Application of Micro-Genetic Algorithm for Task Based Computing

Abstract — Pervasive computing calls for applications which are often composed from independent and distributed components using facilities from the environment. This paradigm has evolved into task based computing where the application composition relies on explicit user task descriptions. The composition of applications has to be performed at run-time as the environment is dynamic and heterogeneous due to e.g., mobility of the user. An algorithm that decides on a component set and allocates it onto hosts accordingly to user task preferences and the platform constraints plays a central role in the application composition process. In this paper we will describe an algorithm for task-based application allocation. The algorithm uses micro-genetic approach and is characterized by a very low computational load and good convergence properties. We will compare the performance and the scalability of our algorithm with a straightforward evolutionary algorithm. Besides, we will outline a system for task-based computing where our algorithm is used. I

Partitioning Bin-Packing Algorithms for Distributed Real-Time Systems

Embedded real-time systems must satisfy not only logical functional requirements but also para-functional properties such as timeliness, Quality of Service (QoS) and reliability. We have developed a model-based tool called Time Weaver which enables the modeling of functional and para-functional behaviors of real-time systems. It also performs automated schedulability analysis, and generates glue code to integrate the final runtime executable for the system. Its extensive glue code generation capabilities include the ability to insert inter-processor communications code at arbitrary software boundaries. In other words, from a functional point of view, a software component may be viewed as a single logical entity but from the tool point of view, the component can be partitioned into two or more pieces running on different nodes. This capability opens up many different possibilities to map (partitioned) software components to hardware nodes. The objective of this deployment is to minimize hardware requirements while satisfying the timing constraints of the software. The classical approach to addressing this problem is to use bin-packing techniques. In this paper, we study Partitioning Bin Packing, an extension to bin-packing algorithms to exploit the capability of partitioning software modules into smaller pieces. We analytically show that the number of bins required can be reduced. We also evaluate a number of heuristics to minimize not only the number of processors (bins) needed but also the network bandwidth required by communicating software modules that are partitioned across different processors. We find that a significant reduction in the number of bins is possible. Finally, we show how different heuristics lead to different tradeoffs in processing vs network needs

Gestor de recursos sobre sistemas virtualizados

Creacion, diseño e implementación de una nube (cloud) basado en un clúster de servidores con sistema de ficheros distribuido en red y virtualización de sistemas operativos que mejora la utilización de los recursos disponibles. Esta nueva arquitectura ofrece una nube privada que simplifica la gestión de los servicios y aporta una disminución del coste de mantenimiento, ademas de añadir flexibilidad, dinamismo y escalabilidad al sistema. Las tecnologías con las que se trabaja son GNU/Linux, Xen Source, NFS, Java, Hibernate, Wicket, ShellScripting. El proyectista dispondrá de un clúster de 10 servidores para la implementación y las pruebas