Quality of service provisioning in network function virtualization: a survey

Mostafavi, Seyedakbar; Hakami, Vesal; Sanaei, Maryam

doi:10.1007/s00607-021-00925-x

Cited by 29 publications

(10 citation statements)

References 128 publications

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“…The purpose of this study is to analyze and compare network quality using the Quality of Service (QoS) parametric with the queue tree method and so researchers will conduct research using action research methods (action research). Action research is an iterative process that involves researchers and practitioners acting together on certain activity cycles, including problem diagnosis, action intervention, and reflective learning [9] [10] [11].…”

Section: Methodsmentioning

confidence: 99%

Comparative Analysis of Network Quality Using QOS Parameters on Mikrotik Routers Using the Queue Tree and Simple Queue Methods

Winarno¹,

Abdullah²,

Fajriana³

2023

JAIEM

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The research was conducted by researchers at Trienggadeng State Vocational School located on Jl Banda Aceh-Medan KM 152, Kec. Trienggadeng Regency. Pidie Jaya. This research is based on the importance of the network in the ongoing teaching and learning process in the network and computer labs, the network in the lab is currently not managed properly and the distribution of bandwidth is not evenly distributed. To get a good internet connection, it is necessary to distribute bandwidth evenly, which can be done using a proxy router. One of the features for distributing bandwidth evenly in the proxy router can be applied using the simple queue and queue tree methods, knowing network quality can be done using Quality of Service (QoS) measurements. This study aims to determine the analysis of network quality using Qos on proxy routers using the simple queue and queue tree methods. The method used in this study is to use the action research method (action research), the stages of the research are analyzing, data collection, data completeness, making network designs, and taking action. The results obtained for network analysis in a comparative study between simple queue and queue tree networks, the results for the delay obtained by the queue tree are 9 ms, packet loss = 0%, and throughput = 549 k. Whereas the simple queue obtains the value from the QoS measurement by performing calculations with the equation formula to obtain a value of delay = 8 ms, packet loss = 0.15% and throughput = 629 k. Based on the Tiphon standard, it can be concluded that for delay, a queue tree is better with a value above the simple queue with a very good index, the packet loss queue tree is better than the simple queue, the index is very good, the throughput of the simple queue is better compared to the throughput queue tree. This research shows that overall Queue Tree is more optimal for QoS.

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Section: Methodsmentioning

confidence: 99%

Comparative Analysis of Network Quality Using QOS Parameters on Mikrotik Routers Using the Queue Tree and Simple Queue Methods

Winarno¹,

Abdullah²,

Fajriana³

2023

JAIEM

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“…NFV allows software developers to run their software on generic shared hardware without the need for new dedicated or purpose-built hardware. NFV also increases scalability, efficiency, flexibility, and ease of management of the network [21], [26], [27], [28]. As shown in Fig.…”

Section: B Network Function Virtualization (Nfv)mentioning

confidence: 99%

Applications of Machine Learning in Resource Management for RAN-Slicing in 5G and Beyond Networks: A Survey

et al. 2022

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One of the key foundations of 5 th Generation (5G) and beyond 5G (B5G) networks is network slicing, in which the network is partitioned into several separated logical networks, taking into account the requirements of diverse applications. In this context, resource management is of great importance to instantiate and operate network slices and meet their performance and functional requirements. Resource management in Radio Access Networks (RANs) is associated with a range of challenges due to network dynamics and the specific requirements of each application while ensuring performance isolation. In this paper, we present a survey on state-of-the-art works that employ Machine Learning (ML) techniques in RAN slicing. We begin by reviewing the challenges, then we review the existing papers on resource management in a comprehensive manner, and classify the papers based on the used ML algorithm, the addressed challenges, and the type of allocated resources. We evaluate the maturity of current methods and state a number of open challenges and some solutions to address these challenges in RAN resource management.

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“…QoS is the capability of a network to provide the required services for selected network traffic. Quality assurance of network services is based on measuring QAs, where the key factors are: path length, throughput, latency/performance, security, hardware dependency, capacity/usability, and energy [34]. To make things worst, those QAs are negatively influencing one another.…”

Section: Qos In Software-defined Networkingmentioning

confidence: 99%

Quality-aware analysis and optimisation of virtual network functions

Munoz

Pinto

Fuentes

2022

Proceedings of the 26th ACM International Systems and Software Product Line Conference - Volume A

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The softwarisation and virtualisation of network functionality is the last milestone in the networking industry. Software-Defined Networks (SDN) and Network Function Virtualization (NFV) offer the possibility of using software to manage computer and mobile networks and build novel Virtual Network Functions (VNFs) deployed in heterogeneous devices. To reason about the variability of network functions and especially about the quality of a software product defined as a set of VNFs instantiated as part of a service (i.e., Service Function Chaining), a variability model along with a quality model is required.However, this domain imposes certain challenges to qualityaware reasoning of service function chains, such as numerical features or configuration-level Quality Attributes (QAs) (e.g., energy consumption). Incorporating numerical reasoning with quality data into SPL analyses is challenging and tool support is rare. In this work, we present 3 groups of operations: model report, aggregate functions to dynamically convert QAs at the feature-level into the configuration-level, and quality-aware optimisation. Our objective is to test the most complete reasoning tools to exploit the extended variability with quality attributes needed for VNFs. CCS CONCEPTS• Software and its engineering → Abstraction, modeling and modularity; Software product lines; Software performance; Requirements analysis; • Theory of computation → Automated reasoning; • Computing methodologies → Representation of mathematical objects.

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Quality of service provisioning in network function virtualization: a survey

Cited by 29 publications

References 128 publications

Comparative Analysis of Network Quality Using QOS Parameters on Mikrotik Routers Using the Queue Tree and Simple Queue Methods

Comparative Analysis of Network Quality Using QOS Parameters on Mikrotik Routers Using the Queue Tree and Simple Queue Methods

Applications of Machine Learning in Resource Management for RAN-Slicing in 5G and Beyond Networks: A Survey

Quality-aware analysis and optimisation of virtual network functions

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