Towards Autonomic Service Provisioning Systems

This paper discusses our experience in building SPIRE, an autonomic system for service provision. The architecture consists of a set of hosted Web Services subject to QoS constraints, and a certain number of servers used to run session-based traffic. Customers pay for having their jobs run, but require in turn certain quality guarantees: there are different SLAs specifying charges for running jobs and penalties for failing to meet promised performance metrics. The system is driven by an utility function, aiming at optimizing the average earned revenue per unit time. Demand and performance statistics are collected, while traffic parameters are estimated in order to make dynamic decisions concerning server allocation and admission control. Different utility functions are introduced and a number of experiments aiming at testing their performance are discussed. Results show that revenues can be dramatically improved by imposing suitable conditions for accepting incoming traffic; the proposed system performs well under different traffic settings, and it successfully adapts to changes in the operating environment.Comment: 11 pages, 9 Figures, http://www.wipo.int/pctdb/en/wo.jsp?WO=201002636

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

Resource Provisioning for Web Applications under Time-varying Traffic

Cloud computing has gained considerable popularity in recent years. In this paradigm, an organization, referred to as a subscriber, acquires resources from an infrastructure provider to deploy its applications and pays for these resources on a pay-as-you-go basis. Typically, an infrastructure provider charges a subscriber based on resource level and duration of usage. From the subscriber's perspective, it is desirable to acquire enough capacity to provide an acceptable quality of service while minimizing the cost. A key indicator of quality of service is response time. In this thesis, we use performance models based on queueing theory to determine the required capacity to meet a performance target given by Pr[response time ≤ x] ≥ β. We first consider the case where resources are obtained from an infrastructure provider for a time period of one hour. This is compatible with the pricing policy of major infrastructure providers where instance usage is charged on an hourly basis. Over such a time period, web application traffic exhibits time-varying behavior. A conventional traffic model such as Poisson process does not capture this characteristic. The Markov-modulated Poisson process (MMPP), on the other hand, is capable of modeling such behavior. In our investigation of MMPP as a traffic model, an available workload generator is extended to produce a synthetic trace of job arrivals with a controlled level of time-variation, and an MMPP is fitted to the synthetic trace. The effectiveness of MMPP is evaluated by comparing the performance results through simulation, using as input the synthetic trace and job arrivals generated by the fitted MMPP. Queueing models with MMPP arrival process are then developed to determine the required capacity to meet a performance target over a one-hour time interval. Specifically, results on response time distribution are used in an optimization to obtain estimates of the required capacity. Two models are of interest to our investigation: a single-server model and a two-stage tandem queue. For both models, it is assumed that service time is represented by a phase-type (PH) distribution and queueing discipline is FCFS. The single-server model is therefore the MMPP/PH/1 (FCFS) model. Analytic results for time-dependent response time distribution of this model are first obtained. Computation of numerical results, however, is very costly. Through numerical examples, it is found that steady-state results are a good approximation for a time interval of one hour; the computation requirement is also significantly lower. Steady-state results are then used to determine the required capacity. The effectiveness of this model in terms of predicting the required capacity to meet the performance target is evaluated using an experimental system based on the TPC-W benchmark. Results on the impact of MMPP parameters on the required capacity are also presented. The second model is a two-stage tandem queue. The accuracy of the required capacity obtained via steady-state analysis is also evaluated using the TPC-W benchmark. We next consider the case where the infrastructure provider uses a time unit (TU) of less than one hour for charging of resource usage. We focus on scenarios where TU is comparable to the average sojourn time in an MMPP state. A one-hour operation interval is divided into a number of service intervals, each having the length one TU. At the beginning of each service interval, an estimate of the arrival rate is used as input to the M/PH/1 (FCFS) model to determine the required capacity to meet the performance target over the upcoming service interval; three heuristic algorithms are developed to estimate the arrival rate. The merit of this strategy, in terms of meeting the performance target over the operation interval and savings in capacity when compared to that determined by the single-server model, is investigated using the TPC-W benchmark