Quality of Experience (QoE) for Wireless Video Over Critical Communication Systems

2017 ◽  
pp. 132-167
Author(s):  
Emad Danish ◽  
Mazin I. Alshamrani
Keyword(s):  
Resource Allocation ◽  
Quality Of Service ◽  
Energy Consumption ◽  
Communication Systems ◽  
Quality Of Experience ◽  
Service Providers ◽  
Wireless Video ◽  
User Centric ◽  
Near Future

Video streaming is expected to acquire a massive share of the global internet traffic in the near future. Meanwhile, it is expected that most of the global traffic will be carried over wireless networks. This trend translates into considerable challenges for Service Providers (SP) in terms of maintaining consumers' Quality of Experience (QoE), energy consumption, utilisation of wireless resources, and profitability. However, the majority of Radio Resource Allocation (RRA) algorithms only consider enhancing Quality of Service (QoS) and network parameters. Since this approach may end up with unsatisfied customers in the future, it is essential to develop innovative RRA algorithms that adopt a user-centric approach based on users' QoE. This chapter focus on wireless video over Critical communication systems that are inspired by QoE perceived by end users. This chapter presents a background to introduce the reader to this area, followed by a review of the related up-to-date literature.

scholarly journals Adaptive Resource Allocation and Provisioning in Multi-Service Cloud Environments

2019 ◽  
pp. 372-381
Author(s):  
Anitha Nithya R ◽  
Saran A ◽  
Vinoth R
Keyword(s):  
Resource Allocation ◽  
Quality Of Service ◽  
Energy Consumption ◽  
Energy Aware ◽  
Research Issues ◽  
Allocation Policy ◽  
Fine Grained ◽  
Policy Problem ◽  
Machine Allocation

Minimizing the energy consumption and resource usage in cloud computing environment is one of the key research issues. Energy aware resource allocation is used to optimize the power consuming by computer resources and storage in cloud. The proposed system is to improve the utilization of computing resources and reduce energy consumption under workload independent quality of service constraints. Using migration for minimizing the number of active physical nodes the dynamic single threshold VM consolidation leverages fine-grained fluctuations in the workloads and continuously reallocates VMs . A genetic algorithm based power-aware scheduling of resource allocation (G-PARS) has been proposed to solve the dynamic virtual machine allocation policy problem. The experiment results show that strategy that has been proposed has a better performance than other strategies, not only in high Quality Of Service(QoS) but also in less energy consumption.

Advances in QoS/E Characterization and Prediction for Next Generation Mobile Communication Systems

Fuzzy Systems ◽  
2017 ◽  
pp. 1739-1765
Author(s):  
Charalampos N. Pitas ◽  
Apostolos G. Fertis ◽  
Dimitris E. Charilas ◽  
Athanasios D. Panagopoulos
Keyword(s):  
Quality Of Service ◽  
Communication Networks ◽  
Mobile Communication ◽  
Communication Systems ◽  
Quality Of Experience ◽  
Mobile Network ◽  
Mobile Users ◽  
Radio Access ◽  
Network Status

The scope of this work is to present a holistic approach in quality of service (QoS) and quality of experience (QoE) characterization and prediction in modern mobile communication networks. Analytically, multi radio access technologies have been deployed in order to deliver mobile services to quality demanded consumers. Quality of Experience (QoE) parameters describe the End-to-End (E2E) quality as experienced by the mobile users. These parameters are difficult to be measured and quantified. System Quality of Service (SQoS) parameters are metrics that are closely related to the network status, and defined from the viewpoint of the service provider rather than the service user. Moreover, E2E Service Quality of Service (ESQoS) parameters describe the QoS of the services and they are obtained directly from the QoE parameters by mapping them into parameters more relevant to network operators, service providers and mobile users. A useful technique for mobile network planning and optimization is to build reliable quality estimation models for mobile voice and video telephony service.

Advances in QoS/E Characterization and Prediction for Next Generation Mobile Communication Systems

2016 ◽  
pp. 512-535
Author(s):  
Charalampos N. Pitas ◽  
Apostolos G. Fertis ◽  
Dimitris E. Charilas ◽  
Athanasios D. Panagopoulos
Keyword(s):  
Quality Of Service ◽  
Communication Networks ◽  
Mobile Communication ◽  
Communication Systems ◽  
Quality Of Experience ◽  
Mobile Network ◽  
Mobile Users ◽  
Radio Access ◽  
Network Status

The scope of this work is to present a holistic approach in quality of service (QoS) and quality of experience (QoE) characterization and prediction in modern mobile communication networks. Analytically, multi radio access technologies have been deployed in order to deliver mobile services to quality demanded consumers. Quality of Experience (QoE) parameters describe the End-to-End (E2E) quality as experienced by the mobile users. These parameters are difficult to be measured and quantified. System Quality of Service (SQoS) parameters are metrics that are closely related to the network status, and defined from the viewpoint of the service provider rather than the service user. Moreover, E2E Service Quality of Service (ESQoS) parameters describe the QoS of the services and they are obtained directly from the QoE parameters by mapping them into parameters more relevant to network operators, service providers and mobile users. A useful technique for mobile network planning and optimization is to build reliable quality estimation models for mobile voice and video telephony service.

scholarly journals Exploiting Virtual Machine Commonality for Improved Resource Allocation in Edge Networks

2020 ◽  
Vol 9 (4) ◽  
pp. 58
Author(s):  
Hadeel Abdah ◽  
João Paulo Barraca ◽  
Rui L. Aguiar
Keyword(s):  
Mathematical Model ◽  
Resource Allocation ◽  
Quality Of Service ◽  
Virtual Machine ◽  
Service Providers ◽  
Mobile Users ◽  
5G Systems ◽  
Multi Access ◽  
Edge Networks

5G systems are putting increasing pressure on Telecom operators to enhance users’ experience, leading to the development of more techniques with the aim of improving service quality. However, it is essential to take into consideration not only users’ demands but also service providers’ interests. In this work, we explore policies that satisfy both views. We first formulate a mathematical model to compute End-to-End (E2E) delay experienced by mobile users in Multi-access Edge Computing (MEC) environments. Then, dynamic Virtual Machine (VM) allocation policies are presented, with the objective of satisfying mobile users Quality of Service (QoS) requirements, while optimally using the cloud resources by exploiting VM resource reuse.Thus, maximizing the service providers’ profit should be ensured while providing the service required by users. We further demonstrate the benefits of these policies in comparison with previous works.

A Stable Matching Algorithm for VM Migration to Improve Energy Consumption and QOS in Cloud Infrastructures

2014 ◽  
Vol 4 (2) ◽  
pp. 15-33
Author(s):  
Abdelaziz Kella ◽  
Ghalem Belalem
Keyword(s):  
Quality Of Service ◽  
Energy Consumption ◽  
Large Scale ◽  
Virtual Machines ◽  
Service Providers ◽  
Stable Matching ◽  
Coase Theorem ◽  
It Services ◽  
Deferred Acceptance

Cloud Computing is one of the fast spreading technologies for providing utility-based IT services to its users. Large-scale virtualized datacenters are established in order to provide these services. Based on a pay-as-you-go model, it enables hosting of pervasive applications from consumer, scientific, and business domains. However, datacenters hosting Cloud applications consume huge amounts of electrical energy, contributing to high operational cost for the service providers as well as for the service users. Energy consumption can be reduced by live migration of virtual machines (VM) as required and by switching off idle physical machines (PM). Therefore, we propose an approach that finds a stable matching fair to both VMs and PMs, to improve the energy consumption without affecting the quality of service, instead of favoring either side because of a deferred acceptance procedure. The approach presumes two dynamics thresholds, and prepares those virtual machines on the physical machines that the load is over one of the two presumed values to be migrated. Before migrating all those VMs, we use the Coase theorem to determine the number of VMs to migrate for optimal costs. Our approach aims to improve energy consumption of the datacenters, while delivering an expected Quality of Service.

scholarly journals Comparative Analysis of GSM Quality of Service in Effurun, Warri Delta State, Nigeria Using Petroleum Training Institute as a Case Study

2018 ◽  
pp. 1-9
Author(s):  
A. I. Idim ◽  
Uzairue Stanley Idiake
Keyword(s):  
Quality Of Service ◽  
Communication Systems ◽  
Service Providers ◽  
Signal Strength ◽  
Direct Impact ◽  
Telecommunication Services ◽  
To Come ◽  
Training Institute

Quality of service has become the major challenges being faced by telecommunication services users. Service providers (MTN, GLO and AIRTEL) have tried as much as possible to fight the disease that is faced by this sector by minimizing the losses that occurs during transmission from the transmitter to the end users, but signal losses still remain the major challenges in this generation and the generation to come. This is so because losses cannot be eliminated in communication systems and as such noise cannot be eliminated, losses has direct impact on the quality of service. This papers focuses on the signal strength in the selected locations using an application called network monitor to measure the signal strength in the selected locations, how the environments affect the quality of service and thereby drawing a conclusion on the best service provider to use by the occupants of these locations.

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