ...
The Internet of Things (IoT) brings a set of unique and complex challenges to the field of Quality of Experience (QoE) evaluation. The state-of-the-art research in QoE mainly targets multimedia services, such as voice, video, and the Web, to determine quality perceived by end-users. Therein, main evaluation metrics involve subjective and objective human factors and network quality factors. Emerging IoT may also include intelligent machines within services, such as health-care, logistics, and manufacturing. The integration of new technologies such as machine-to-machine communications and artificial intelligence within IoT services may lead to service quality degradation caused by machines. In this article, we argue that evaluating QoE in the IoT services should also involve novel metrics for measuring the performance of the machines alongside metrics for end-users' QoE. This article extends the legacy QoE definition in the area of IoT and defines conceptual metrics for evaluating QoE using an industrial IoT case study.
... In this paper, when considering the individual user perspective, we consider a QoE mapping function as relating user perceived quality to system conditions, i.e., QoS parameters (please note that when assuming a broader view of QoE, this relationship can of course depend on additional context, user factors, etc.). In the context of customer retention and avoiding churn, it is well known that QoE is one of the main drivers for service and network providers when deploying their services [15][16][17]. Further, considering the provider's perspective, ensuring a certain level of QoE for a given user session entails a certain cost, results in a certain revenue, etc. ...
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With Quality of Experience (QoE) research having made significant advances over the years, service and network providers aim at user-centric evaluation of the services provided in their system. The question arises how to derive QoE in systems. In the context of subjective user studies conducted to derive relationships between influence factors and QoE, user diversity leads to varying distributions of user rating scores for different test conditions. Such models are commonly exploited by providers to derive various QoE metrics in their system, such as expected QoE, or the percentage of users rating above a certain threshold. The question then becomes how to combine (a) user rating distributions obtained from subjective studies, and (b) system parameter distributions, so as to obtain the actual observed QoE distribution in the system? Moreover, how can various QoE metrics of interest in the system be derived? We prove fundamental relationships for the derivation of QoE in systems, thus providing an important link between the QoE community and the systems community. In our numerical examples, we focus mainly on QoE metrics. We furthermore provide a more generalized view on quantifying the quality of systems by defining a QoE-based Service-level Quality Index. This index exploits the fact that quality can be seen as a proxy measure for utility. Following the assumption that not all user sessions should be weighted equally, we aim to provide a generic framework that can be utilized to quantify the overall utility of a service delivered by a system.
... Moreover, those who are driving MNOs are looking for new mechanisms to assess the degree of user satisfaction through QoE metrics, which involves many subjective factors unrelated to quality of service (QoS) evaluation metrics, which are largely based on network performance. However, they also involve assessing the mood of the user or discovering how to represent system responsiveness, and this leads to a special kind of service level agreements (SLA) which are designed to establish a common standard for the level of quality that the customer will experience from using the services and are also called experience level agreements (ELA) [4]. ...
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The rapid growth of the Internet and technological advances are forcing mobile operators to increasingly invest in network infrastructures. C-RAN and SDN are regarded as enabling technologies that can overcome the limitations faced by operators, by reducing costs, increasing scalability, and paving the way for the next generation of 5G cellular networks. In this paper, an architectural solution based on SDN and computational intelligence is proposed for C-RAN, which can adjust BBU-RRH mapping through network load balancing rules by predicting subjective and objective QoE metrics for UHD video streaming. The simulation results achieved gains between 59% and 129%, in scenarios without activating a new BBU and scenarios that involve activating a new BBU, respectively.
... Quality of Service (QoS): The quality of a mobile data services is delineated by several parameters, including but not limited to speed, packet loss, delay and jitter [49]. QoS is also affected by signal strength, network load and user device and application design; however OoS is also influenced by several extra factors that can be beyond the control of operators (such as the type of the devices, the application and propagation environment). ...
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Network Slicing (NS) is an evolving area of research, performing a logical arrangement of resources to operate as individual networks, hence allowing for massively customizable service and tenant requirements. NS, via the respective network architecture can enable an effective deployment of 5G networks and support a great variety of emerging use cases and/or related services. In this scope and with the aim of extending all potential network and service benefits, the concept of dynamic NS becomes a prominent feature of 5G allowing for connectivity and data processing tailored to specific customers’ requirements. We discuss several essential features and fundamental designing principles that can affect the realization of a reliable dynamic NS, capable of serving an immense multiplicity of 5G-based innovations, towards structuring a fully mobile and inclusive society. Furthermore, due to its context, dynamic NS can support digital transformation and mobilization of industry vertical customers, implicating for significant commercial potential. To this aim, we also discuss related perspectives for market growth coming from proposed business models, together with regulatory concerns that could affect future growth of dynamic NS.
Defining an ontology to support all possible scenarios in the Internet of Things (IoT) is challenging, given the range of IoT applications, the context in which they are used, and their continued evolution. Furthermore, a desire for flexibility and ability to accommodate all scenarios using a standardized approach renders this a complex operational and management environment. An approach taken by some researchers in defining ontologies is to integrate components from different ontological schemes into a single schema. This approach is rationalized through the subsequent ability to support interoperability across IoT deployments. It also allows niche areas in individual ontologies to be merged to produce a more encompassing approach. However, this continues to be a pieced-together strategy and depends on service provider intelligence to ensure all applications are fulfilled: service level agreements (SLAs) provisioned on this basis therefore continue to require manual intervention. In response to this gap, we have proposed an ontology for the IoT. The ontology is unique in that it incorporates details associated with customers and their preferences, such as their ability to tolerate the observed dataset becoming unavailable or the data collection frequency of a dataset changing. Furthermore, it supports ability to accommodate domain-specific angles when working on a cross-domain approach; we believe that this is key in overcoming the limitations in other IoT ontologies and works toward a single standardized ontology. Through accommodating domain-specific elements, it is our objective that Quality of Service (QoS) needs of the applications are fulfilled without a customer needing significant technical knowledge. Taking these unique aspects into account, we believe the ontology will facilitate automatic SLA provision, service setup, and service management throughout the SLA lifetime. This will help to support the business objectives of the Internet service provider. The ontology has been defined in our previous work. In this document, the use of these ontology terms to generate personalized SLAs is presented.
Despite the vital role of automated queuing system on organizational performance from human resource management literature, researchers have paid little attention on state-owned commercial entities and more specifically in Kenya. Studies have revealed controversial findings on the link between automated queuing system and organizational performance thus the need for further studies to bridge the knowledge gaps. The general objective of this study will be to examine the influence of automated queuing system on performance of selected state-owned commercial entities in Kenya. Three specific objectives will be examined. The first objective will be to determine the extent of adoption of automated queuing systems in the selected State-owned entities in Kenya. The second objective will be to establish the influence of automated queuing systems on performance of selected State-owned entities in Kenya. The third objective will be to assess the challenges experienced by selected State-owned entities in Kenya when implementing automated queuing system policies and the fourth objective will be to ascertain ways of mitigating the challenges of implementing automated queuing system policies in State-owned entities in Kenya. This study will be informed by Queue management theory and technology acceptance theory. Exploratory research design will be utilized in this study. Desktop research analysis will be adopted. Published materials including peer-reviewed journals, conference papers, theses and reports relevant with the topic of the current study will be reviewed. Findings, conclusions and recommendations of this study will be derived from findings of previous empirical studies. Recommendations will be made in accordance with recommended protocols and guidelines of statistical literature. Further research will be recommended in other areas using different methodologies to facilitate collaboration of the results.
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It is often the case that in the current literature, the term “QoE” is used in contexts where “QoS” would be more appropriate. This is likely due to several reasons, one of which being the current popularity of all things related with QoE, but more fundamentally it is due to the boundaries between QoS and QoE not being clearly defined—and indeed, sometimes hard to define clearly. QoE is an intrinsically multi-disciplinary field, and practitioners from different backgrounds see it, quite naturally, from different perspectives colored by their own expertise. For networking people, in particular, QoE is sometimes seen as a simple extension, or even a re-branding, of the well-established concept of QoS. In this chapter we will delve into the differences and commonalities between the two, with the goal of easing and encouraging their proper use.
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The rapid evolution of the cloud market is leading to the emergence of new services, new ways for service provisioning and new interaction and collaboration models both amongst cloud providers and service ecosystems exploiting cloud resources. Service Level Agreements (SLAs) govern the aforementioned relationships by defining the terms of engagement for the participating entities.
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This paper presents a parametric packet-layer model for predicting perceived quality of High Definition (HD, 1920 × 1080) and Standard Definition (SD, 720 × 576) videos for IPTV services. The model can be applied both for network planning and service monitoring. It takes as input the video resolution, packet-layer information such as bit-rate, packet-loss-rate and burstiness factor, and information on the encoder/decoder settings such as the number of slices per frame and the packet-loss-concealment. Addressed degradations are compression artifacts related to H.264 and MPEG2 encoding, and transmission errors leading to slicing errors or freezing, depending on the applied packet-loss-concealment. Extensive subjective video quality tests have been conducted for measuring the perceived quality of the original and degraded video sequences. The model is developed based on the obtained test results. The model prediction shows a correlation of 0.98 for HD and 0.96 for SD with the mean subjective ratings.
Conference Paper
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This work proposes a XML-based model for the specification of service level agreements (SLA). The model has XML elements to define a semantic to represent key performance indicators (KPI) and key quality indicators (KQI) and the relationship between them. Upper and lower thresholds are associated to the indicators in order to indicate warnings or errors conditions. The relationship between the indicators is expressed by reusable functions which are evoked by the XML-based model. An example of reusable function for calculating the KQI service availability based on KPI indicators is also presented in this paper.
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This paper presents an audiovisual quality model for IPTV services. The model estimates the audiovisual quality of standard and high definition video as perceived by the user. The model is developed for applications such as network planning and packet-layer quality monitoring. It mainly covers audio and video compression artifacts and impairments due to packet loss. The quality tests conducted for model development demonstrate a mutual influence of the perceived audio and video quality, and the predominance of the video quality for the overall audiovisual quality. The balance between audio quality and video quality, however, depends on the content, the video format, and the audio degradation type. The proposed model is based on impairment factors which quantify the quality-impact of the different degradations. The impairment factors are computed from parameters extracted from the bitstream or packet headers. For high definition video, the model predictions show a correlation with unknown subjective ratings of 95%. For comparison, we have developed a more classical audiovisual quality model which is based on the audio and video qualities and their interaction. Both quality- and impairment-factor-based models are further refined by taking the content-type into account. At last, the different model variants are compared with modeling approaches described in the literature.
Introduction Requirements for service driven management The SLA Specification of level of service (SLS) Service contract chains SLA types SLA management (SLM) SLA modeling and representation Research projects and activities Conclusion Abbreviations and acronyms Bibliography
Conference Paper
Despite the fact that novel QoE based charging mechanism are vitally needed, the complex interrelation of payment and quality perception has been examined only marginally so far. In this paper we want to describe a comprehensive experiment which investigates the intricate interplay of content selection, quality decisions & evaluation and payment strategies in the context of a video on demand scenario. Beside depicting methodological challenges and providing recommendations for further empirical work, we also compare current findings with our previous work to reveal new sights and research attempts.
Conference Paper
With the availability of cloud computing infrastructures, migrating business-critical functionality to public clouds is becoming commonplace. Cloud providers typically offer a variety of computing capabilities and pricing options. Therefore, the problem of selecting the ones that suit enterprise needs becomes critical. Our major focus is on the migration of enterprise communication services, such as IP-telephony, to the cloud. We design a tool to assist in optimally deciding among the set of available hosting and network connectivity Service-Level Agreements (SLAs) under Quality-of-Experience (QoE) and budget constraints. In particular, we propose a multi-objective optimization framework making use of application-specific QoE estimation tools to tackle with the conflicting objectives of price and quality and demonstrate its application to a cloud-based teleconferencing service as a case study.
Conference Paper
The Weber-Fechner Law (WFL) is an important principle in psychophysics which describes the relationship be- tween the magnitude of a physical stimulus and its perceived intensity. With the sensory system of the human body, in many cases this dependency turns out to be of logarithmic nature. Re- cent quantitative QoE research shows that in several different scenarios a similar logarithmic relationship can be observed be- tween the size of a certain QoS parameter of the communication system and the resulting QoE on the user side as observed during appropriate user trials. In this paper, we discuss this surprising link in more detail. After a brief survey on the background of the WFL, we review its basic implications with respect to related work on QoE assessment for VoIP, most notably the recently published IQX hypothesis, before we present results of our own trials on QoE assessment for mobile broadband scenarios which confirm this dependency also for data services. Finally, we point out some conclusions and directions for further research.
The conversational quality of a VoIP communication is dependent on several factors such as the coding process used, the network conditions and the type of error correction or concealment employed. Furthermore, the quality perceived by the users is also dependent on the characteristics of the conversation itself. Assessing this kind of communication is a very difficult problem, and most of the studies available in the literature simplify the issue by restricting the analysis to only one or two parameters. However, the number of potentially affecting factors is typically higher, and their joint effect on quality is complex. In this paper we study the combined effects of bit rate, forward error correction, loss rate, loss distribution, delay and jitter on the perceived conversational quality. In order to achieve this we use the pseudo-subjective quality assessment (PSQA) technique, which allows us to obtain accurate, subjective-like assessments, in real time if necessary. Our contributions are thus twofold: firstly, we offer a detailed analysis of the impact of these parameters and their interactions on the perceived conversational quality. Secondly, we show how the PSQA methodology can be used to provide accurate conversational quality estimations.