Fuzzy C-Mean And Genetic Algorithms Based Scheduling For Independent Jobs In Computational Grid

The concept of Grid computing is becoming the most important research area in the high performance computing. Under this concept, the jobs scheduling in Grid computing has more complicated problems to discover a diversity of available resources, select the appropriate applications and map to suitable resources. However, the major problem is the optimal job scheduling, which Grid nodes need to allocate the appropriate resources for each job. In this paper, we combine Fuzzy C-Mean and Genetic Algorithms which are popular algorithms, the Grid can be used for scheduling. Our model presents the method of the jobs classifications based mainly on Fuzzy C-Mean algorithm and mapping the jobs to the appropriate resources based mainly on Genetic algorithm. In the experiments, we used the workload historical information and put it into our simulator. We get the better result when compared to the traditional algorithms for scheduling policies. Finally, the paper also discusses approach of the jobs classifications and the optimization engine in Grid scheduling

Autonomic Cloud Computing: Open Challenges and Architectural Elements

As Clouds are complex, large-scale, and heterogeneous distributed systems, management of their resources is a challenging task. They need automated and integrated intelligent strategies for provisioning of resources to offer services that are secure, reliable, and cost-efficient. Hence, effective management of services becomes fundamental in software platforms that constitute the fabric of computing Clouds. In this direction, this paper identifies open issues in autonomic resource provisioning and presents innovative management techniques for supporting SaaS applications hosted on Clouds. We present a conceptual architecture and early results evidencing the benefits of autonomic management of Clouds.Comment: 8 pages, 6 figures, conference keynote pape

Distributed computing practice for large-scale science and engineering applications

It is generally accepted that the ability to develop large-scale distributed applications has lagged seriously behind other developments in cyberinfrastructure. In this paper, we provide insight into how such applications have been developed and an understanding of why developing applications for distributed infrastructure is hard. Our approach is unique in the sense that it is centered around half a dozen existing scientific applications; we posit that these scientific applications are representative of the characteristics, requirements, as well as the challenges of the bulk of current distributed applications on production cyberinfrastructure (such as the US TeraGrid). We provide a novel and comprehensive analysis of such distributed scientific applications. Specifically, we survey existing models and methods for large-scale distributed applications and identify commonalities, recurring structures, patterns and abstractions. We find that there are many ad hoc solutions employed to develop and execute distributed applications, which result in a lack of generality and the inability of distributed applications to be extensible and independent of infrastructure details. In our analysis, we introduce the notion of application vectors: a novel way of understanding the structure of distributed applications. Important contributions of this paper include identifying patterns that are derived from a wide range of real distributed applications, as well as an integrated approach to analyzing applications, programming systems and patterns, resulting in the ability to provide a critical assessment of the current practice of developing, deploying and executing distributed applications. Gaps and omissions in the state of the art are identified, and directions for future research are outlined

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

Conceptual and Implementation Models for the Grid

The Grid is rapidly emerging as the dominant paradigm for wide area distributed application systems. As a result, there is a need for modeling and analyzing the characteristics and requirements of Grid systems and programming models. This paper adopts the well-established body of models for distributed computing systems, which are based upon carefully stated assumptions or axioms, as a basis for defining and characterizing Grids and their programming models and systems. The requirements of programming Grid applications and the resulting requirements on the underlying virtual organizations and virtual machines are investigated. The assumptions underlying some of the programming models and systems currently used for Grid applications are identified and their validity in Grid environments is discussed. A more in-depth analysis of two programming systems, the Imperial College E-Science Networked Infrastructure (ICENI) and Accord, using the proposed definitions’ structure is presented. Keywords—Distributed systems, Grid programming models, Grid programming systems, Grid system definition. I