Fair Quality of Experience (QoE) Measurements Related with Networking Technologies

Martínez-Yelmo, Isaías; Seoane, Isaac; Guerrero, Carmen

doi:10.1007/978-3-642-13315-2_19

Cited by 17 publications

(13 citation statements)

References 33 publications

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“…all these applications include social networks. Any unusual action that shows a different behavior of one user against all other users present in similar platform confirms the presence of an intrusion [10]. There are several studies proposed on the identification of such malicious users and there are several names given for it such as an outlier, abnormality, and anomaly and so on.…”

Section: Introduction Of Intrusionsmentioning

confidence: 93%

Privacy Preserving Big Data publishing- A scalable K-anonymization approach using MapReduce

Sharma¹,

Kumar²

2018

IJCA

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Networked data contain interconnected entities for which inferences are to be made. For example, web pages are interconnected by hyperlinks, research papers are associated by references, phone accounts are linked by calls, and conceivable terrorists are linked by communications. Networks have turned out to be ubiquitous. Correspondence networks, financial transaction networks, networks portraying physical systems, and social networks are all ending up noticeably progressively important in our everyday life. Regularly, we are interested in models of how nodes in the system influence each other (for example, who taints whom in an epidemiological system), models for predicting an attribute of intrigue in light of observed attributes of objects in the system. The technique of SVM is applied which will classify the data into malicious and non-malicious. In the previous study authors proposed various model for privacy preserving which are group based records, K-anonymity etc. In the existing models there are various problems like it affect data utilities, harm the data identifiers. In the research work, the hybrid approach has been designed to ensure data privacy which is based on attribute and data identifiers.

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Section: Introduction Of Intrusionsmentioning

confidence: 93%

Privacy Preserving Big Data publishing- A scalable K-anonymization approach using MapReduce

Sharma¹,

Kumar²

2018

IJCA

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“…Kuipers et al [7], for example, present an overview of techniques that can be used to measure QoE. Martinez-Yelmo et al [11] discuss "How QoE can be measured and how the district layer in a networking environment can influence (...) perceived QoE. "…”

Section: Motivationmentioning

confidence: 98%

A framework to evaluate performance from an application and user perspective

Kist

2011

2011 Australasian Telecommunication Networks and Applications Conference (ATNAC)

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Experience related performance measures are important for operational, commercial as well as usability reasons. Quality of Experience (QoE) is widely discussed as a measure of user perceived performance, no universal definition for QoE are available. Instead of moulding QoE into a one-fits-all parameter, this work identifies and defines four specific performance measures that are more descriptive than traditional QoE. They account for objective and subjective measures and focus on applications and tasks specific performance. The parameters include Quality of (an) Application (QoA) and Quality of Use (QoU). They are further divided into objective and subjective measures, i.e. objective QoA, subjective QoA, objective QoU and subjective QoU. Subjective QoA reassembles QoE widely discussed in technical literature; QoU provides a measure for quality of experience in a more general, non technical sense. It is proposed that these individual performance parameters provide a better picture of performance and allow stakeholders to make more specific decisions. The paper also introduces a set of sample performance evaluations for the Remote Access Laboratories application to demonstrate how this framework can be applied and how such parameters can be measured.

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“…Sanchez-Macian et al [40] use ontologies and rules in a framework to calculate QoE by the values of QoS parameters. In [29], authors organize network-related aspects into layers (similar to the TCP/IP model) and discuss how each layer affects QoE. Agboma and Liotta [1,2] use statical analysis to identify how to adjust QoS parameters while maintaining high QoE levels in multimedia systems.…”

Section: Qoe-oriented Systemsmentioning

confidence: 99%

“…Quality of Experience is distinct from the more traditional Quality of Service (QoS) metrics, as the latter is mainly focused on technology and performance in the technical perspective, and the former is a user-centric approach that considers users' goals [28,29].…”

Section: Qoe-oriented Systemsmentioning

confidence: 99%

Quality of experience in distributed databases

Costa

Furtado

2011

Distrib Parallel Databases

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This paper proposes an approach to improve the level of Quality of Experience (QoE) that distributed database systems provide.Quality of Experience is a measure of users' satisfaction when using a certain service or application. Therefore, the main objective of this paper is to provide mechanisms to increase users' satisfaction when accessing distributed database systems.In traditional database systems, users cannot specify execution-related constraints. Then, the database system cannot evaluate if user expectations are satisfied and neither the system can take corrective actions when necessary.In this work, we present the QoE-oriented distributed database system (QoE-DDB). It allow users to specify Data Access Requirements (DARs) and aims to please users by satisfying the DARs they define. We define a set of types of Data Access Requirements and propose some SQL extensions that enable users to specify execution-related requirements. Proposed types of DARs include execution deadline and priority, execution start and finish times, data availability and freshness degrees, and disconnected execution mode.In our QoE-DDB, each user's command is transformed into one or more tasks that are executed by data services. Community modules and local data services negotiate Service Level Objectives (SLOs) for each task, which improves the system's dependability. We propose both QoE-oriented scheduling and dynamic data placement strategies. Proposed architecture and scheduling strategies enable the system to be used in a wide range of distributed environments, from tightly-coupled homogeneous environments (e.g. composed by off-the-shelf computers connected by a LAN) Communicated by Amit Sheth. R.L.d.C. Costa ( ) · P. Furtado 362 Distrib Parallel Databases (2011) 29:361-396 to highly heterogeneous and geographically distributed systems, where data services have some degree of autonomy.Traditional performance indicators (e.g. throughput and response time) are not adequate to measure the QoE a system provides. We also propose some specialized Key Performance Indicators (KPIs) to estimate the QoE level a database system provides.Finally, we present experimental results obtained through the use of benchmark data and queries together with a prototype that implements proposed strategies. In our experiments, we consider realistic scenarios and compare proposed scheduling strategies with their best-effort oriented counterparts. Obtained results prove the importance of our QoE-oriented approach.

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Fair Quality of Experience (QoE) Measurements Related with Networking Technologies

Cited by 17 publications

References 33 publications

Privacy Preserving Big Data publishing- A scalable K-anonymization approach using MapReduce

Privacy Preserving Big Data publishing- A scalable K-anonymization approach using MapReduce

A framework to evaluate performance from an application and user perspective

Quality of experience in distributed databases

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