249 research outputs found

    Investigating the Effects of Network Dynamics on Quality of Delivery Prediction and Monitoring for Video Delivery Networks

    Get PDF
    Video streaming over the Internet requires an optimized delivery system given the advances in network architecture, for example, Software Defined Networks. Machine Learning (ML) models have been deployed in an attempt to predict the quality of the video streams. Some of these efforts have considered the prediction of Quality of Delivery (QoD) metrics of the video stream in an effort to measure the quality of the video stream from the network perspective. In most cases, these models have either treated the ML algorithms as black-boxes or failed to capture the network dynamics of the associated video streams. This PhD investigates the effects of network dynamics in QoD prediction using ML techniques. The hypothesis that this thesis investigates is that ML techniques that model the underlying network dynamics achieve accurate QoD and video quality predictions and measurements. The thesis results demonstrate that the proposed techniques offer performance gains over approaches that fail to consider network dynamics. This thesis results highlight that adopting the correct model by modelling the dynamics of the network infrastructure is crucial to the accuracy of the ML predictions. These results are significant as they demonstrate that improved performance is achieved at no additional computational or storage cost. These techniques can help the network manager, data center operatives and video service providers take proactive and corrective actions for improved network efficiency and effectiveness

    Big Data - Supply Chain Management Framework for Forecasting: Data Preprocessing and Machine Learning Techniques

    Full text link
    This article intends to systematically identify and comparatively analyze state-of-the-art supply chain (SC) forecasting strategies and technologies. A novel framework has been proposed incorporating Big Data Analytics in SC Management (problem identification, data sources, exploratory data analysis, machine-learning model training, hyperparameter tuning, performance evaluation, and optimization), forecasting effects on human-workforce, inventory, and overall SC. Initially, the need to collect data according to SC strategy and how to collect them has been discussed. The article discusses the need for different types of forecasting according to the period or SC objective. The SC KPIs and the error-measurement systems have been recommended to optimize the top-performing model. The adverse effects of phantom inventory on forecasting and the dependence of managerial decisions on the SC KPIs for determining model performance parameters and improving operations management, transparency, and planning efficiency have been illustrated. The cyclic connection within the framework introduces preprocessing optimization based on the post-process KPIs, optimizing the overall control process (inventory management, workforce determination, cost, production and capacity planning). The contribution of this research lies in the standard SC process framework proposal, recommended forecasting data analysis, forecasting effects on SC performance, machine learning algorithms optimization followed, and in shedding light on future research

    DESiRED -- Dynamic, Enhanced, and Smart iRED: A P4-AQM with Deep Reinforcement Learning and In-band Network Telemetry

    Full text link
    Active Queue Management (AQM) is a mechanism employed to alleviate transient congestion in network device buffers, such as routers and switches. Traditional AQM algorithms use fixed thresholds, like target delay or queue occupancy, to compute random packet drop probabilities. A very small target delay can increase packet losses and reduce link utilization, while a large target delay may increase queueing delays while lowering drop probability. Due to dynamic network traffic characteristics, where traffic fluctuations can lead to significant queue variations, maintaining a fixed threshold AQM may not suit all applications. Consequently, we explore the question: \textit{What is the ideal threshold (target delay) for AQMs?} In this work, we introduce DESiRED (Dynamic, Enhanced, and Smart iRED), a P4-based AQM that leverages precise network feedback from In-band Network Telemetry (INT) to feed a Deep Reinforcement Learning (DRL) model. This model dynamically adjusts the target delay based on rewards that maximize application Quality of Service (QoS). We evaluate DESiRED in a realistic P4-based test environment running an MPEG-DASH service. Our findings demonstrate up to a 90x reduction in video stall and a 42x increase in high-resolution video playback quality when the target delay is adjusted dynamically by DESiRED.Comment: Preprint (Computer Networks under review

    Quality of service and dependability of cellular vehicular communication networks

    Get PDF
    Improving the dependability of mobile network applications is a complicated task for many reasons: Especially in Germany, the development of cellular infrastructure has not always been fast enough to keep up with the growing demand, resulting in many blind spots that cause communication outages. However, even when the infrastructure is available, the mobility of the users still poses a major challenge when it comes to the dependability of applications: As the user moves, the capacity of the channel can experience major changes. This can mean that applications like adjustable bitrate video streaming cannot infer future performance by analyzing past download rates, as it will only have old information about the data rate at a different location. In this work, we explore the use of 4G LTE for dependable communication in mobile vehicular scenarios. For this, we first look at the performance of LTE, especially in mobile environments, and how it has developed over time. We compare measurements performed several years apart and look at performance differences in urban and rural areas. We find that even though the continued development of the 4G standard has enabled better performance in theory, this has not always been reflected in real-life performance due to the slow development of infrastructure, especially along highways. We also explore the possibility of performance prediction in LTE networks without the need to perform active measurements. For this, we look at the relationship between the measured signal quality and the achievable data rates and latencies. We find that while there is a strong correlation between some of the signal quality indicators and the achievable data rates, the relationship between them is stochastic, i.e., a higher signal quality makes better performance more probable but does not guarantee it. We then use our empirical measurement results as a basis for a model that uses signal quality measurements to predict a throughput distribution. The resulting estimate of the obtainable throughput can then be used in adjustable bitrate applications like video streaming to improve their dependability. Mobile networks also task TCP congestion control algorithms with a new challenge: Usually, senders use TCP congestion control to avoid causing congestion in the network by sending too many packets and so that the network bandwidth is divided fairly. This can be a challenging task since it is not known how many senders are in the network, and the network load can change at any time. In mobile vehicular networks, TCP congestion control is confronted with the additional problem of a constantly changing capacity: As users change their location, the quality of the channel also changes, and the capacity of the channel can experience drastic reductions even when the difference of location is very small. Additionally, in our measurements, we have observed that packet losses only rarely occur (and instead, packets are delayed and retransmitted), meaning that loss-based algorithms like Reno or CUBIC can be at a significant disadvantage. In this thesis, we compare several popular congestion control algorithms in both stationary and mobile scenarios. We find that many loss-based algorithms tend to cause bufferbloat and thus overly increase delays. At the same time, many delay-based algorithms tend to underestimate the network capacity and thus achieve data rates that are too low. The algorithm that performed the best in our measurements was TCP BBR, as it was able to utilize the full capacity of the channel without causing bufferbloat and also react to changes in capacity by adjusting its window. However, since TCP BBR can be unfair towards other algorithms in wired networks, its use could be problematic. Finally, we also propose how our model for data rate prediction can be used to improve the dependability of mobile video streaming. For this, we develop an algorithm for adaptive bitrate streaming that provides a guarantee that the video freeze probability does not exceed a certain pre-selected upper threshold. For the algorithm to work, it needs to know the distribution of obtainable throughput. We use a simulation to verify the function of this algorithm using a distribution obtained through the previously proposed data rate prediction algorithm. In our simulation, the algorithm limited the video freeze probability as intended. However, it did so at the cost of frequent switches of video bitrate, which can diminish the quality of user experience. In future work, we want to explore the possibility of different algorithms that offer a trade-off between the video freeze probability and the frequency of bitrate switches.Die Verbesserung der Zuverlässigkeit von mobilen Netzwerk-basierten Anwendungen ist aus vielen Gründen eine komplizierte Aufgabe: Vor allem in Deutschland war die Entwicklung der Mobilfunkinfrastruktur nicht immer schnell genug, um mit der wachsenden Nachfrage Schritt zu halten. Es gibt immer noch viele Funklöchern, die für Kommunikationsausfälle verantwortlich sind. Aber auch an Orten, an denen Infrastruktur ausreichend vorhanden ist, stellt die Mobilität der Nutzer eine große Herausforderung für die Zuverlässigkeit der Anwendungen dar: Wenn sich der Nutzer bewegt, kann sich die Kapazität des Kanals stark verändern. Dies kann dazu führen, dass Anwendungen wie Videostreaming mit einstellbarer Bitrate die in der Vergangenheit erreichten Downloadraten nicht zur Vorhersage der zukünftigen Leistung nutzen können, da diese nur alte Informationen über die Datenraten an einem anderen Standort enthalten. In dieser Arbeit untersuchen wir die Nutzung von 4G LTE für zuverlässige Kommunikation in mobilen Fahrzeugszenarien. Zu diesem Zweck untersuchen wir zunächst die Leistung von LTE, insbesondere in mobilen Umgebungen, und wie sie sich im Laufe der Zeit entwickelt hat. Wir vergleichen Messungen, die in einem zeitlichen Abstand von mehreren Jahren durchgeführt wurden, und untersuchen Leistungsunterschiede in städtischen und ländlichen Gebieten. Wir stellen fest, dass die kontinuierliche Weiterentwicklung des 4G-Standards zwar theoretisch eine bessere Leistung ermöglicht hat, dass sich dies aber aufgrund des langsamen Ausbaus der Infrastruktur, insbesondere entlang von Autobahnen, nicht immer in der Praxis bemerkbar gemacht hat. Wir untersuchen auch die Möglichkeit der Leistungsvorhersage in LTE-Netzen, ohne aktive Messungen durchführen zu müssen. Zu diesem Zweck untersuchen wir die Beziehung zwischen der gemessenen Signalqualität und den erreichbaren Datenraten und Latenzzeiten. Wir stellen fest, dass es zwar eine starke Korrelation zwischen einigen der Signalqualitätsindikatoren und den erreichbaren Datenraten gibt, die Beziehung zwischen ihnen aber stochastisch ist, d. h. eine höhere Signalqualität macht eine bessere Leistung zwar wahrscheinlicher, garantiert sie aber nicht. Wir verwenden dann unsere empirischen Messergebnisse als Grundlage für ein Modell, das die Signalqualitätsmessungen zur Vorhersage einer Durchsatzverteilung nutzt. Die sich daraus ergebende Schätzung des erzielbaren Durchsatzes kann dann in Anwendungen mit einstellbarer Bitrate wie Videostreaming verwendet werden, um deren Zuverlässigkeit zu verbessern. Mobile Netze stellen auch TCP Congestion Control Algorithmen vor eine neue Herausforderung: Normalerweise verwenden Sender TCP Congestion Control, um eine Überlastung des Netzes durch das Senden von zu vielen Paketen zu vermeiden, und um die Bandbreite des Netzes gerecht aufzuteilen. Dies kann eine schwierige Aufgabe sein, da es nicht bekannt ist, wie viele Sender sich im Netz befinden, und sich die Netzlast jederzeit ändern kann. In mobilen Fahrzeugnetzen ist TCP Congestion Control mit dem zusätzlichen Problem einer sich ständig ändernden Kapazität konfrontiert: Wenn die Benutzer ihren Standort wechseln, ändert sich auch die Qualität des Kanals, und die Kanalkapazität des Kanals kann drastisch sinken, selbst wenn der Unterschied zwischen den Standorten sehr gering ist. Darüber hinaus haben wir bei unseren Messungen festgestellt, dass Paketverluste nur selten auftreten (stattdessen werden Pakete verzögert und erneut übertragen), was bedeutet, dass verlustbasierte Algorithmen wie Reno oder CUBIC einen großen Nachteil haben können. In dieser Arbeit vergleichen wir mehrere gängige Congestion Control Algorithmen sowohl in stationären als auch in mobilen Szenarien. Wir stellen fest, dass viele verlustbasierte Algorithmen dazu neigen, einen Pufferüberlauf zu verursachen und somit die Latenzen übermäßig erhöhen, während viele latenzbasierte Algorithmen dazu neigen, die Kanalkapazität zu unterschätzen und somit zu niedrige Datenraten erzielen. Der Algorithmus, der bei unseren Messungen am besten abgeschnitten hat, war TCP BBR, da er in der Lage war, die volle Kapazität des Kanals auszunutzen, ohne den Pufferfüllstand übermäßig zu erhöhen. Ebenso hat TCP BBR schnell auf Kapazitätsänderungen reagiert, indem er seine Fenstergröße angepasst hat. Da TCP BBR jedoch in kabelgebundenen Netzen gegenüber anderen Algorithmen unfair sein kann, könnte seine Verwendung problematisch sein. Schließlich schlagen wir auch vor, wie unser Modell zur Vorhersage von Datenraten verwendet werden kann, um die Zuverlässigkeit des mobilen Videostreaming zu verbessern. Dazu entwickeln wir einen Algorithmus für Streaming mit adaptiver Bitrate, der garantiert, dass die Wahrscheinlichkeit des Anhaltens eines Videos eine bestimmte, vorher festgelegte Obergrenze nicht überschreitet. Damit der Algorithmus funktionieren kann, muss er die Verteilung des erreichbaren Durchsatzes kennen. Wir verwenden eine Simulation, um die Funktion dieses Algorithmus zu überprüfen. Hierzu verwenden wir eine Verteilung, die wir durch den zuvor vorgeschlagenen Algorithmus zur Vorhersage von Datenraten erhalten haben. In unserer Simulation begrenzte der Algorithmus die Wahrscheinlichkeit des Anhaltens von Videos wie beabsichtigt, allerdings um den Preis eines häufigen Wechsels der Videobitrate, was die Qualität der Benutzererfahrung beeinträchtigen kann. In zukünftigen Arbeiten wollen wir die Möglichkeit verschiedener Algorithmen untersuchen, die einen Kompromiss zwischen der Wahrscheinlichkeit des Anhaltens des Videos und der Häufigkeit der Bitratenwechsel bieten

    Sesión 495, Ordinaria Modalidad Virtual. Vigésimo Cuarto Consejo Académico, 12 de julio de 2022

    Get PDF
    1 archivo PDF (305 páginas) + 1 archivo zipLista de asistencia. -- Orden del Día. -- Acta de la Sesión 495 Ordinaria. -- Acuerdos de la Sesión 495 Ordinaria. -- Acta de la Sesión 494. -- Jurados Diploma a la Investigación 2021. -- Renuncias Comisiones Dictaminadoras Divisionales (Dr. Ernesto Rodrigo Vázquez Cerón, Mtro. Fabricio Vanden Broeck y Dr. León Tomás Ejea Mendoza). -- Dictamen creación de Área Mecánica -- Dictamen Premio a las Áreas 2022 -- Escrito DCSH.AZC.336.22 Dictamen C. Docencia Consejo Divisional CSH. -- Escrito suscrito por el Dr. Saúl Jerónimo Romero con fecha 11 de julio de 2022 y anexos. -- Escrito con fecha 12 de julio de 2022 firmado por un grupo del sector estudiantil relativo a la petición de un diálogo público con el Rector de la Unidad Azcapotzalco

    Reliable Data Transmission in Challenging Vehicular Network using Delay Tolerant Network

    Get PDF
    In the 21st century, there has been an increasing tendency toward the wide adoption of wireless networks and technologies due to their significant advantages such as flexibility, mobility, accessibility, and low cost. Wireless technologies have therefore become essential factors in the improvement of intra-vehicle road safety in Vehicular Ad-hoc Network (VANET), which potentially reduce road traffic accidents by enabling efficient exchange of information between vehicles in the early stages. However, due to the inherent high mobility and rapid change of topology, there are numerous challenges in VANET. Hence, different software packages have been combined in this project to create the VANET environment, whereby the Objective Modular Network Testbed (OMNeT++) and the Simulation of Urban Mobility (SUMO), along with Vehicles in Network Simulation (VEINS) are integrated to model the VANET environment. Also, Delay Tolerant Network (DTN) are implemented in the Opportunistic Network Environment (ONE) simulator, where the Store-Carry-Forward technique is used to route traffic. When network resources are not limited, a high delivery ratio is possible. However, when network resources are scarce, these protocols will have a low delivery ratio and high overhead. Due to these limitations, in this research, an extensive performance evaluation of various routing protocols for DTN with different buffer management policies, giving insight into the impact of these policies on DTN routing protocol performance has been conducted. The empirical study gave insight into the strengths and limitations of the existing protocols thus enabling the selection of the benchmark protocols utilized in evaluating a new Enhanced Message Replication Technique (EMRT) proposed in this thesis. The main contribution of this thesis is the design, implementation, and evaluation of a novel EMRT that dynamically adjusts the number of message replicas based on a node's ability to quickly disseminate the message and maximize the delivery ratio. EMRT is evaluated using three different quota protocols: Spray&Wait, Encounter Based Routing (EBR), and Destination Based Routing Protocol (DBRP). Simulation results show that applying EMRT to these protocols improves the delivery ratio while reducing overhead ratio and latency average. For example, when combined with Spray&Wait, EBR, and DBRP, the delivery probability is improved by 13%, 8%, and 10%, respectively, while the latency average is reduced by 51%, 14%, and 13%, respectively

    Collected Papers (on Neutrosophics, Plithogenics, Hypersoft Set, Hypergraphs, and other topics), Volume X

    Get PDF
    This tenth volume of Collected Papers includes 86 papers in English and Spanish languages comprising 972 pages, written between 2014-2022 by the author alone or in collaboration with the following 105 co-authors (alphabetically ordered) from 26 countries: Abu Sufian, Ali Hassan, Ali Safaa Sadiq, Anirudha Ghosh, Assia Bakali, Atiqe Ur Rahman, Laura Bogdan, Willem K.M. Brauers, Erick González Caballero, Fausto Cavallaro, Gavrilă Calefariu, T. Chalapathi, Victor Christianto, Mihaela Colhon, Sergiu Boris Cononovici, Mamoni Dhar, Irfan Deli, Rebeca Escobar-Jara, Alexandru Gal, N. Gandotra, Sudipta Gayen, Vassilis C. Gerogiannis, Noel Batista Hernández, Hongnian Yu, Hongbo Wang, Mihaiela Iliescu, F. Nirmala Irudayam, Sripati Jha, Darjan Karabašević, T. Katican, Bakhtawar Ali Khan, Hina Khan, Volodymyr Krasnoholovets, R. Kiran Kumar, Manoranjan Kumar Singh, Ranjan Kumar, M. Lathamaheswari, Yasar Mahmood, Nivetha Martin, Adrian Mărgean, Octavian Melinte, Mingcong Deng, Marcel Migdalovici, Monika Moga, Sana Moin, Mohamed Abdel-Basset, Mohamed Elhoseny, Rehab Mohamed, Mohamed Talea, Kalyan Mondal, Muhammad Aslam, Muhammad Aslam Malik, Muhammad Ihsan, Muhammad Naveed Jafar, Muhammad Rayees Ahmad, Muhammad Saeed, Muhammad Saqlain, Muhammad Shabir, Mujahid Abbas, Mumtaz Ali, Radu I. Munteanu, Ghulam Murtaza, Munazza Naz, Tahsin Oner, ‪Gabrijela Popović‬‬‬‬‬, Surapati Pramanik, R. Priya, S.P. Priyadharshini, Midha Qayyum, Quang-Thinh Bui, Shazia Rana, Akbara Rezaei, Jesús Estupiñán Ricardo, Rıdvan Sahin, Saeeda Mirvakili, Said Broumi, A. A. Salama, Flavius Aurelian Sârbu, Ganeshsree Selvachandran, Javid Shabbir, Shio Gai Quek, Son Hoang Le, Florentin Smarandache, Dragiša Stanujkić, S. Sudha, Taha Yasin Ozturk, Zaigham Tahir, The Houw Iong, Ayse Topal, Alptekin Ulutaș, Maikel Yelandi Leyva Vázquez, Rizha Vitania, Luige Vlădăreanu, Victor Vlădăreanu, Ștefan Vlăduțescu, J. Vimala, Dan Valeriu Voinea, Adem Yolcu, Yongfei Feng, Abd El-Nasser H. Zaied, Edmundas Kazimieras Zavadskas.‬

    Enhancing User Experience by Extracting Application Intelligence from Network Traffic

    Full text link
    Internet Service Providers (ISPs) continue to get complaints from users on poor experience for diverse Internet applications ranging from video streaming and gaming to social media and teleconferencing. Identifying and rectifying the root cause of these experience events requires the ISP to know more than just coarse-grained measures like link utilizations and packet losses. Application classification and experience measurement using traditional deep packet inspection (DPI) techniques is starting to fail with the increasing adoption of traffic encryption and is not cost-effective with the explosive growth in traffic rates. This thesis leverages the emerging paradigms of machine learning and programmable networks to design and develop systems that can deliver application-level intelligence to ISPs at scale, cost, and accuracy that has hitherto not been achieved before. This thesis makes four new contributions. Our first contribution develops a novel transformer-based neural network model that classifies applications based on their traffic shape, agnostic to encryption. We show that this approach has over 97% f1-score for diverse application classes such as video streaming and gaming. Our second contribution builds and validates algorithmic and machine learning models to estimate user experience metrics for on-demand and live video streaming applications such as bitrate, resolution, buffer states, and stalls. For our third contribution, we analyse ten popular latency-sensitive online multiplayer games and develop data structures and algorithms to rapidly and accurately detect each game using automatically generated signatures. By combining this with active latency measurement and geolocation analysis of the game servers, we help ISPs determine better routing paths to reduce game latency. Our fourth and final contribution develops a prototype of a self-driving network that autonomously intervenes just-in-time to alleviate the suffering of applications that are being impacted by transient congestion. We design and build a complete system that extracts application-aware network telemetry from programmable switches and dynamically adapts the QoS policies to manage the bottleneck resources in an application-fair manner. We show that it outperforms known queue management techniques in various traffic scenarios. Taken together, our contributions allow ISPs to measure and tune their networks in an application-aware manner to offer their users the best possible experience

    Physics of Ionic Conduction in Narrow Biological and Artificial Channels

    Get PDF
    The book reprints a set of important scientific papers applying physics and mathematics to address the problem of selective ionic conduction in narrow water-filled channels and pores. It is a long-standing problem, and an extremely important one. Life in all its forms depends on ion channels and, furthermore, the technological applications of artificial ion channels are already widespread and growing rapidly. They include desalination, DNA sequencing, energy harvesting, molecular sensors, fuel cells, batteries, personalised medicine, and drug design. Further applications are to be anticipated.The book will be helpful to researchers and technologists already working in the area, or planning to enter it. It gives detailed descriptions of a diversity of modern approaches, and shows how they can be particularly effective and mutually reinforcing when used together. It not only provides a snapshot of current cutting-edge scientific activity in the area, but also offers indications of how the subject is likely to evolve in the future
    corecore