A Dynamic Model for Quality of Service Evaluation of Heterogeneous Networks

A Dynamic Model for Quality of Service Evaluation of Heterogeneous Networks

Farnaz Farid (The University of Sydney, Sydney, Australia), Seyed Shahrestani (Western Sydney University, Sydney, Australia) and Chun Ruan (Western Sydney University, Sydney, Australia)
DOI: 10.4018/IJWNBT.2020070102


The quality of service (QoS) evaluation of heterogeneous networks is an interesting area of research. The traditional QoS evaluation methods usually use a set of network-centric parameters for the performance evaluation of these networks. As a result, using such methods it is not possible to report a comprehensive performance review of networks that would include diverse applications and technologies. To resolve this issue, in this research, a novel approach has been proposed that applies dynamic significance weights and unified metrics of the QoS-related parameters to various applications and technologies present within a heterogeneous network. The result analysis shows that by applying this methodical approach, the performance evaluation of heterogeneous networks can be carried out systematically and efficiently.
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The heterogeneous network architecture is an emerging paradigm that expands network capacity and coverage. However, effective QoS evaluation of such networks is still a critical issue (Zhu et al., 2019; Hwang et al., 2015, Farid, 2015). The challenges rise due to the composite nature of the underlying infrastructure, and the diverse characteristics of incorporated access networks and devices (Trestian, 2018). Therefore, the establishment of an integrated QoS metric to facilitate the performance evaluation of such networks is crucial (Ben Dhia et al., 2018; Comsa et al., 2018).

Many studies have assessed the performance of a single access technology or single application in a heterogeneous environment, for instance, see the work published in (Chen et al, 2018; Mohamed et al., 2019, Stamou et al., 2019). These studies focus on evaluating one single access technology and application at a time. Our work is an extension in the context of combining the performance of multiple access technologies and applications. The primary objective of our study is to measure the overall performance of heterogeneous networks considering the QoS levels of all underlying access technologies and applications. To do that we quantify the effects of each participating application, and access technology, in the form of an integrated QoS metric. The metric is then used to select the high-performing network configurations, for instance, when some particular services and varying numbers of users are concerned.

Several studies have evaluated heterogenous network-oriented service models through extensive simulation studies (Farid, Shahrestani, & Ruan, 2013b, Alshamrani et al., 2011). These studies confirm that different dynamics such as diverse technologies and a varying number of users make this a difficult task. For some applications, while the delay or jitter may be at an acceptable level, the packet loss may simply be too high. For instance, with the presence of twenty voice clients and one streaming client, in some network, the end-to-end delays of voice calls show an acceptable value. However, for the same number of users, concerning packet loss, the voice calls do not achieve a satisfactory performance level (Farid, Shahrestani, & Ruan, 2013a). Additionally, the effects of different communication technologies on the performance of various applications, say voice and video, must be accounted for efficiently and methodically. In such circumstances, a unified QoS metric that quantifies network performance as a singular entity is advantageous. Such metric provides a more realistic yet holistic view of the configuration that the analysis of each QoS parameter, application or access technology separately cannot offer.

Hence, the objective of this paper is to present a methodical approach to resolve some of the underlying QoS evaluation issues of Heterogenous networks discussed above. This is achieved by underpinning the application and network parameters together through a dynamic weight-based approach. Each QoS parameter, application and access technology are assigned a weight based on the underlying network context, for example, rural or urban area, number of users, the reasons for application usage etc. The performance results of all these entities are then combined using the weights to obtain a final performance result.

To discuss these points further, the rest of the paper is organized as follows. Background and Motivations section presents the literature review of this work. Section 3 discusses the proposed dynamic weight based QoS evaluation method. Section 4 and 5 elaborate on the various steps involved in the dynamic weight-based method. Section 6, 7 and 8 report the simulation studies along with the analysis of their results. The final section reflects on concluding remarks and future works.

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