Calidad de servicio en computación en la nube: técnicas de modelado y sus aplicaciones

Recent years have seen the massive migration of enterprise applications to the cloud. One of the challenges posed by cloud applications is Quality-of-Service (QoS) management, which is the problem of allocating resources to the application to guarantee a service level along dimensions such as performance, availability and reliability. This paper aims at supporting research in this area by providing a survey of the state of the art of QoS modeling approaches suitable for cloud systems. We also review and classify their early application to some decision-making problems arising in cloud QoS management

Modeling and Prediction of I/O Performance in Virtualized Environments

We present a novel performance modeling approach tailored to I/O performance prediction in virtualized environments. The main idea is to identify important performance-influencing factors and to develop storage-level I/O performance models. To increase the practical applicability of these models, we combine the low-level I/O performance models with high-level software architecture models. Our approach is validated in a variety of case studies in state-of-the-art, real-world environments

Automated Experiments for Deriving Performance-relevant Properties of Software Execution Environments

The execution environment can play a crucial role when analyzing the performance of a software system. However, detecting execution environment properties and integrating such properties into performance analyses is a manual, error-prone task. In this thesis, a novel approach for detecting performance-relevant properties of the software execution environment is presented. These properties are automatically detected using predefined experiments and integrated into performance prediction tools

Autonomic Performance-Aware Resource Management in Dynamic IT Service Infrastructures

Model-based techniques are a powerful approach to engineering autonomic and self-adaptive systems. This thesis presents a model-based approach for proactive and autonomic performance-aware resource management in dynamic IT infrastructures. Core of the approach is an architecture-level modeling language to describe performance and resource management related aspects in such environments. With this approach, it is possible to autonomically find suitable system configurations at the model level

Application Driven MOdels for Resource Management in Cloud Environments

El despliegue y la ejecución de aplicaciones de gran escala en sistemas distribuidos con unos parametros de Calidad de Servicio adecuados necesita gestionar de manera eficiente los recursos computacionales. Para desacoplar los requirimientos funcionales y los no funcionales (u operacionales) de dichas aplicaciones, se puede distinguir dos niveles de abstracción: i) el nivel funcional, que contempla aquellos requerimientos relacionados con funcionalidades de la aplicación; y ii) el nivel operacional, que depende del sistema distribuido donde se despliegue y garantizará aquellos parámetros relacionados con la Calidad del Servicio, disponibilidad, tolerancia a fallos y coste económico, entre otros. De entre las diferentes alternativas del nivel operacional, en la presente tesis se contempla un entorno cloud basado en la virtualización de contenedores, como puede ofrecer Kubernetes.El uso de modelos para el diseño de aplicaciones en ambos niveles permite garantizar que dichos requerimientos sean satisfechos. Según la complejidad del modelo que describa la aplicación, o el conocimiento que el nivel operacional tenga de ella, se diferencian tres tipos de aplicaciones: i) aplicaciones dirigidas por el modelo, como es el caso de la simulación de eventos discretos, donde el propio modelo, por ejemplo Redes de Petri de Alto Nivel, describen la aplicación; ii) aplicaciones dirigidas por los datos, como es el caso de la ejecución de analíticas sobre Data Stream; y iii) aplicaciones dirigidas por el sistema, donde el nivel operacional rige el despliegue al considerarlas como una caja negra.En la presente tesis doctoral, se propone el uso de un scheduler específico para cada tipo de aplicación y modelo, con ejemplos concretos, de manera que el cliente de la infraestructura pueda utilizar información del modelo descriptivo y del modelo operacional. Esta solución permite rellenar el hueco conceptual entre ambos niveles. De esta manera, se proponen diferentes métodos y técnicas para desplegar diferentes aplicaciones: una simulación de un sistema de Vehículos Eléctricos descrita a través de Redes de Petri; procesado de algoritmos sobre un grafo que llega siguiendo el paradigma Data Stream; y el propio sistema operacional como sujeto de estudio.En este último caso de estudio, se ha analizado cómo determinados parámetros del nivel operacional (por ejemplo, la agrupación de contenedores, o la compartición de recursos entre contenedores alojados en una misma máquina) tienen un impacto en las prestaciones. Para analizar dicho impacto, se propone un modelo formal de una infrastructura operacional concreta (Kubernetes). Por último, se propone una metodología para construir índices de interferencia para caracterizar aplicaciones y estimar la degradación de prestaciones incurrida cuando dos contenedores son desplegados y ejecutados juntos. Estos índices modelan cómo los recursos del nivel operacional son usados por las applicaciones. Esto supone que el nivel operacional maneja información cercana a la aplicación y le permite tomar mejores decisiones de despliegue y distribución.<br /

Artificial intelligence driven anomaly detection for big data systems

The main goal of this thesis is to contribute to the research on automated performance anomaly detection and interference prediction by implementing Artificial Intelligence (AI) solutions for complex distributed systems, especially for Big Data platforms within cloud computing environments. The late detection and manual resolutions of performance anomalies and system interference in Big Data systems may lead to performance violations and financial penalties. Motivated by this issue, we propose AI-based methodologies for anomaly detection and interference prediction tailored to Big Data and containerized batch platforms to better analyze system performance and effectively utilize computing resources within cloud environments. Therefore, new precise and efficient performance management methods are the key to handling performance anomalies and interference impacts to improve the efficiency of data center resources. The first part of this thesis contributes to performance anomaly detection for in-memory Big Data platforms. We examine the performance of Big Data platforms and justify our choice of selecting the in-memory Apache Spark platform. An artificial neural network-driven methodology is proposed to detect and classify performance anomalies for batch workloads based on the RDD characteristics and operating system monitoring metrics. Our method is evaluated against other popular machine learning algorithms (ML), as well as against four different monitoring datasets. The results prove that our proposed method outperforms other ML methods, typically achieving 98–99% F-scores. Moreover, we prove that a random start instant, a random duration, and overlapped anomalies do not significantly impact the performance of our proposed methodology. The second contribution addresses the challenge of anomaly identification within an in-memory streaming Big Data platform by investigating agile hybrid learning techniques. We develop TRACK (neural neTwoRk Anomaly deteCtion in sparK) and TRACK-Plus, two methods to efficiently train a class of machine learning models for performance anomaly detection using a fixed number of experiments. Our model revolves around using artificial neural networks with Bayesian Optimization (BO) to find the optimal training dataset size and configuration parameters to efficiently train the anomaly detection model to achieve high accuracy. The objective is to accelerate the search process for finding the size of the training dataset, optimizing neural network configurations, and improving the performance of anomaly classification. A validation based on several datasets from a real Apache Spark Streaming system is performed, demonstrating that the proposed methodology can efficiently identify performance anomalies, near-optimal configuration parameters, and a near-optimal training dataset size while reducing the number of experiments up to 75% compared with naïve anomaly detection training. The last contribution overcomes the challenges of predicting completion time of containerized batch jobs and proactively avoiding performance interference by introducing an automated prediction solution to estimate interference among colocated batch jobs within the same computing environment. An AI-driven model is implemented to predict the interference among batch jobs before it occurs within system. Our interference detection model can alleviate and estimate the task slowdown affected by the interference. This model assists the system operators in making an accurate decision to optimize job placement. Our model is agnostic to the business logic internal to each job. Instead, it is learned from system performance data by applying artificial neural networks to establish the completion time prediction of batch jobs within the cloud environments. We compare our model with three other baseline models (queueing-theoretic model, operational analysis, and an empirical method) on historical measurements of job completion time and CPU run-queue size (i.e., the number of active threads in the system). The proposed model captures multithreading, operating system scheduling, sleeping time, and job priorities. A validation based on 4500 experiments based on the DaCapo benchmarking suite was carried out, confirming the predictive efficiency and capabilities of the proposed model by achieving up to 10% MAPE compared with the other models.Open Acces

Analytical characterization of inband and outband D2D Communications for network access

Mención Internacional en el título de doctorCooperative short-range communication schemes provide powerful tools to solve interference and resource shortage problems in wireless access networks. With such schemes, a mobile node with excellent cellular connectivity can momentarily accept to relay traffic for its neighbors experiencing poor radio conditions and use Device-to-Device (D2D) communications to accomplish the task. This thesis provides a novel and comprehensive analytical framework that allows evaluating the effects of D2D communications in access networks in terms of spectrum and energy efficiency. The analysis covers the cases in which D2D communications use the same bandwidth of legacy cellular users (in-band D2D) or a different one (out-band D2D) and leverages on the characterization of underlying queueing systems and protocols to capture the complex intertwining of short-range and legacy WiFi and cellular communications. The analysis also unveils how D2D affects the use and scope of other optimization techniques used for, e.g., interference coordination and fairness in resource distribution. Indeed, characterizing the performance of D2D-enabled wireless access networks plays an essential role in the optimization of system operation and, as a consequence, permits to assess the general applicability of D2D solutions. With such characterization, we were able to design several mechanisms that improve system capabilities. Specifically, we propose bandwidth resource management techniques for controlling interference when cellular users and D2D pairs share the same spectrum, we design advanced and energy-aware access selection mechanisms, we show how to adopt D2D communications in conjunction with interference coordination schemes to achieve high and fair throughputs, and we discuss on end-to-end fairness—beyond the use of access network resources—when D2D communications is adopted in C-RAN. The results reported in this thesis show that identifying performance bottlenecks is key to properly control network operation, and, interestingly, bottlenecks may not be represented just by wireless resources when end-to-end fairness is of concern.Programa Oficial de Doctorado en Ingeniería TelemáticaPresidente: Marco Ajmone Marsan.- Secretario: Miquel Payaró Llisterri.- Vocal: Omer Gurewit

Journal of Telecommunications and Information Technology, 2018, nr 4

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