AUGURES : profit-aware web infrastructure management

Over the last decade, advances in technology together with the increasing use of the Internet for everyday tasks, are causing profound changes in end-users, as well as in businesses and technology providers. The widespread adoption of high-speed and ubiquitous Internet access, is also changing the way users interact with Web applications and their expectations in terms of Quality-of-Service (QoS) and User eXperience (UX). Recently, Cloud computing has been rapidly adopted to host and manage Web applications, due to its inherent cost effectiveness and on-demand scaling of infrastructures. However, system administrators still need to make manual decisions about the parameters that affect the business results of their applications ie., setting QoS targets and defining metrics for scaling the number of servers during the day. Therefore, understanding the workload and user behavior ¿the demand, poses new challenges for capacity planning and scalability ¿the supply, and ultimately for the success of a Web site. This thesis contributes to the current state-of-art of Web infrastructure management by providing: i) a methodology for predicting Web session revenue; ii) a methodology to determine high response time effect on sales; and iii) a policy for profit-aware resource management, that relates server capacity, to QoS, and sales. The approach leverages Machine Learning (ML) techniques on custom, real-life datasets from an Ecommerce retailer featuring popular Web applications. Where the experimentation shows how user behavior and server performance models can be built from offline information, to determine how demand and supply relations work as resources are consumed. Producing in this way, economical metrics that are consumed by profit-aware policies, that allow the self-configuration of cloud infrastructures to an optimal number of servers under a variety of conditions. While at the same time, the thesis, provides several insights applicable for improving Autonomic infrastructure management and the profitability of Ecommerce applications.Durante la última década, avances en tecnología junto al incremento de uso de Internet, están causando cambios en los usuarios finales, así como también a las empresas y proveedores de tecnología. La adopción masiva del acceso ubicuo a Internet de alta velocidad, crea cambios en la forma de interacción con las aplicaciones Web y en las expectativas de los usuarios en relación de calidad de servicio (QoS) y experiencia de usuario (UX) ofrecidas. Recientemente, el modelo de computación Cloud ha sido adoptado rápidamente para albergar y gestionar aplicaciones Web, debido a su inherente efectividad en costos y servidores bajo demanda. Sin embargo, los administradores de sistema aún tienen que tomar decisiones manuales con respecto a los parámetros de ejecución que afectan a los resultados de negocio p.ej. definir objetivos de QoS y métricas para escalar en número de servidores. Por estos motivos, entender la carga y el comportamiento de usuario (la demanda), pone nuevos desafíos a la planificación de capacidad y escalabilidad (el suministro), y finalmente el éxito de un sitio Web.Esta tesis contribuye al estado del arte actual en gestión de infraestructuras Web presentado: i) una metodología para predecir los beneficios de una sesión Web; ii) una metodología para determinar el efecto de tiempos de respuesta altos en las ventas; y iii) una política para la gestión de recursos basada en beneficios, al relacionar la capacidad de los servidores, QoS, y ventas. La propuesta se basa en aplicar técnicas Machine Learning (ML) a fuentes de datos de producción de un proveedor de Ecommerce, que ofrece aplicaciones Web populares. Donde los experimentos realizados muestran cómo modelos de comportamiento de usuario y de rendimiento de servidor pueden obtenerse de datos históricos; con el fin de determinar la relación entre la demanda y el suministro, según se utilizan los recursos. Produciendo así, métricas económicas que son luego aplicadas en políticas basadas en beneficios, para permitir la auto-configuración de infraestructuras Cloud a un número adecuado de servidores. Mientras que al mismo tiempo, la tesis provee información relevante para mejorar la gestión de infraestructuras Web de forma autónoma y aumentar los beneficios en aplicaciones de Ecommerce

Markov chain Monte Carlo for continuous-time discrete-state systems

A variety of phenomena are best described using dynamical models which operate on a discrete state space and in continuous time. Examples include Markov (and semi-Markov) jump processes, continuous-time Bayesian networks, renewal processes and other point processes. These continuous-time, discrete-state models are ideal building blocks for Bayesian models in fields such as systems biology, genetics, chemistry, computing networks, human-computer interactions etc. However, a challenge towards their more widespread use is the computational burden of posterior inference; this typically involves approximations like time discretization and can be computationally intensive. In this thesis, we describe a new class of Markov chain Monte Carlo methods that allow efficient computation while still being exact. The core idea is an auxiliary variable Gibbs sampler that alternately resamples a random discretization of time given the state-trajectory of the system, and then samples a new trajectory given this discretization. We introduce this idea by relating it to a classical idea called uniformization, and use it to develop algorithms that outperform the state-of-the-art for models based on the Markov jump process. We then extend the scope of these samplers to a wider class of models such as nonstationary renewal processes, and semi-Markov jump processes. By developing a more general framework beyond uniformization, we remedy various limitations of the original algorithms, allowing us to develop MCMC samplers for systems with infinite state spaces, unbounded rates, as well as systems indexed by more general continuous spaces than time

Investigation of the tolerance of wavelength-routed optical networks to traffic load variations.

This thesis focuses on the performance of circuit-switched wavelength-routed optical network with unpredictable traffic pattern variations. This characteristic of optical networks is termed traffic forecast tolerance. First, the increasing volume and heterogeneous nature of data and voice traffic is discussed. The challenges in designing robust optical networks to handle unpredictable traffic statistics are described. Other work relating to the same research issues are discussed. A general methodology to quantify the traffic forecast tolerance of optical networks is presented. A traffic model is proposed to simulate dynamic, non-uniform loads, and used to test wavelength-routed optical networks considering numerous network topologies. The number of wavelengths required and the effect of the routing and wavelength allocation algorithm are investigated. A new method of quantifying the network tolerance is proposed, based on the calculation of the increase in the standard deviation of the blocking probabilities with increasing traffic load non-uniformity. The performance of different networks are calculated and compared. The relationship between physical features of the network topology and traffic forecast tolerance is investigated. A large number of randomly connected networks with different sizes were assessed. It is shown that the average lightpath length and the number of wavelengths required for full interconnection of the nodes in static operation both exhibit a strong correlation with the network tolerance, regardless of the degree of load non-uniformity. Finally, the impact of wavelength conversion on network tolerance is investigated. Wavelength conversion significantly increases the robustness of optical networks to unpredictable traffic variations. In particular, two sparse wavelength conversion schemes are compared and discussed: distributed wavelength conversion and localized wavelength conversion. It is found that the distributed wavelength conversion scheme outperforms localized wavelength conversion scheme, both with uniform loading and in terms of the network tolerance. The results described in this thesis can be used for the analysis and design of reliable WDM optical networks that are robust to future traffic demand variations

Improving Resource Efficiency in Cloud Computing

Customers inside the cloud computing market are heterogeneous in several aspects, e.g., willingness to pay and performance requirement. By taking advantage of trade-offs created by these heterogeneities, the service provider can realize a more efficient system. This thesis is concerned with methods to improve the utilization of cloud infrastructure resources, and with the role of pricing in realizing those improvements and leveraging heterogeneity. Towards improving utilization, we explore methods to optimize network usage through traffic engineering. Particularly, we introduce a novel optimization framework to decrease the bandwidth required by inter-data center networks through traffic scheduling and shaping, and then propose algorithms to improve network utilization based on the analytical results derived from the optimization. When considering pricing, we focus on elucidating conditions under which providing a mix of services can increase a service provider\u27s revenue. Specifically, we characterize the conditions under which providing a ``delayed\u27\u27 service can result in a higher revenue for the service provider, and then offer guidelines for both users and providers

Optical flow switched networks

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.Includes bibliographical references (p. 253-279).In the four decades since optical fiber was introduced as a communications medium, optical networking has revolutionized the telecommunications landscape. It has enabled the Internet as we know it today, and is central to the realization of Network-Centric Warfare in the defense world. Sustained exponential growth in communications bandwidth demand, however, is requiring that the nexus of innovation in optical networking continue, in order to ensure cost-effective communications in the future. In this thesis, we present Optical Flow Switching (OFS) as a key enabler of scalable future optical networks. The general idea behind OFS-agile, end-to-end, all-optical connections-is decades old, if not as old as the field of optical networking itself. However, owing to the absence of an application for it, OFS remained an underdeveloped idea-bereft of how it could be implemented, how well it would perform, and how much it would cost relative to other architectures. The contributions of this thesis are in providing partial answers to these three broad questions. With respect to implementation, we address the physical layer design of OFS in the metro-area and access, and develop sensible scheduling algorithms for OFS communication. Our performance study comprises a comparative capacity analysis for the wide-area, as well as an analytical approximation of the throughput-delay tradeoff offered by OFS for inter-MAN communication. Lastly, with regard to the economics of OFS, we employ an approximate capital expenditure model, which enables a throughput-cost comparison of OFS with other prominent candidate architectures. Our conclusions point to the fact that OFS offers significant advantage over other architectures in economic scalability.(cont.) In particular, for sufficiently heavy traffic, OFS handles large transactions at far lower cost than other optical network architectures. In light of the increasing importance of large transactions in both commercial and defense networks, we conclude that OFS may be crucial to the future viability of optical networking.by Guy E. Weichenberg.Ph.D