scholarly journals Augmenting Speech Quality Estimation in Software-Defined Networking Using Machine Learning Algorithms

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3477
Author(s):  
Jan Rozhon ◽  
Filip Rezac ◽  
Jakub Jalowiczor ◽  
Ladislav Behan
Keyword(s):  
Network Performance ◽  
Service Providers ◽  
Software Defined Networking ◽  
Speech Quality ◽  
Machine Learning Algorithms ◽  
Link Quality ◽  
Multimedia Communications ◽  
Speech Communication ◽  
Sdn Controller

With the increased number of Software-Defined Networking (SDN) installations, the data centers of large service providers are becoming more and more agile in terms of network performance efficiency and flexibility. While SDN is an active and obvious trend in a modern data center design, the implications and possibilities it carries for effective and efficient network management are not yet fully explored and utilized. With most of the modern Internet traffic consisting of multimedia services and media-rich content sharing, the quality of multimedia communications is at the center of attention of many companies and research groups. Since SDN-enabled switches have an inherent feature of monitoring the flow statistics in terms of packets and bytes transmitted/lost, these devices can be utilized to monitor the essential statistics of the multimedia communications, allowing the provider to act in case of network failing to deliver the required service quality. The internal packet processing in the SDN switch enables the SDN controller to fetch the statistical information of the particular packet flow using the PacketIn and Multipart messages. This information, if preprocessed properly, can be used to estimate higher layer interpretation of the link quality and thus allowing to relate the provided quality of service (QoS) to the quality of user experience (QoE). This article discusses the experimental setup that can be used to estimate the quality of speech communication based on the information provided by the SDN controller. To achieve higher accuracy of the result, latency characteristics are added based on the exploiting of the dummy packet injection into the packet stream and/or RTCP packet analysis. The results of the experiment show that this innovative approach calculates the statistics of each individual RTP stream, and thus, we obtain a method for dynamic measurement of speech quality, where when quality decreases, it is possible to respond quickly by changing routing at the network level for each individual call. To improve the quality of call measurements, a Convolutional Neural Network (CNN) was also implemented. This model is based on two standard approaches to measuring the speech quality: PESQ and E-model. However, unlike PESQ/POLQA, the CNN-based model can take delay into account, and unlike the E-model, the resulting accuracy is much higher.

scholarly journals Estimating PQoS of Video Conferencing on Wi-Fi Networks Using Machine Learning

2021 ◽  
Vol 13 (3) ◽  
pp. 63
Author(s):  
Maghsoud Morshedi ◽  
Josef Noll
Keyword(s):  
Machine Learning ◽  
Network Performance ◽  
Service Providers ◽  
Video Conferencing ◽  
Machine Learning Algorithms ◽  
Performance Parameters ◽  
High Definition ◽  
Internet Service ◽  
Novel Approach ◽  
5 Ghz

Video conferencing services based on web real-time communication (WebRTC) protocol are growing in popularity among Internet users as multi-platform solutions enabling interactive communication from anywhere, especially during this pandemic era. Meanwhile, Internet service providers (ISPs) have deployed fiber links and customer premises equipment that operate according to recent 802.11ac/ax standards and promise users the ability to establish uninterrupted video conferencing calls with ultra-high-definition video and audio quality. However, the best-effort nature of 802.11 networks and the high variability of wireless medium conditions hinder users experiencing uninterrupted high-quality video conferencing. This paper presents a novel approach to estimate the perceived quality of service (PQoS) of video conferencing using only 802.11-specific network performance parameters collected from Wi-Fi access points (APs) on customer premises. This study produced datasets comprising 802.11-specific network performance parameters collected from off-the-shelf Wi-Fi APs operating at 802.11g/n/ac/ax standards on both 2.4 and 5 GHz frequency bands to train machine learning algorithms. In this way, we achieved classification accuracies of 92–98% in estimating the level of PQoS of video conferencing services on various Wi-Fi networks. To efficiently troubleshoot wireless issues, we further analyzed the machine learning model to correlate features in the model with the root cause of quality degradation. Thus, ISPs can utilize the approach presented in this study to provide predictable and measurable wireless quality by implementing a non-intrusive quality monitoring approach in the form of edge computing that preserves customers’ privacy while reducing the operational costs of monitoring and data analytics.

scholarly journals Estimating PQoS of Video Streaming on Wi-Fi Networks Using Machine Learning

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 621
Author(s):  
Maghsoud Morshedi ◽  
Josef Noll
Keyword(s):  
Machine Learning ◽  
Video Streaming ◽  
Network Performance ◽  
Service Providers ◽  
Perceived Quality ◽  
Internet Service Providers ◽  
Performance Parameters ◽  
Internet Service ◽  
Access Points

Video on demand (VoD) services such as YouTube have generated considerable volumes of Internet traffic in homes and buildings in recent years. While Internet service providers deploy fiber and recent wireless technologies such as 802.11ax to support high bandwidth requirement, the best-effort nature of 802.11 networks and variable wireless medium conditions hinder users from experiencing maximum quality during video streaming. Hence, Internet service providers (ISPs) have an interest in monitoring the perceived quality of service (PQoS) in customer premises in order to avoid customer dissatisfaction and churn. Since existing approaches for estimating PQoS or quality of experience (QoE) requires external measurement of generic network performance parameters, this paper presents a novel approach to estimate the PQoS of video streaming using only 802.11 specific network performance parameters collected from wireless access points. This study produced datasets comprising 802.11n/ac/ax specific network performance parameters labelled with PQoS in the form of mean opinion scores (MOS) to train machine learning algorithms. As a result, we achieved as many as 93–99% classification accuracy in estimating PQoS by monitoring only 802.11 parameters on off-the-shelf Wi-Fi access points. Furthermore, the 802.11 parameters used in the machine learning model were analyzed to identify the cause of quality degradation detected on the Wi-Fi networks. Finally, ISPs can utilize the results of this study to provide predictable and measurable wireless quality by implementing non-intrusive monitoring of customers’ perceived quality. In addition, this approach reduces customers’ privacy concerns while reducing the operational cost of analytics for ISPs.

scholarly journals Multipath Dynamic Adaptive Streaming over HTTP Using Scalable Video Coding in Software Defined Networking

2020 ◽  
Vol 10 (21) ◽  
pp. 7691
Author(s):  
Ali Gohar ◽  
Sanghwan Lee
Keyword(s):  
Video Coding ◽  
Network Topology ◽  
Service Providers ◽  
Scalable Video Coding ◽  
Software Defined Networking ◽  
Multimedia Streaming ◽  
Scalable Video ◽  
Adaptive Streaming ◽  
Network Bandwidth

Dynamic Adaptive Streaming over HTTP (DASH) offers adaptive and dynamic multimedia streaming solutions to heterogeneous end systems. However, it still faces many challenges in determining an appropriate rate adaptation technique to provide the best quality of experience (QoE) to the end systems. Most of the suggested approaches rely on servers or client-side heuristics to improve multimedia streaming QoE. Moreover, using evolving technologies such as Software Defined Networking (SDN) that provide a network overview, combined with Multipath Transmission Control Protocol (MPTCP), can enhance the QoE of streaming multimedia media based on scalable video coding (SVC). Therefore, we enhance our previous work and propose a Dynamic Multi Path Finder (DMPF) scheduler that determines optimal techniques to enhance QoE. DMPF scheduler is a part of the DMPF Scheduler Module (DSM) which runs as an application over the SDN controller. The DMPF scheduler accommodates maximum client requests while providing the basic representation of the media requested. We evaluate our implementation on real network topology and explore how SVC layers should be transferred over network topology. We also test the scheduler for network bandwidth usage. Through extensive simulations, we show clear trade-offs between the number of accommodated requests and the quality of the streaming. We conclude that it is better to schedule the layers of a request into the same path as much as possible than into multiple paths. Furthermore, these result would help service providers optimize their services.

scholarly journals Intelligent Performance Prediction: The Use Case of a Hadoop Cluster

Electronics ◽  
2021 ◽  
Vol 10 (21) ◽  
pp. 2690
Author(s):  
Dimitris Uzunidis ◽  
Panagiotis Karkazis ◽  
Chara Roussou ◽  
Charalampos Patrikakis ◽  
Helen C. Leligou
Keyword(s):  
Service Providers ◽  
Single Layer ◽  
Machine Learning Algorithms ◽  
Service Characteristics ◽  
Potential System ◽  
Break Points ◽  
Network Functions ◽  
Hadoop Cluster ◽  
Service Life Cycle

The optimum utilization of infrastructural resources is a highly desired yet cumbersome task for service providers to achieve. This is because the optimal amount of such resources is a function of various parameters, such as the desired/agreed quality of service (QoS), the service characteristics/profile, workload and service life-cycle. The advent of frameworks that foresee the dynamic establishment and placement of service and network functions further contributes to a decrease in the effectiveness of traditional resource allocation methods. In this work, we address this problem by developing a mechanism which first performs service profiling and then a prediction of the resources that would lead to the desired QoS for each newly deployed service. The main elements of our approach are as follows: a) the collection of data from all three layers of the deployed infrastructure (hardware, virtual and service), instead of a single layer of the deployed infrastructure, to provide a clearer picture on the potential system break points, b) the study of well-known container based implementations following that microservice paradigm and c) the use of a data analysis routine that employs a set of machine learning algorithms and performs accurate predictions of the required resources for any future service requests. We investigate the performance of the proposed framework using our open-source implementation to examine the case of a Hadoop cluster. The results show that running a small number of tests is adequate to assess the main system break points and at the same time to attain accurate resource predictions for any future request.

Software-Defined Networking’s Traffic Engineering Benefits the Improvement of Network Performance

2020 ◽  
Vol 17 (1) ◽  
pp. 27-31
Author(s):  
B. Naveen Chandar ◽  
N. Arivazhagan ◽  
K. Venkatesh
Keyword(s):  
Quality Of Service ◽  
Traffic Engineering ◽  
Traffic Management ◽  
Network Performance ◽  
Packet Delay ◽  
Software Defined Networking ◽  
Delivery Ratio ◽  
Network Simulator ◽  
Network Resource

Quality of Service is considered as one of the important specifications in Software Defined Networking and we are focusing on Traffic Engineering which is capable of managing traffic characteristics like bandwidth for improving network performance. In this paper, performance evaluation of Quality of Service parameters such as Packet Delivery Ratio, Packet Delay and Packet Loss are performed with Network simulator 2 for all types of Software Defined Networking topologies. To do such evaluation on these parameters we use Traffic Engineering, which helps on improving the network performance, design mechanisms for routing to manage the traffic in network by improving the network resource usages and other major Quality of Service requisites. So in this proposed methodology, we use point-to-point topology related to traffic calculation which includes network parameters like general calculation of a framework, analyzing the traffic and future indication. Also the work process relevant to traffic management includes bandwidth of the traffic, scheduling of Quality of Service-assurance, saving power and management of traffic in Software Defined Networking. Existing technologies used for the above parameters are discussed below and our insights for future development on traffic engineering between the nodes in Software Defined Networking are offered.

scholarly journals Cyberattacks Detection in IoT-Based Smart City Applications Using Machine Learning Techniques

2020 ◽  
Vol 17 (24) ◽  
pp. 9347 ◽  
Author(s):  
Md Mamunur Rashid ◽  
Joarder Kamruzzaman ◽  
Mohammad Mehedi Hassan ◽  
Tasadduq Imam ◽  
Steven Gordon
Keyword(s):  
Machine Learning ◽  
Smart City ◽  
Service Providers ◽  
Detection System ◽  
Smart Cities ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Increased Risk ◽  
Iot Devices

In recent years, the widespread deployment of the Internet of Things (IoT) applications has contributed to the development of smart cities. A smart city utilizes IoT-enabled technologies, communications and applications to maximize operational efficiency and enhance both the service providers’ quality of services and people’s wellbeing and quality of life. With the growth of smart city networks, however, comes the increased risk of cybersecurity threats and attacks. IoT devices within a smart city network are connected to sensors linked to large cloud servers and are exposed to malicious attacks and threats. Thus, it is important to devise approaches to prevent such attacks and protect IoT devices from failure. In this paper, we explore an attack and anomaly detection technique based on machine learning algorithms (LR, SVM, DT, RF, ANN and KNN) to defend against and mitigate IoT cybersecurity threats in a smart city. Contrary to existing works that have focused on single classifiers, we also explore ensemble methods such as bagging, boosting and stacking to enhance the performance of the detection system. Additionally, we consider an integration of feature selection, cross-validation and multi-class classification for the discussed domain, which has not been well considered in the existing literature. Experimental results with the recent attack dataset demonstrate that the proposed technique can effectively identify cyberattacks and the stacking ensemble model outperforms comparable models in terms of accuracy, precision, recall and F1-Score, implying the promise of stacking in this domain.

scholarly journals A Speech Quality Classifier based on Tree-CNN Algorithm that Considers Network Degradations

2020 ◽  
Vol 16 (2) ◽  
pp. 180-187
Author(s):  
Samuel Terra Vieira ◽  
Renata Lopes Rosa ◽  
Demóstenes Zegarra Rodríguez
Keyword(s):  
Quality Assessment ◽  
Service Providers ◽  
Signal To Noise Ratio ◽  
Assessment Method ◽  
Speech Quality ◽  
Speech Communication ◽  
Training Time ◽  
Wired Networks ◽  
Speech Quality Assessment ◽  
Network Tree

Many factors can affect the users’ quality of experience (QoE) in speech communication services. The impairment factors appear due to physical phenomena that occur in the transmission channel of wireless and wired networks. The monitoring of users’ QoE is important for service providers. In this context, a non-intrusive speech quality classifier based on the Tree Convolutional Neural Network (Tree-CNN) is proposed. The Tree-CNN is an adaptive network structure composed of hierarchical CNNs models, and its main advantage is to decrease the training time that is very relevant on speech quality assessment methods. In the training phase of the proposed classifier model, impaired speech signals caused by wired and wireless network degradation are used as input. Also, in the network scenario, different modulation schemes and channel degradation intensities, such as packet loss rate, signal-to-noise ratio, and maximum Doppler shift frequencies are implemented. Experimental results demonstrated that the proposed model achieves significant reduction of training time, reaching 25% of reduction in relation to another implementation based on DRBM. The accuracy reached by the Tree-CNN model is almost 95% for each quality class. Performance assessment results show that the proposed classifier based on the Tree-CNN overcomes both the current standardized algorithm described in ITU-T Rec. P.563 and the speech quality assessment method called ViSQOL.

scholarly journals Different Routing Protocols for Wireless Body Sensor Area Network

2019 ◽  
Vol 8 (11) ◽  
pp. 3360-3363
Keyword(s):  
Network Performance ◽  
Data Dissemination ◽  
Life Time ◽  
Link Quality ◽  
Sensor Nodes ◽  
Area Network ◽  
Body Movements ◽  
Dynamic Changes ◽  
Efficient Data

Locomotion produced by body movements in Wireless Body Area Networks (WBANs) affects the link-quality of intra-BAN and inter-BAN interacting units, that, in turn, changes the Quality-of-Service (QoS) of individual WBAN, that includes reliability, efficient data transmission and network throughput . Further, the variation in link quality In central of WBANs and Access Points (APs) makes the WBAN-equipped cold-blooded more resource-constrained in nature, which also varies the data dissemination delay. Therefore, to lessen the DDA of the network, WBANs send Cold-blooded’ physiologic info to local servers using the proposed opportunistic transient connectivity establishment algorithm. Additionally, limb/body movements induce dynamic changes to the on-body network topology, which, in turn, increases the network management cost and decreases the life-time of the sensor nodes periodically. Simulation results show significant improvement in the network performance compared to the existing solutions

scholarly journals LSEA: Software-Defined Networking-Based QoS-Aware Routing Mechanism for Live-Soccer Event Applications in Smart Cities

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Yingcheng Zhang ◽  
Gang Zhao
Keyword(s):  
Network Performance ◽  
Smart Cities ◽  
Routing Algorithm ◽  
Software Defined Networking ◽  
Live Streaming ◽  
Streaming Applications ◽  
Delay Jitter ◽  
Building Process ◽  
Routing Mechanism

The smart cities provide a better connection between services and citizens based on new Internet technologies. During the building process of smart cities, some burgeoning applications have been emerging and changing the daily lifestyle of people, e.g., live streaming applications. Especially, the live-soccer event applications have attracted much attention and can improve people’s enjoyment of life to a great extent, such as the Europe five major league matches and FIFA world cup. For such applications, the traditional routing strategies cannot do Quality-of-Service (QoS) awareness, and thus, the network performance and the Quality of Experience (QoE) of users cannot be guaranteed. In this paper, we employ Software-Defined Networking (SDN) to make QoS awareness for the special live-soccer event applications, in which the QoS-aware routing mechanism is proposed, called LSEA. Meanwhile, delay, delay jitter, and packet loss rate are considered as three objects. On this basis, the improved Dijkstra routing algorithm and SDN-based disjoint routing algorithm are devised. Finally, the proposed LSEA is implemented over Mininet, and the experimental results demonstrate its feasibility and efficiency.

scholarly journals AN INTELLIGENT SOFTWARE DEFINED NETWORKING CONTROLLER COMPONENT TO DETECT AND MITIGATE DENIAL OF SERVICE ATTACKS

2020 ◽  
Vol 20 ◽  
Author(s):  
Huseyin Polat ◽  
Onur Polat
Keyword(s):  
Network Performance ◽  
Denial Of Service ◽  
Software Defined Networking ◽  
Security Threats ◽  
Simulation Environment ◽  
Dos Attacks ◽  
Device Identification ◽  
Intelligent Software ◽  
And Performance ◽  
Sdn Controller

Despite many advantages of software defined networking (SDN) such as manageability, scalability, and performance, it has inherent security threats. In particular, denial of service (DoS) attacks are major threats to SDN. The controller’s processing and communication abilities are overwhelmed by DoS attacks. The capacity of the flow tables in the switching device is exhausted due to excess flows created by the controller because of malicious packets. DoS attacks on the controller cause the network performance to drop to a critical level. In this paper, a new SDN controller component was proposed to detect and mitigate DoS attacks in the SDN controller. POX layer three controller component was used for underlying a testbed for PacketIn messages. Any packet from the host was incremented to measure the rate of packet according to its device identification and its input port number. Considering the rate of packets received by the controller and threshold set, malicious packets could be detected and mitigated easily. A developed controller component was tested in a Mininet simulation environment with an hping3 tool to build artificial DoS attacks. Using the enhanced controller component, DoS packets were prevented from accessing the controller and thus, the data plane (switching devices) was prevented from being filled with unwanted flows.

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