432 research outputs found
Performance Improvement of Multicommodity Flow of Tactile and Best Effort Packet in Internet Network
Development and implementation of the LBORU method for dynamic server load balancing in hybrid SDN networks
Softverski definisano umrežavanje (SDN) pruža mnoge prednosti, uključujući programiranje saobraćaja, agilnost, i automatizaciju rada mreže. Međutim, finansijska ograničenja ispoljena kroz tehničke (npr. skalabilnost, tolerancija grešaka, bezbednost) i ponekad, poslovne izazove (korisnikovo prihvatanje i poverenje mrežnih operatora) čine provajdere nesigurnim pri tranziciji na potpunu implementaciju SDN-a. Stoga, inkrementalna primena SDN funkcionalnosti kroz postavljanje ograničenog broja SDN uređaja među tradicionalne uređaje, predstavlja racionalno i efikasno okruženje koje klijentima može ponuditi moderne usluge, sa razmenom ogromne količine podataka. Međutim, iako hibridna SDN mreža pruža mnoge prednosti, takođe poseduje i specifične izazove. Ovo istraživanje daje odgovor na jedan od ovih izazova predstavljanjem istraživanja i razvoja nove šeme balansiranja opterećenja u hibridnom okruženju koje čini minimalan broj SDN uređaja (jedan kontroler i jedan svič). Izložena je nova šema balansiranja opterećenja koja nadzire trenutne indikatore opterećenja servera i primenjuje višeparametarsku metriku pri raspodeli veza kako bi se balansiralo opterećenje servera na što je moguće efikasniji način. Osnovu nove šeme balansiranja opterećenja čini kontinualno praćenje indikatora opterećenja servera i implementacija višeparametarske metrike (CPU load, I/O Read, I/O Write, Link Upload, Link Download) za raspoređivanje veza ka serverima. Testiranje obavljeno na serverima ima za cilj što efikasnije balansiranje opterećenja servera. Dobijeni rezultati pokazali su da se ovim mehanizmom postižu bolje performanse mreže nego kod postojećih šema balansiranja opterećenja u tradicionalnim i SDN mrežama. Štaviše, predložena šema za balansiranje može se koristiti pri realizaciji raznih usluga i primeniti u bilo kom klijent-server okruženju.Software-defined networking (SDN) provides many benefits, including traffic programmability, agility, and network automation. However, budget constraints burdened with technical (e.g., scalability, fault tolerance, security issues) and, sometimes, business challenges (user acceptance and confidence of network operators) make providers indecisive for full SDN deployment. Therefore, incremental deployment of SDN functionality through the placement of a limited set of SDN devices among traditional devices represents a rational and efficient environment that can offer customers modern and more data-intensive services. However, while hybrid SDN provides many benefits, it also has specific challenges addressed in the literature. This research answers one of these challenges by presenting the research and development of a new load balancing scheme in the hybrid SDN environment built with a minimal SDN device set (controller and one switch). This dissertation proposes a novel load balancing scheme to monitor current server load indicators and apply multi-parameter metrics for scheduling connections to balance the load on the servers as efficiently as possible. The base of the new load balancing scheme is continuous monitoring of server load indicators and implementations of multi-parameter metrics (CPU load, I/O Read, I/O Write, Link Upload, Link Download) for scheduling connections. The testing performed on servers aims to balance the server’s load as efficiently as possible. The obtained results have shown that this mechanism achieves better results than existing load balancing schemes in traditional and SDN networks. Moreover, a proposed load balancing scheme can be used with various services and applied in any client-server environment
Enhancing User Experience by Extracting Application Intelligence from Network Traffic
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
Dual Queue Coupled AQM: Deployable Very Low Queuing Delay for All
On the Internet, sub-millisecond queueing delay and capacity-seeking have
traditionally been considered mutually exclusive. We introduce a service that
offers both: Low Latency Low Loss Scalable throughput (L4S). When tested under
a wide range of conditions emulated on a testbed using real residential
broadband equipment, queue delay remained both low (median 100--300 s) and
consistent (99th percentile below 2 ms even under highly dynamic workloads),
without compromising other metrics (zero congestion loss and close to full
utilization). L4S exploits the properties of `Scalable' congestion controls
(e.g., DCTCP, TCP Prague). Flows using such congestion control are however very
aggressive, which causes a deployment challenge as L4S has to coexist with
so-called `Classic' flows (e.g., Reno, CUBIC). This paper introduces an
architectural solution: `Dual Queue Coupled Active Queue Management', which
enables balance between Scalable and Classic flows. It counterbalances the more
aggressive response of Scalable flows with more aggressive marking, without
having to inspect flow identifiers. The Dual Queue structure has been
implemented as a Linux queuing discipline. It acts like a semi-permeable
membrane, isolating the latency of Scalable and `Classic' traffic, but coupling
their capacity into a single bandwidth pool. This paper justifies the design
and implementation choices, and visualizes a representative selection of
hundreds of thousands of experiment runs to test our claims.Comment: Preprint. 17pp, 12 Figs, 60 refs. Submitted to IEEE/ACM Transactions
on Networkin
Air Traffic Management Abbreviation Compendium
As in all fields of work, an unmanageable number of abbreviations are used today in aviation for terms, definitions, commands, standards and technical descriptions. This applies in general to the areas of aeronautical communication, navigation and surveillance, cockpit and air traffic control working positions, passenger and cargo transport, and all other areas of flight planning, organization and guidance. In addition, many abbreviations are used more than once or have different meanings in different languages.
In order to obtain an overview of the most common abbreviations used in air traffic management, organizations like EUROCONTROL, FAA, DWD and DLR have published lists of abbreviations in the past, which have also been enclosed in this document. In addition, abbreviations from some larger international projects related to aviation have been included to provide users with a directory as complete as possible. This means that the second edition of the Air Traffic Management Abbreviation Compendium includes now around 16,500 abbreviations and acronyms from the field of aviation
Data Driven Network Design for Cloud Services Based on Historic Utilization
In recent years we have seen a shift from traditional networking in enterprises with Data Center centric architectures moving to cloud services. Companies are moving away from private networking technologies like MPLS as they migrate their application workloads to the cloud. With these migrations, network architects must struggle with how to design and build new network infrastructure to support the cloud for all their end users including office workers, remote workers, and home office workers. The main goal for network design is to maximize availability and performance and minimize cost. However, network architects and network engineers tend to over provision networks by sizing the bandwidth for worst case scenarios wasting millions of dollars per year. This thesis will analyze traditional network utilization data from twenty-five of the Fortune 500 companies in the United States and determine the most efficient bandwidth to support cloud services from providers like Amazon, Microsoft, Google, and others. The analysis of real-world data and the resulting proposed scaling factor is an original contribution from this study
SOFTWARE DEFINED CUSTOMIZATION OF NETWORK PROTOCOLS WITH LAYER 4.5
The rise of software defined networks, programmable data planes, and host level kernel programmability gives rise to highly specialized enterprise networks. One form of network specialization is protocol customization, which traditionally extends existing protocols with additional features, primarily for security and performance reasons. However, the current methodologies to deploy protocol customizations lack the agility to support rapidly changing customization needs. This dissertation designs and evaluates the first software-defined customization architecture capable of distributing and continuously managing protocol customizations within enterprise or datacenter networks. Our unifying architecture is capable of performing per-process customizations, embedding per-network security controls, and aiding the traversal of customized application flows through otherwise problematic middlebox devices. Through the design and evaluation of the customization architecture, we further our understanding of, and provide robust support for, application transparent protocol customizations. We conclude with the first ever demonstration of active application flow "hot-swapping" of protocol customizations, a capability not currently supported in operational networks.Office of Naval Research, Arlington, VA 22203Lieutenant Commander, United States NavyApproved for public release. Distribution is unlimited
Machine learning models for traffic classification in electromagnetic nano-networks
The number of nano-sensors connected to wireless electromagnetic nano-network generates different traffic volumes that have increased dramatically, enabling various applications of the Internet of nano-things. Nano-network traffic classification is more challenging nowadays to analyze different types of flows and study the overall performance of a nano-network that connects to the Internet through micro/nanogateways. There are traditional techniques to classify traffic, such as port-based technique and load-based technique, however the most promising technique used recently is machine learning. As machine learning models have a great impact on traffic classification and network performance evaluation in general, it is difficult to declare which is the best or the most suitable model to address the analysis of large volumes of traffic collected in operational nano-networks. In this paper, we study the classification problem of nano-network traffic captured by micro/nano-gateway, and then five supervised machine learning algorithms are used to analyze and classify the nano-network traffic from traditional traffic. Experimental analysis of the proposed models is evaluated and compared to show the most adequate classifier for nano-network traffic that gives very good accuracy and performance score to other classifiers.This work was supported in part by the ‘‘Agencia Estatal de Investigación’’ of ‘‘Ministerio de Ciencia e Innovación’’ of Spain under Project PID2019-108713RB-C51/MCIN/AEI/10.13039/501100011033, and in part by the ‘‘Agència de Gestió d’Ajuts Universitaris i de Recerca’’ (AGAUR) of the ‘‘Generalitat de Catalunya’’ under Grant 2021FI_B2 00091.Postprint (published version
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