Survey Paper on Online Software Performance Prediction

Now a days Performance is very important non-functional requirement for almost all software system. In survey study we are going to learn how performance prediction is possible before the development of that particular software. For this task we have to implement one analytical model which is going to be used for evaluating the performance of software with some specific parameter like response time, throughput etc

Energy-aware scheduling in virtualized datacenters

The reduction of energy consumption in large-scale datacenters is being accomplished through an extensive use of virtualization, which enables the consolidation of multiple workloads in a smaller number of machines. Nevertheless, virtualization also incurs some additional overheads (e.g. virtual machine creation and migration) that can influence what is the best consolidated configuration, and thus, they must be taken into account. In this paper, we present a dynamic job scheduling policy for power-aware resource allocation in a virtualized datacenter. Our policy tries to consolidate workloads from separate machines into a smaller number of nodes, while fulfilling the amount of hardware resources needed to preserve the quality of service of each job. This allows turning off the spare servers, thus reducing the overall datacenter power consumption. As a novelty, this policy incorporates all the virtualization overheads in the decision process. In addition, our policy is prepared to consider other important parameters for a datacenter, such as reliability or dynamic SLA enforcement, in a synergistic way with power consumption. The introduced policy is evaluated comparing it against common policies in a simulated environment that accurately models HPC jobs execution in a virtualized datacenter including power consumption modeling and obtains a power consumption reduction of 15% with respect to typical policies.Peer ReviewedPostprint (published version

Cost and Performance-Based Resource Selection Scheme for Asynchronous Replicated System in Utility-Based Computing Environment

A resource selection problem for asynchronous replicated systems in utility-based computing environment is addressed in this paper. The needs for a special attention on this problem lies on the fact that most of the existing replication scheme in this computing system whether implicitly support synchronous replication and/or only consider read-only job. The problem is undoubtedly complex to be solved as two main issues need to be concerned simultaneously, i.e. 1) the difficulty on predicting the performance of the resources in terms of job response time, and 2) an efficient mechanism must be employed in order to measure the trade-off between the performance and the monetary cost incurred on resources so that minimum cost is preserved while providing low job response time. Therefore, a simple yet efficient algorithm that deals with the complexity of resource selection problem in utility-based computing systems is proposed in this paper. The problem is formulated as a Multi Criteria Decision Making (MCDM) problem. The advantages of the algorithm are two-folds. On one fold, it hides the complexity of resource selection process without neglecting important components that affect job response time. The difficulty on estimating job response time is captured by representing them in terms of different QoS criteria levels at each resource. On the other fold, this representation further relaxed the complexity in measuring the trade-offs between the performance and the monetary cost incurred on resources. The experiments proved that our proposed resource selection scheme achieves an appealing result with good system performance and low monetary cost as compared to existing algorithms

Aplicación de la simulación en tiempo real para mejorar la calidad de servicio del middleware

La utilización de aplicaciones de diferente naturaleza dentro de un mismo entorno, entorno heterogéneo, se está extendiendo gracias a la incorporación de técnicas de virtualización a los servidores. Compartir un servidor ofrece ventajas sobretodo en términos de eficiencia de energía, utilización del espacio o mantenimiento. La virtualización añade ventajas en la separación de las diferentes aplicaciones o entornos. Aún así los gestores de recursos para entornos heterogéneos tienen como principal dificultad ofrecer calidad de servicio (QoS) a diferentes aplicaciones, entornos o cargas. Una aplicación que realice streaming y otra que realice cálculo intensivo, normalmente , no colisionaran ya que los recursos utilizados son diferentes. Por el otro lado, colisionaran dos aplicaciones que trabajen con la CPU.Nuestra propuesta ofrece la posibilidad de introducir dentro de estos gestores de recursos la capacidad de predecir este tipo de entornos, en concreto transaccionales y Grid, para aumentar la QoS y el rendimiento. Las predicciones han de utilizar técnicas de simulación ya que la mayoria de las veces el sistema no será representable mediante técnicas analíticas, por ser un sistema saturado o tener características difíciles de representar.La simulación es una técnica utilizada para predecir el comportamiento de sistemas en multitud de áreas. Las simulaciones de componentes hardware son muy comunes, dado el coste de construcción de los sistemas simulados (procesadores, memorias...). Sin embargo, el uso de la simulación en entornos complejos, como es el middleware, y su aplicación en gestores de recursos tiene un uso muy bajo. Nosotros proponemos simulaciones ligeras capaces de obtener resultados utilizables en estos entornos.Entre las aportaciones y contribuciones de la tesis tenemos: (i) utilización de métodos de simulación para incrementar el rendimiento y la calidad de servicio de estos sistemas. (ii) ampliación de un sistema de monitorización global para aplicaciones mixtas (JAVA y C) que nos ofrece la posibilidad de conseguir información de lo que ocurre en el middleware y de relacionarlo con el sistema. (iii) creación de un gestor de recursos capaz de repartir los recursos en un entorno heterogéneo utilizando la predicción para tener en cuenta diferentes parámetros de calidad de servicio.En la tesis se muestran los mecanismos de creación de los distintos simuladores, las herramientas de obtención de datos y monitorización, así como mecanismos autónomos que pueden alimentarse de la predicción para producir mejores resultados. Los resultados obtenidos, con gran impacto en la QoS en el gestor creado para Globus, demuestran que los métodos aplicados en esta tesis pueden ser válidos para crear gestores de recursos inteligentes, alimentados de las predicciones del sistema para tomar decisiones. Finalmente, utilizamos las simulaciones realizadas incorporándolas dentro de un prototipo de gestor de recursos heterogéneo capaz de repartir los recursos entre un entorno transaccional y un entorno Grid dentro del mismo servidor.Using different applications inside the same environment, heterogeneous environment, is getting more and more usual due the incorporation of the virtualization inside servers. Sharing a server offer advantages in different levels: energy, space, management. Virtualization helps to separate different applications or environments. On the other hand, resource managers have as principal issue offer Quality of Service for different applications, environments or workloads. A streaming server and a CPU intensive application would not collide; the resources they need are different. However, two applications that need CPU processing power will collide.Our proposal offers the possibility to introduce inside the resource manager the capacity to predict these environments. We will work with transactional and Grid environments, and we will increase the QoS and the performance. We need to use simulation techniques for our predictions because a large number of times the system won't be able to be modelled with analytic techniques, for being a saturated system or having features that are hard to reproduce.Simulation is a technique used to predict the behaviour of multiple systems in a large number of areas. Hardware simulations are very common because the building/testing cost of the simulated system (processor, memory, cache,...) is high. However, using simulation in complex environments, as the middleware, and its use in resource management is low. We propose light simulations that can obtain results that can be used in these environments.We will enumerate our contributions: (i) Use simulations to increase the performance and the QoS of those systems. (ii) Improve a global monitoring system for mixed applications (JAVA and C) that gives us information about what happens in the middleware and in the system. (iii) Build a resource manager that can share the resources in a heterogeneous environment an use the prediction to ensure the different QoS parameters that we provide.In the thesis we show how we built the different simulators, the different tools to obtain information and monitorize the applications, and finally the autonomic mechanisms that can feed with the prediction to obtain better results. Results obtained, with great success in the case of the resource manager created for Globus, show and demonstrate that the applied methods in this thesis are suitable to create intelligent resource managers, fed with predictions of the system to take decisions. Finally, we add the built simulations inside a heterogeneous resource manager that shares resources between a transactional environment and a Grid environment inside the same server

Improved self-management of datacenter systems applying machine learning

Autonomic Computing is a Computer Science and Technologies research area, originated during mid 2000's. It focuses on optimization and improvement of complex distributed computing systems through self-control and self-management. As distributed computing systems grow in complexity, like multi-datacenter systems in cloud computing, the system operators and architects need more help to understand, design and optimize manually these systems, even more when these systems are distributed along the world and belong to different entities and authorities. Self-management lets these distributed computing systems improve their resource and energy management, a very important issue when resources have a cost, by obtaining, running or maintaining them. Here we propose to improve Autonomic Computing techniques for resource management by applying modeling and prediction methods from Machine Learning and Artificial Intelligence. Machine Learning methods can find accurate models from system behaviors and often intelligible explanations to them, also predict and infer system states and values. These models obtained from automatic learning have the advantage of being easily updated to workload or configuration changes by re-taking examples and re-training the predictors. So employing automatic modeling and predictive abilities, we can find new methods for making "intelligent" decisions and discovering new information and knowledge from systems. This thesis departs from the state of the art, where management is based on administrators expertise, well known data, ad-hoc studied algorithms and models, and elements to be studied from computing machine point of view; to a novel state of the art where management is driven by models learned from the same system, providing useful feedback, making up for incomplete, missing or uncertain data, from a global network of datacenters point of view. - First of all, we cover the scenario where the decision maker works knowing all pieces of information from the system: how much will each job consume, how is and will be the desired quality of service, what are the deadlines for the workload, etc. All of this focusing on each component and policy of each element involved in executing these jobs. -Then we focus on the scenario where instead of fixed oracles that provide us information from an expert formula or set of conditions, machine learning is used to create these oracles. Here we look at components and specific details while some part of the information is not known and must be learned and predicted. - We reduce the problem of optimizing resource allocations and requirements for virtualized web-services to a mathematical problem, indicating each factor, variable and element involved, also all the constraints the scheduling process must attend to. The scheduling problem can be modeled as a Mixed Integer Linear Program. Here we face an scenario of a full datacenter, further we introduce some information prediction. - We complement the model by expanding the predicted elements, studying the main resources (this is CPU, Memory and IO) that can suffer from noise, inaccuracy or unavailability. Once learning predictors for certain components let the decision making improve, the system can become more ¿expert-knowledge independent¿ and research can focus on an scenario where all the elements provide noisy, uncertainty or private information. Also we introduce to the management optimization new factors as for each datacenter context and costs may change, turning the model as "multi-datacenter" - Finally, we review of the cost of placing datacenters depending on green energy sources, and distribute the load according to green energy availability

Architecture-Level Software Performance Models for Online Performance Prediction

Proactive performance and resource management of modern IT infrastructures requires the ability to predict at run-time, how the performance of running services would be affected if the workload or the system changes. In this thesis, modeling and prediction facilities that enable online performance prediction during system operation are presented. Analyses about the impact of reconfigurations and workload trends can be conducted on the model level, without executing expensive performance tests