Application-Aware Network Design Using Software Defined Networking for Application Performance Optimization for Big Data and Video Streaming

Title from PDF of title page viewed October 30, 2017Dissertation advisor: Deep MedhiVitaIncludes bibliographical references (pages 122-135)Thesis (Ph.D.)--School of Computing and Engineering. University of Missouri--Kansas City, 2017This dissertation investigates improvement in application performance. For applications, we consider two classes: Hadoop MapReduce and video streaming. The Hadoop MapReduce (M/R) framework has become the de facto standard for Big Data analytics. However, the lack of network-awareness of the default MapReduce resource manager in a traditional IP network can cause unbalanced job scheduling and network bottlenecks; such factors can eventually lead to an increase in the Hadoop MapReduce job completion time. Dynamic Video streaming over the HTTP (MPEG-DASH) is becoming the defacto dominating transport for today’s video applications. It has been implemented in today’s major media carriers such as Youtube and Netflix. It enables new video applications to fully utilize the existing physical IP network infrastructure. For new 3D immersive medias such as Virtual Reality and 360-degree videos are drawing great attentions from both consumers and researchers in recent years. One of the biggest challenges in streaming such 3D media is the high band width demands and video quality. A new Tile-based video is introduced in both video codec and streaming layer to reduce the transferred media size. In this dissertation, we propose a Software-Defined Network (SDN) approach in an Application-Aware Network (AAN) platform. We first present an architecture for our approach and then show how this architecture can be applied to two aforementioned application areas. Our approach provides both underlying network functions and application level forwarding logics for Hadoop MapReduce and video streaming. By incorporating a comprehensive view of the network, the SDN controller can optimize MapReduce work loads and DASH flows for videos by application-aware traffic reroute. We quantify the improvement for both Hadoop and MPEG-DASH in terms of job completion time and user’s quality of experience (QoE), respectively. Based on our experiments, we observed that our AAN platform for Hadoop MapReduce job optimization offer a significant improvement compared to a static, traditional IP network environment by reducing job run time by 16% to 300% for various MapReduce benchmark jobs. As for MPEG-DASH based video streaming, we can increase user perceived video bitrate by 100%.Introduction -- Research survey -- Proposed architecture -- AAN-SDN for Hadoop -- Study of User QoE Improvement for Dynamic Adaptive Streaming over HTTP (MPEG-DASH) -- AAN-SDN For MPEG-DASH -- Conclusion -- Appendix A. Mininet Topology Source Code For DASH Setup -- Appendix B. Hadoop Installation Source Code -- Appendix C. Openvswitch Installation Source Code -- Appendix D. HiBench Installation Guid

A Tutorial on Clique Problems in Communications and Signal Processing

Since its first use by Euler on the problem of the seven bridges of K\"onigsberg, graph theory has shown excellent abilities in solving and unveiling the properties of multiple discrete optimization problems. The study of the structure of some integer programs reveals equivalence with graph theory problems making a large body of the literature readily available for solving and characterizing the complexity of these problems. This tutorial presents a framework for utilizing a particular graph theory problem, known as the clique problem, for solving communications and signal processing problems. In particular, the paper aims to illustrate the structural properties of integer programs that can be formulated as clique problems through multiple examples in communications and signal processing. To that end, the first part of the tutorial provides various optimal and heuristic solutions for the maximum clique, maximum weight clique, and $k$-clique problems. The tutorial, further, illustrates the use of the clique formulation through numerous contemporary examples in communications and signal processing, mainly in maximum access for non-orthogonal multiple access networks, throughput maximization using index and instantly decodable network coding, collision-free radio frequency identification networks, and resource allocation in cloud-radio access networks. Finally, the tutorial sheds light on the recent advances of such applications, and provides technical insights on ways of dealing with mixed discrete-continuous optimization problems

Energy-aware service provisioning in P2P-assisted cloud ecosystems

Cotutela Universitat Politècnica de Catalunya i Instituto Tecnico de LisboaEnergy has been emerged as a first-class computing resource in modern systems. The trend has primarily led to the strong focus on reducing the energy consumption of data centers, coupled with the growing awareness of the adverse impact on the environment due to data centers. This has led to a strong focus on energy management for server class systems. In this work, we intend to address the energy-aware service provisioning in P2P-assisted cloud ecosystems, leveraging economics-inspired mechanisms. Toward this goal, we addressed a number of challenges. To frame an energy aware service provisioning mechanism in the P2P-assisted cloud, first, we need to compare the energy consumption of each individual service in P2P-cloud and data centers. However, in the procedure of decreasing the energy consumption of cloud services, we may be trapped with the performance violation. Therefore, we need to formulate a performance aware energy analysis metric, conceptualized across the service provisioning stack. We leverage this metric to derive energy analysis framework. Then, we sketch a framework to analyze the energy effectiveness in P2P-cloud and data center platforms to choose the right service platform, according to the performance and energy characteristics. This framework maps energy from the hardware oblivious, top level to the particular hardware setting in the bottom layer of the stack. Afterwards, we introduce an economics-inspired mechanism to increase the energy effectiveness in the P2P-assisted cloud platform as well as moving toward a greener ICT for ICT for a greener ecosystem.La energía se ha convertido en un recurso de computación de primera clase en los sistemas modernos. La tendencia ha dado lugar principalmente a un fuerte enfoque hacia la reducción del consumo de energía de los centros de datos, así como una creciente conciencia sobre los efectos ambientales negativos, producidos por los centros de datos. Esto ha llevado a un fuerte enfoque en la gestión de energía de los sistemas de tipo servidor. En este trabajo, se pretende hacer frente a la provisión de servicios de bajo consumo energético en los ecosistemas de la nube asistida por P2P, haciendo uso de mecanismos basados en economía. Con este objetivo, hemos abordado una serie de desafíos. Para instrumentar un mecanismo de servicio de aprovisionamiento de energía consciente en la nube asistida por P2P, en primer lugar, tenemos que comparar el consumo energético de cada servicio en la nube P2P y en los centros de datos. Sin embargo, en el procedimiento de disminuir el consumo de energía de los servicios en la nube, podemos quedar atrapados en el incumplimiento del rendimiento. Por lo tanto, tenemos que formular una métrica, sobre el rendimiento energético, a través de la pila de servicio de aprovisionamiento. Nos aprovechamos de esta métrica para derivar un marco de análisis de energía. Luego, se esboza un marco para analizar la eficacia energética en la nube asistida por P2P y en la plataforma de centros de datos para elegir la plataforma de servicios adecuada, de acuerdo con las características de rendimiento y energía. Este marco mapea la energía desde el alto nivel independiente del hardware a la configuración de hardware particular en la capa inferior de la pila. Posteriormente, se introduce un mecanismo basado en economía para aumentar la eficacia energética en la plataforma en la nube asistida por P2P, así como avanzar hacia unas TIC más verdes, para las TIC en un ecosistema más verde.Postprint (published version

Monte Carlo Method with Heuristic Adjustment for Irregularly Shaped Food Product Volume Measurement

Volume measurement plays an important role in the production and processing of food products. Various methods have been proposed to measure the volume of food products with irregular shapes based on 3D reconstruction. However, 3D reconstruction comes with a high-priced computational cost. Furthermore, some of the volume measurement methods based on 3D reconstruction have a low accuracy. Another method for measuring volume of objects uses Monte Carlo method. Monte Carlo method performs volume measurements using random points. Monte Carlo method only requires information regarding whether random points fall inside or outside an object and does not require a 3D reconstruction. This paper proposes volume measurement using a computer vision system for irregularly shaped food products without 3D reconstruction based on Monte Carlo method with heuristic adjustment. Five images of food product were captured using five cameras and processed to produce binary images. Monte Carlo integration with heuristic adjustment was performed to measure the volume based on the information extracted from binary images. The experimental results show that the proposed method provided high accuracy and precision compared to the water displacement method. In addition, the proposed method is more accurate and faster than the space carving method

Improving Academic Natural Language Processing Infrastructures Utilizing Cluster Computation

In light of widespread digitization endeavors and ever-growing textual data generation, developing efficient academic Natural Language Processing (NLP) infrastructures, which can deal with large amounts of data, is of particular importance. Novel computation technologies allow tools that support big data and heavy computation while performing timely and cost-effective data processing. This development has led researchers to demand that knowledge be extracted from ever-increasing textual data before it is outdated. Cluster computation is a modern technology for handling big data efficiently. It provides distribution of computing and data over a number of machines in a cluster, as well as efficient use of resources, which are key requirements to process big data in a timely manner. It also assures applications’ high availability and fault tolerance, which are fundamental concerns when dealing with vast amounts of data. In addition, it provides load balancing of data during the execution of tasks, which results in optimal use of resources and enhances efficiency. Data-oriented parallelization is an effective solution to enable the currently available academic NLP infrastructures to process big data. This approach offers a solution to parallelize the NLP tools which comprise identical non-complicated tasks without the expense of changing NLP algorithms. This thesis presents the adaption of cluster computation technology to academic NLP infrastructures to address the notable features that are essential to process vast quantities of text materials efficiently, in terms of both resources and time. Apache Spark on top of Apache Hadoop and its ecosystem have been utilized to develop a set of NLP tools that provide a distributed environment to execute the NLP tasks. Many experiments were conducted to assess the functionality of the designated strategy. This thesis shows that using cluster computation technology and data-oriented parallelization enables academic NLP infrastructures to execute large amounts of textual data in a timely manner while improving the performance of the NLP tools. Moreover, these experiments provide information that brings a more realistic and transparent estimation of workflows’ costs (required hardware resources) and execution time, along with the fastest, optimum, or feasible resource configuration for each individual workflow. This knowledge can be employed by users to trade-off between run-time, size of data, and hardware, and it enables them to design a strategy for data storage, duration of data retention, and delivery time. This has the potential to enhance researchers’ satisfaction when using academic NLP infrastructures. The thesis also shows that a cluster computation approach provides the capacity to adapt NLP services with JIT delivery systems. The proposed strategy assures the reliability and predictability of the services, which are the main characteristics of the services in JIT delivery systems. Defining the relevant parameters, recording the behavior of the services, and analyzing the generated data resulted in the provision of knowledge that can be utilized to create a service catalog—a fundamental requirement for the services in JIT delivery systems—for each service offered. This knowledge also helps to generate the performance profiles for each item mentioned in the service catalog and to update them continuously to cover new experiments and improve service quality

QoE on media deliveriy in 5G environments

231 p.5G expandirá las redes móviles con un mayor ancho de banda, menor latencia y la capacidad de proveer conectividad de forma masiva y sin fallos. Los usuarios de servicios multimedia esperan una experiencia de reproducción multimedia fluida que se adapte de forma dinámica a los intereses del usuario y a su contexto de movilidad. Sin embargo, la red, adoptando una posición neutral, no ayuda a fortalecer los parámetros que inciden en la calidad de experiencia. En consecuencia, las soluciones diseñadas para realizar un envío de tráfico multimedia de forma dinámica y eficiente cobran un especial interés. Para mejorar la calidad de la experiencia de servicios multimedia en entornos 5G la investigación llevada a cabo en esta tesis ha diseñado un sistema múltiple, basado en cuatro contribuciones.El primer mecanismo, SaW, crea una granja elástica de recursos de computación que ejecutan tareas de análisis multimedia. Los resultados confirman la competitividad de este enfoque respecto a granjas de servidores. El segundo mecanismo, LAMB-DASH, elige la calidad en el reproductor multimedia con un diseño que requiere una baja complejidad de procesamiento. Las pruebas concluyen su habilidad para mejorar la estabilidad, consistencia y uniformidad de la calidad de experiencia entre los clientes que comparten una celda de red. El tercer mecanismo, MEC4FAIR, explota las capacidades 5G de analizar métricas del envío de los diferentes flujos. Los resultados muestran cómo habilita al servicio a coordinar a los diferentes clientes en la celda para mejorar la calidad del servicio. El cuarto mecanismo, CogNet, sirve para provisionar recursos de red y configurar una topología capaz de conmutar una demanda estimada y garantizar unas cotas de calidad del servicio. En este caso, los resultados arrojan una mayor precisión cuando la demanda de un servicio es mayor

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

Runtime Adaptation of Scientific Service Workflows

Software landscapes are rather subject to change than being complete after having been built. Changes may be caused by a modified customer behavior, the shift to new hardware resources, or otherwise changed requirements. In such situations, several challenges arise. New architectural models have to be designed and implemented, existing software has to be integrated, and, finally, the new software has to be deployed, monitored, and, where appropriate, optimized during runtime under realistic usage scenarios. All of these situations often demand manual intervention, which causes them to be error-prone. This thesis addresses these types of runtime adaptation. Based on service-oriented architectures, an environment is developed that enables the integration of existing software (i.e., the wrapping of legacy software as web services). A workflow modeling tool that aims at an easy-to-use approach by separating the role of the workflow expert and the role of the domain expert. After the development of workflows, tools that observe the executing infrastructure and perform automatic scale-in and scale-out operations are presented. Infrastructure-as-a-Service providers are used to scale the infrastructure in a transparent and cost-efficient way. The deployment of necessary middleware tools is automatically done. The use of a distributed infrastructure can lead to communication problems. In order to keep workflows robust, these exceptional cases need to treated. But, in this way, the process logic of a workflow gets mixed up and bloated with infrastructural details, which yields an increase in its complexity. In this work, a module is presented that can deal automatically with infrastructural faults and that thereby allows to keep the separation of these two layers. When services or their components are hosted in a distributed environment, some requirements need to be addressed at each service separately. Although techniques as object-oriented programming or the usage of design patterns like the interceptor pattern ease the adaptation of service behavior or structures. Still, these methods require to modify the configuration or the implementation of each individual service. On the other side, aspect-oriented programming allows to weave functionality into existing code even without having its source. Since the functionality needs to be woven into the code, it depends on the specific implementation. In a service-oriented architecture, where the implementation of a service is unknown, this approach clearly has its limitations. The request/response aspects presented in this thesis overcome this obstacle and provide a SOA-compliant and new methods to weave functionality into the communication layer of web services. The main contributions of this thesis are the following: Shifting towards a service-oriented architecture: The generic and extensible Legacy Code Description Language and the corresponding framework allow to wrap existing software, e.g., as web services, which afterwards can be composed into a workflow by SimpleBPEL without overburdening the domain expert with technical details that are indeed handled by a workflow expert. Runtime adaption: Based on the standardized Business Process Execution Language an automatic scheduling approach is presented that monitors all used resources and is able to automatically provision new machines in case a scale-out becomes necessary. If the resource's load drops, e.g., because of less workflow executions, a scale-in is also automatically performed. The scheduling algorithm takes the data transfer between the services into account in order to prevent scheduling allocations that eventually increase the workflow's makespan due to unnecessary or disadvantageous data transfers. Furthermore, a multi-objective scheduling algorithm that is based on a genetic algorithm is able to additionally consider cost, in a way that a user can define her own preferences rising from optimized execution times of a workflow and minimized costs. Possible communication errors are automatically detected and, according to certain constraints, corrected. Adaptation of communication: The presented request/response aspects allow to weave functionality into the communication of web services. By defining a pointcut language that only relies on the exchanged documents, the implementation of services must neither be known nor be available. The weaving process itself is modeled using web services. In this way, the concept of request/response aspects is naturally embedded into a service-oriented architecture

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications