Improving end-to-end Quality of Service (QoS) in existing network systems is a fundamental problem, as it can be affected by many factors, including congestion, packet scheduling, attacks, and air-time allocation. This dissertation addresses QoS in two critical environments: home WiFi and cloud networks. In home networks, we focus on improving QoS over WiFi networks, the dominant means for home Internet access. Three major reasons for end-to-end QoS efforts fail in WiFi networks are its: 1) inherent wireless channel characteristics, 2) approach to access control of the shared broadcast channel, and 3) impact on transport layer protocols, such as TCP, that operate end-to-end, and over-react to the loss or delay caused by the single WiFi link. We present our cross-layer design, Virtual Wire, leveraging the philosophy of centralization in modern networking to address the problem at the point of entry/egress into the WiFi network. Based on network conditions measured from buffer sizes, airtime, and throughput, flows are scheduled to the optimal utility. Unlike most existing WiFi QoS approaches, our design only relies on transparent modifications, requiring no changes to the network (including link layer) protocols, applications, or user intervention. Through extensive experimental investigation, we show that our design significantly enhances the reliability and predictability of WiFi performance, providing a ``virtual wire''-like link to the targeted application. In cloud networks, we explore mechanisms to improve availability during DDoS attacks. The availability of cloud servers is impacted when excessive loads induced by DDoS attacks cause the servers to crash or respond too slowly to legitimate session requests. We model and analyze the effectiveness of a shuffling mechanism: the periodic, randomized re-assignment of users to servers. This shuffling mechanism not only complicates malicious users’ abilities to target specific servers but also, over time, allows a system to identify who the malicious users are. We design and evaluate improved classifiers which can, with statistical accuracy and well-defined levels of confidence, identify malicious users. We also propose and explore the effectiveness of a two-tiered system in which servers are partitioned in two, where one partition serves only ”filtered” users who have demonstrated non-malicious behavior. Our results show how shuffling with these novel classifiers can improve the QoS of the system, which is evaluated by the survival probability, the probability of a legitimate session not being affected by attacks.

D. Tech. Electrical Engineering. / Discusses the protocol design in VANETs is very challenging due to their low latency and high data rate requirements in a high mobility environment. Hence, the central metrics of QoS such as throughput, reliability and delays are critical to the design of protocol in VANETs. Therefore, this project focuses on the scheduling and QoS enhancement algorithms. The QoS analytical model and multi-channel MAC protocol were completed; this was significant for the development of the VANETs.The anticipated benefits of this study may be described as: 1. The duty cycle adaptive MAC protocol could improve the QoS of VANET in the situation where the OBU is equipped with only one transceiver. 2. The results obtained from this model is significant for the designing and evaluation of the vehicular network. 3. Due to the characteristics of VANETs, the requirements of high throughput and low latency are critical in VANETs. An efficient multi-channel MAC protocol is a vital requirement in order to offer efficient, fair and stable channel access using the limited channel resources.

The IEEE 802.16 standard popularly known as WiMAX is at the forefront of the technological drive. Achieving high system throughput in these networks is challenging due to interference which limits concurrent transmissions. In this thesis, we study routing and link scheduling inWiMAX mesh networks. We present simple joint routing and link scheduling algorithms that have outperformed most of the existing proposals in our experiments. Our session based routing and links scheduling produced results approximately 90% of a trivial lower bound. We also study the problem of quality of service (QoS) provisioning in WiMAX mesh networks. QoS has become an attractive area of study driven by the increasing demand for multimedia content delivered wirelessly. To accommodate the different applications, the IEEE 802.16 standard defines four classes of service. In this dissertation, we propose a comprehensive scheme consisting of routing, link scheduling, call admission control (CAC) and channel assignment that considers all classes of service. Much of the work in the literature considers each of these problems in isolation. Our routing schemes use a metric that combines interference and traffic load to compute routes for requests while our link scheduling ensures that the QoS requirements of admitted requests are strictly met. Results from our simulation indicate that our routing and link scheduling schemes significantly improve network performance when the network is congested. / ix, 77 leaves : ill. ; 29 cm

The promise of service oriented computing, and the availability of Web services promote the delivery and creation of new services based on existing services, in order to meet new demands and new markets. As Web and internet based services move into Clouds, inter-dependency of services and their complexity will increase substantially. There are standards and frameworks for specifying and composing Web Services based on functional properties. However, mechanisms to individually address non-functional properties of services and their compositions have not been well established. Furthermore, the Cloud ontology depicts service layers from a high-level, such as Application and Software, to a low-level, such as Infrastructure and Platform. Each component that resides in one layer can be useful to another layer as a service. It hints at the amount of complexity resulting from not only horizontal but also vertical integrations in building and deploying a composite service. To meet the requirements and facilitate using Web services, we first propose a WSDL extension to permit specification of non-functional or Quality of Service (QoS) properties. On top of the foundation, the QoS-aware framework is established to adapt publicly available tools for Web services, augmented by ontology management tools, along with tools for performance modeling to exemplify how the non-functional properties such as response time, throughput, or utilization of services can be addressed in the service acquisition and composition process. To facilitate Web service composition standards, in this work we extended the framework with additional qualitative information to the service descriptions using Business Process Execution Language (BPEL). Engineers can use BPEL to explore design options, and have the QoS properties analyzed for the composite service. The main issue in our research is performance evaluation in software system and engineering. We researched the Web service computation as the first half of this dissertation, and performance antipattern detection and elimination in the second part. Performance analysis of software system is complex due to large number of components and the interactions among them. Without the knowledge of experienced experts, it is difficult to diagnose performance anomalies and attempt to pinpoint the root causes of the problems. Software performance antipatterns are similar to design patterns in that they provide what to avoid and how to fix performance problems when they appear. Although the idea of applying antipatterns is promising, there are gaps in matching the symptoms and generating feedback solution for redesign. In this work, we analyze performance antipatterns to extract detectable features, influential factors, and resource involvements so that we can lay the foundation to detect their presence. We propose system abstract layering model and suggestive profiling methods for performance antipattern detection and elimination. Solutions proposed can be used during the refactoring phase, and can be included in the software development life cycle. Proposed tools and utilities are implemented and their use is demonstrated with RUBiS benchmark.

Orientadores: Nelson Luis Saldanha da Fonseca, Juliana Freitag Borin / Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Computação / Made available in DSpace on 2018-08-27T14:47:59Z (GMT). No. of bitstreams: 1 AstudilloTrujillo_CarlosAlberto_M.pdf: 2488879 bytes, checksum: 5004b31fcfcabbbf8528fe73f484da7d (MD5) Previous issue date: 2015 / Resumo: A crescente demanda por acesso banda larga móvel tem motivado a implantação da tecnologia long term evolution (LTE) pelas operadoras de redes móveis (MNOs), permitindo o fornecimento de novos serviços que demandam alta largura de banda e requisitos rigorosos de qualidade de serviço (QoS). Este aumento da demanda de banda passante leva à necessidade de um grande número de estações rádio base e ao aumento na quantidade de tráfego injetado no backhaul da rede móvel (MBH). Para lidar com os elevados custos do MBH, redes ópticas passivas (PONs) podem ser usadas para reduzir os custos do MBH usando sistemas fiber to anywhere (FTTx) já implantado bem como lidar com a crescente demanda por acesso banda larga à Internet por usuários móveis. Além disso, a estação rádio base LTE, também conhecida como evolved NodeB (eNB), pode ser integrada à unidade de rede óptica (ONU) das PONs em um dispositivo único, chamado ONU-eNB. A ONU-eNB compete pela largura de banda com outras ONUs em PONs, o que pode potencialmente prejudicar o fornecimento de QoS aos usuários móveis. Esta dissertação propõe um framework para provisão de QoS em redes móveis LTE que empregam backhaul baseado em redes ópticas passivas Ethernet (EPONs). Propõe também, um escalonador para redes LTE com o objetivo de prover requisitos de QoS e melhorar a vazão total. O framework introduz uma arquitetura funcional para o dispositivo integrado ONU-eNB, um esquema de mapeamento de QoS e um método para permitir que os escalonadores LTE atuais possam levar em conta informações disponíveis da rede de acesso móvel (LTE) bem com informações sobre a rede de backhaul (EPON), a fim de melhorar o desempenho total da rede, especialmente quando o enlace de backhaul está congestionado. Mostra-se como a implementação do framework proposto melhora a utilização da rede e a provisão de QoS em uma rede integrada, mesmo sob variação da carga de tráfego no backhaul e na rede LTE / Abstract: The increasing demand for mobile broadband access has motivated mobile network operators (MNOs) to deploy the long term evolution (LTE) technology, which allows the support of new services demanding large amount of bandwidth and strict QoS requirements. The growth of bandwidth demands will increase dramatically the number of base stations and the amount of traffic injected into the mobile backhaul (MBH) network. To cope with the high cost of MBH networks, passive optical networks (PONs) can be employed by using the already deployed fiber to anywhere (FTTx) systems. Moreover, the LTE base station, also known as evolved NodeB (eNB), can be integrated with the optical network unit (ONU) in a single device, called ONU-eNB, which competes for bandwidth with other ONUs in PONs. Such competition for bandwidth can jeopardize the support of mobile users¿ quality of service (QoS) requirements. This dissertation proposes a QoS provisioning framework for LTE mobile networks employing Ethernet PON (EPON)-based backhaul. It also introduces a novel LTE scheduler with the aim of supporting QoS requirements as well as high throughput. The framework includes a functional architecture for the ONU-eNB, a QoS mapping scheme and a method to allow current LTE schedulers to take into account both information available from the mobile access network (LTE) and information from the backhaul network (EPON) in order to improve the overall network performance, specially when the backhaul link is congested. It is shown how the proposed framework can improve network utilization and quality of service provisioning in an integrated network even under variations of traffic load in the backhaul and in the LTE networks / Mestrado / Ciência da Computação / Mestre em Ciência da Computação

This research study focuses on the application models of Data Mining and Machine Learning covering cellular network traffic, in the objective to arm Mobile Network Operators with full view of performance branches (Services, Device, Subscribers). The purpose is to optimize and minimize the time to detect service and subscriber patterns behaviour. Different data mining techniques and predictive algorithms have been applied on real cellular network datasets to uncover different data usage patterns using specific Key Performance Indicators (KPIs) and Key Quality Indicators (KQI). The following tools will be used to develop the concept: RStudio for Machine Learning and process visualization, Apache Spark, SparkSQL for data and big data processing and clicData for service Visualization. Two use cases have been studied during this research. In the first study, the process of Data and predictive Analytics are fully applied in the field of Telecommunications to efficiently address users’ experience, in the goal of increasing customer loyalty and decreasing churn or customer attrition. Using real cellular network transactions, prediction analytics are used to predict customers who are likely to churn, which can result in revenue loss. Prediction algorithms and models including Classification Tree, Random Forest, Neural Networks and Gradient boosting have been used with an exploratory Data Analysis, determining relationship between predicting variables. The data is segmented in to two, a training set to train the model and a testing set to test the model. The evaluation of the best performing model is based on the prediction accuracy, sensitivity, specificity and the Confusion Matrix on the test set. The second use case analyses Service Quality Management using modern data mining techniques and the advantages of in-memory big data processing with Apache Spark and SparkSQL to save cost on tool investment; thus, a low-cost Service Quality Management model is proposed and analyzed. With increase in Smart phone adoption, access to mobile internet services, applications such as streaming, interactive chats require a certain service level to ensure customer satisfaction. As a result, an SQM framework is developed with Service Quality Index (SQI) and Key Performance Index (KPI). The research concludes with recommendations and future studies around modern technology applications in Telecommunications including Internet of Things (IoT), Cloud and recommender systems. / Cellular networks have evolved and are still evolving, from traditional GSM (Global System for Mobile Communication) Circuit switched which only supported voice services and extremely low data rate, to LTE all Packet networks accommodating high speed data used for various service applications such as video streaming, video conferencing, heavy torrent download; and for say in a near future the roll-out of the Fifth generation (5G) cellular networks, intended to support complex technologies such as IoT (Internet of Things), High Definition video streaming and projected to cater massive amount of data. With high demand on network services and easy access to mobile phones, billions of transactions are performed by subscribers. The transactions appear in the form of SMSs, Handovers, voice calls, web browsing activities, video and audio streaming, heavy downloads and uploads. Nevertheless, the stormy growth in data traffic and the high requirements of new services introduce bigger challenges to Mobile Network Operators (NMOs) in analysing the big data traffic flowing in the network. Therefore, Quality of Service (QoS) and Quality of Experience (QoE) turn in to a challenge. Inefficiency in mining, analysing data and applying predictive intelligence on network traffic can produce high rate of unhappy customers or subscribers, loss on revenue and negative services’ perspective. Researchers and Service Providers are investing in Data mining, Machine Learning and AI (Artificial Intelligence) methods to manage services and experience. This research study focuses on the application models of Data Mining and Machine Learning covering network traffic, in the objective to arm Mobile Network Operators with full view of performance branches (Services, Device, Subscribers). The purpose is to optimize and minimize the time to detect service and subscriber patterns behaviour. Different data mining techniques and predictive algorithms will be applied on cellular network datasets to uncover different data usage patterns using specific Key Performance Indicators (KPIs) and Key Quality Indicators (KQI). The following tools will be used to develop the concept: R-Studio for Machine Learning, Apache Spark, SparkSQL for data processing and clicData for Visualization. / Electrical and Mining Engineering / M. Tech (Electrical Engineering)