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10.1109/ACCESS.2021.3063769

The results reveal current 3GPP deficits in its QoS model to address the bufferbloat and the contribution of the segmentation/reassembly procedure to the total delay.

Preventing RLC Buffer Sojourn Delays in 5G

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10.1016/J.MATPR.2020.12.536

This paper intends to encourage inquiries in this area by examining cutting-edge approaches to cloud-based QoS modeling.

WITHDRAWN: A review on modeling techniques of quality-of-service

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10.1109/TNSM.2021.3074547

Then, a tensor-based QoS model is proposed to construct multi-dimensional relationships between QoS evaluation factors and the QoS values of service providers based on the collected reference reports.

A Tensor-Based Approach for the QoS Evaluation in Service-Oriented Environments

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10.35940/ijitee.j9412.08101021

To prune the network connection problem IPv6 a QoS model has been incorporated.

Taguchi Approach for Determining the Most Essential Aspect in Enhancing the RPL Performance

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10.1145/3448734.3450872

Considering the factors such as CPU, memory, disk, bandwidth and the number of containers, the QoS model is established and the scheduling algorithm is improved to obtain the optimal allocation of resource scheduling.

A container cloud scheduling strategy based on QoS

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10.1109/VTCSPRING.2019.8746404

Moreover, assessing user applications QoE can hardly be done using only simulated environments while the QoS modeling of a satellite system can often lead to non-conclusive or ambiguous results.

Making Trustable Satellite Experiments: An Application to a VoIP Scenario

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10.1002/cpe.4463

For a given cloud‐based software service, its QoS model is firstly trained on history data, which is capable to predict the QoS value as output by using the information on workload and allocated resources as inputs.

Self‐learning and self‐adaptive resource allocation for cloud‐based software services

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10.1007/978-3-030-58628-7_1

Our present work focuses on the use of stochastic methods, particularly, the Markov Decision Process to deliver a QoS model of the smart application in relation to the available Cloud computing resources.

Algorithms for a Smart Construction Environment

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10.1109/JIOT.2019.2943284

The contributions of this article are: 1) the problem of managing QoS while trading CoT resources is investigated as an optimization problem; 2) a QoS model is proposed to solve the problem by optimizing five different QoS objectives; and 3) experimental evaluation of the proposed model using three optimization algorithms.

A Multiobjective QoS Model for Trading Cloud of Things Resources

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10.1504/IJAHUC.2019.099638

The proposed solutions in the literature are broadly classified into four categories, namely: QoS routing protocols, QoS signalling, QoS-aware MAC protocols and QoS models, which are the main concern of our study.

A critical review of quality of service models in mobile ad hoc networks

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Qos Model - The results reveal current 3GPP deficits in its QoS model to address the bufferbloat and the contribution of the segmentation/reassembly procedure to the total delay. [1] This paper intends to encourage inquiries in this area by examining cutting-edge approaches to cloud-based QoS modeling. [2] Then, a tensor-based QoS model is proposed to construct multi-dimensional relationships between QoS evaluation factors and the QoS values of service providers based on the collected reference reports. [3] To prune the network connection problem IPv6 a QoS model has been incorporated. [4] Considering the factors such as CPU, memory, disk, bandwidth and the number of containers, the QoS model is established and the scheduling algorithm is improved to obtain the optimal allocation of resource scheduling. [5] Moreover, assessing user applications QoE can hardly be done using only simulated environments while the QoS modeling of a satellite system can often lead to non-conclusive or ambiguous results. [6] For a given cloud‐based software service, its QoS model is firstly trained on history data, which is capable to predict the QoS value as output by using the information on workload and allocated resources as inputs. [7] Our present work focuses on the use of stochastic methods, particularly, the Markov Decision Process to deliver a QoS model of the smart application in relation to the available Cloud computing resources. [8] The contributions of this article are: 1) the problem of managing QoS while trading CoT resources is investigated as an optimization problem; 2) a QoS model is proposed to solve the problem by optimizing five different QoS objectives; and 3) experimental evaluation of the proposed model using three optimization algorithms. [9] The proposed solutions in the literature are broadly classified into four categories, namely: QoS routing protocols, QoS signalling, QoS-aware MAC protocols and QoS models, which are the main concern of our study. [10]