Network resources (such as bandwidth on a link) are not unlimited, and must be shared by all networked applications in some manner of fairness. This calls for the development and implementation of effective strategies that enable optimal utilization of these scarce network resources among the various applications that share the network. Although several rate controllers have been proposed in the literature to address the issue of optimal rate allocation, they do not appear to capture other factors that are of critical concern. For example, consider a battlefield data fusion application where a fusion center desires to allocate more bandwidth to incoming flows that are perceived to be more accurate and important. For these applications, network users should consider transmission rates of other users in the process of rate allocation. Hence, a rate controller should consider application specific rate coordination directives given by the underlying application. The work reported herein addresses this issue of how a rate controller may establish and maintain the desired application specific rate coordination directives. We identify three major challenges in meeting this objective. First, the application specific performance measures must be formulated as rate coordination directives. Second, it is necessary to incorporate these rate coordination directives into a rate controller. Of course, the resulting rate controller must co-exist with ordinary rate controllers, such as TCP Reno, in a shared network. Finally, a mechanism for identifying those flows that require the rate allocation directives must be put in place. The first challenge is addressed by means of a utility function which allows the performance of the underlying application to be maximized. The second challenge is addressed by utilizing the Network Utility Maximization (NUM) framework. The standard utility function (i.e. utility function of the standard rate controller) is augmented by inserting the application specific utility function as an additive term. Then the rate allocation problem is formulated as a constrained optimization problem, where the objective is to maximize the aggregate utility of the network. The gradient projection algorithm is used to solve the optimization problem. The resulting solution is formulated and implemented as a window update function. To address the final challenge we resort to a machine learning algorithm. We demonstrate how data features estimated utilizing only a fraction of the flow can be used as evidential input to a series of Bayesian Networks (BNs). We account for the uncertainty introduced by partial flow data through the Dempster-Shafer (DS) evidential reasoning framework.

" IEEE 802.11 wireless access points (APs) act as the central communication hub inside homes, connecting all networked devices to the Internet. Home users run a variety of network applications with diverse Quality-of-Service requirements (QoS) through their APs. However, wireless APs are often the bottleneck in residential networks as broadband connection speeds keep increasing. Because of the lack of QoS support and complicated configuration procedures in most off-the-shelf APs, users can experience QoS degradation with their wireless networks, especially when multiple applications are running concurrently. This dissertation presents CATNAP, Classification And Treatment iN an AP , to provide better QoS support for various applications over residential wireless networks, especially timely delivery for real-time applications and high throughput for download-based applications. CATNAP consists of three major components: supporting functions, classifiers, and treatment modules. The supporting functions collect necessary flow level statistics and feed it into the CATNAP classifiers. Then, the CATNAP classifiers categorize flows along three-dimensions: response-based/non-response-based, interactive/non-interactive, and greedy/non-greedy. Each CATNAP traffic category can be directly mapped to one of the following treatments: push/delay, limited advertised window size/drop, and reserve bandwidth. Based on the classification results, the CATNAP treatment module automatically applies the treatment policy to provide better QoS support. CATNAP is implemented with the NS network simulator, and evaluated against DropTail and Strict Priority Queue (SPQ) under various network and traffic conditions. In most simulation cases, CATNAP provides better QoS supports than DropTail: it lowers queuing delay for multimedia applications such as VoIP, games and video, fairly treats FTP flows with various round trip times, and is even functional when misbehaving UDP traffic is present. Unlike current QoS methods, CATNAP is a plug-and-play solution, automatically classifying and treating flows without any user configuration, or any modification to end hosts or applications. "

This dissertation describes a study which aims to classify video flows from Internet network traffic. In this study, classification is done based on the characteristics of the flow, which includes features such as payload sizes and inter-arrival time. The purpose of this is to give an alternative to classifying flows based on the contents of their payload packets. Because of an increase of encrypted flows within Internet network traffic, this is a necessity. Data with known class is fed to a machine learning classifier such that a model can be created. This model can then be used for classification of new unknown data. For this study, two different classifiers are used, namely decision trees and random forest. Several tests are completed to attain the best possible models. The results of this dissertation shows that classification based on characteristics is possible and the random forest classifier in particular achieves good accuracies. However, the accuracy of classification of encrypted flows was not able to be tested within this project. / HITS, 4707

Recently, machine learning has been considered an important tool for various networkingrelated use cases such as intrusion detection, flow classification, etc. Traditionally, machinelearning based classification algorithms run on dedicated machines that are outside of thefast path, e.g. on Deep Packet Inspection boxes, etc. This imposes additional latency inorder to detect threats or classify the flows.With the recent advance of programmable data planes, implementing advanced function-ality directly in the fast path is now a possibility. In this thesis, we propose to implementArtificial Neural Network inference together with flow metadata extraction directly in thedata plane of P4 programmable switches, routers, or Network Interface Cards (NICs).We design a P4 pipeline, optimize the memory and computational operations for our dataplane target, a programmable NIC with Micro-C external support. The results show thatneural networks of a reasonable size (i.e. 3 hidden layers with 30 neurons each) can pro-cess flows totaling over a million packets per second, while the packet latency impact fromextracting a total of 46 features is 1.85μs.

Data Centers (DCs) have become an intrinsic element of emerging technologies such as big data, artificial intelligence, cloud services; all of which entails interconnected and sophisticated computing and storage resources. Recent studies of conventional data center networks (DCNs) revealed two key challenges: a biased distribution of inter-rack traffic and unidentified flow classes: delay sensitive mice flows (MFs) and throughput-hungry elephant flows (EFs). Unfortunately, existing DCN topologies support only uniform distribution of capacities, provide limited bandwidth flexibilities and lacks of efficient flow classification mechanism. Fortunately, wireless DCs can leverage wireless communication emerging technologies, such as multi-terabit free-space optic (FSO), to provide flexible and reconfigurable DCN topologies. It is worth noting that indoor FSO links are less vulnerable to outdoor FSO channel impairments. Consequently, indoor FSO links are more robust and can offer high bandwidths with long stability, which can further be enhanced with wavelength division multiplexing (WDM) methods. In this thesis, we alleviate the bandwidth inefficiency by FSO links that have the desired agility by allocating the transmission powers to adapt link capacity for dynamically changing traffic conditions, and to reduce the maintenance costs and overhead. While routing the two classes along the same path causes unpleasant consequences, the DC researchers proposed traffic management solutions to treat them separately. However, the solutions either suffer from packet reordering and high queuing delay, or lack of accurate visibility and estimation on end-to-end path status. Alternatively, we leverage WDM to design elastic network topologies (i.e., part of the wavelengths are assigned to route MFs and the remaining for EFs). Since bandwidth demands can be lower than available capacity of WDM channels, we use traffic grooming to aggregate multiple flows into a larger flow and to enhance the link utilization. On the other hand, to reap the benefits of the proposed WDM isolated topology, an accurate and fast EF detection mechanism is necessary. Accordingly, we propose a scheme that uses TCP communication behavior and collect indicative packets for its flow classification algorithm, it demonstrates perfect flow classification accuracy, and is in order of magnitudes faster than existing solutions with low communication and computation overhead.

[pt] A motivação deste trabalho consiste no uso do four-roll mill para aumentar a separação de fases de emulsões água em óleo (A/O) presente no processamento primário da indústria de petróleo. A partir da conservação de massa e momento, a fase contínua foi modelada como escoamento incompressível, bi-dimensional e isotérmico. Simulações numéricas utilizando o método de elementos finitos foram implementadas para revelar a influência das diversas configurações de escoamento no comportamento mecânico do material. A partir dos resultados obtidos, a habitual forma de classificar o escoamento no four-roll mill de acordo com a literatura se demonstrou ineficiente. Este trabalho sugere classificações locais de escoamento a cada posição dependendo se a mesma está ocupada pela fase contínua ou dispersa da emulsão. O efeito da fase dispersa é descrito via pós-processamento. Microelementos no formato de vetores foram inseridos no domínio e investigou-se suas deformações e trajetórias. Consequentemente, analisou-se a deformação de gotas e a sua respectiva influência na instabilidade da emulsão. / [en] The motivation of this work consists in the use of four-roll mill in order to increase the phase separation of water-in-oil emulsions (W/O) present in the primary process of oil industry. With mass and momentum conservation, the continuous phase is modeled by an incompressible, bi-dimensional and isothermal flow. Numerical simulations employing the finite element method were implemented to reveal the influence of the several flow configurations in the material mechanical behavior. From the obtained results, the standard way of classifying the flow in the four-roll mill according to the literature was proved inefficient. This work suggests local flow classifications for each position depending if it is occupied by the continuous or dispersed phase. The effect of the dispersed phase was described by a post-processing scheme. Microelements in shape of vectors were inserted in the domain and their deformations and pathlines were investigated. Thus, the deformation of droplets and their respective influences in the emulsion instability were analyzed.

Intrusion detection systems (IDSs) play a crucial role in the identification and mitigation for attacks on host systems. Of these systems, vehicular ad hoc networks (VANETs) are difficult to protect due to the dynamic nature of their clients and their necessity for constant interaction with their respective cyber-physical systems. Currently, there is a need for a VANET-specific IDS that meets this criterion. To this end, a spline-based intrusion detection system has been pioneered as a solution. By combining clustering with spline-based general linear model classification, this knot flow classification method (KFC) allows for robust intrusion detection to occur. Due its design and the manner it is constructed, KFC holds great potential for implementation across a distributed system. The purpose of this thesis was to explain and extrapolate the afore mentioned IDS, highlight its effectiveness, and discuss the conceptual design of the distributed system for use in future research.