Multiprocessor task scheduling in multistage hyrid flowshops: a genetic algorithm approach

This paper considers multiprocessor task scheduling in a multistage hybrid flow-shop environment. The objective is to minimize the make-span, that is, the completion time of all the tasks in the last stage. This problem is of practical interest in the textile and process industries. A genetic algorithm (GA) is developed to solve the problem. The GA is tested against a lower bound from the literature as well as against heuristic rules on a test bed comprising 400 problems with up to 100 jobs, 10 stages, and with up to five processors on each stage. For small problems, solutions found by the GA are compared to optimal solutions, which are obtained by total enumeration. For larger problems, optimum solutions are estimated by a statistical prediction technique. Computational results show that the GA is both effective and efficient for the current problem. Test problems are provided in a web site at www.benchmark.ibu.edu.tr/mpt-h; fsp

Survivable algorithms and redundancy management in NASA's distributed computing systems

The design of survivable algorithms requires a solid foundation for executing them. While hardware techniques for fault-tolerant computing are relatively well understood, fault-tolerant operating systems, as well as fault-tolerant applications (survivable algorithms), are, by contrast, little understood, and much more work in this field is required. We outline some of our work that contributes to the foundation of ultrareliable operating systems and fault-tolerant algorithm design. We introduce our consensus-based framework for fault-tolerant system design. This is followed by a description of a hierarchical partitioning method for efficient consensus. A scheduler for redundancy management is introduced, and application-specific fault tolerance is described. We give an overview of our hybrid algorithm technique, which is an alternative to the formal approach given

Parallel local search for solving Constraint Problems on the Cell Broadband Engine (Preliminary Results)

We explore the use of the Cell Broadband Engine (Cell/BE for short) for combinatorial optimization applications: we present a parallel version of a constraint-based local search algorithm that has been implemented on a multiprocessor BladeCenter machine with twin Cell/BE processors (total of 16 SPUs per blade). This algorithm was chosen because it fits very well the Cell/BE architecture and requires neither shared memory nor communication between processors, while retaining a compact memory footprint. We study the performance on several large optimization benchmarks and show that this achieves mostly linear time speedups, even sometimes super-linear. This is possible because the parallel implementation might explore simultaneously different parts of the search space and therefore converge faster towards the best sub-space and thus towards a solution. Besides getting speedups, the resulting times exhibit a much smaller variance, which benefits applications where a timely reply is critical

Survey on Heuristic Search Techniques to Solve Artificial Intelligence Problems

Artificial intelligence (AI) is an area of computer science that highlights the creation of machines that are intelligent, also they work and react like humans. Since AI problems are complex and cannot be solved with direct techniques we resort to heuristic search techniques. Heuristic search technique is any approach to problem solving, learning, or discovery which uses a practical methodology which is not guaranteed to be optimal or perfect, but it is sufficient for the immediate goals. This paper surveys some of the heuristic techniques which is used for searching an optimal solution among multiprocessor environment, followed by and method which enhances the search by doing a search in bidirection and also a method for task scheduling in multiprocessor environment. The paper also discuses about how heuristic is used to solve binary quadratic program and also how it is used in 3G (3rd Generation) Universal Mobile Telecommunication System (UMTS) network. DOI: 10.17762/ijritcc2321-8169.15058

Ant Colony Heuristic for Mapping and Scheduling Tasks and Communications on Heterogeneous Embedded Systems

To exploit the power of modern heterogeneous multiprocessor embedded platforms on partitioned applications, the designer usually needs to efficiently map and schedule all the tasks and the communications of the application, respecting the constraints imposed by the target architecture. Since the problem is heavily constrained, common methods used to explore such design space usually fail, obtaining low-quality solutions. In this paper, we propose an ant colony optimization (ACO) heuristic that, given a model of the target architecture and the application, efficiently executes both scheduling and mapping to optimize the application performance. We compare our approach with several other heuristics, including simulated annealing, tabu search, and genetic algorithms, on the performance to reach the optimum value and on the potential to explore the design space. We show that our approach obtains better results than other heuristics by at least 16% on average, despite an overhead in execution time. Finally, we validate the approach by scheduling and mapping a JPEG encoder on a realistic target architecture

Problema de contratación de carretilleros para un almacén de productos manufacturados

En este trabajo se analiza un problema planteado recientemente a sus autores por una empresa fabricante de componentes de automóviles. Dicha empresa almacena sus productos manufacturados hasta que los clientes (compradores) pasan a recogerlos. Los clientes solicitan sus productos con una frecuencia conocida. Se trata de determinar, en función de dichas frecuencias, en qué fechas y a qué horas o slots han de pasar los clientes a recoger sus pedidos. Fijado el horizonte temporal objeto de estudio, el objetivo para la empresa es minimizar en ese conjunto de días, el número de carretilleros necesarios para cargar los pedidos en los camiones de los clientes. El número de carretilleros diarios viene determinado por el slot más ocupado. En este problema se han de tomar decisiones a dos niveles: elaboración del calendario de entrega para cada uno de los pedidos, y asignación diaria de pedidos a slots. No se ha encontrado en la literatura ningún trabajo que estudie o pueda ajustarse a este problema. Se diseñan y analizan 3 sencillos Metaheurísticos: uno sigue un proceso de Búsqueda Tabú básico, otro es un procedimiento de Búsqueda en Entorno Variable y el tercero es un Algoritmo Evolutivo. Se realizan pruebas con instancias ficticias y por último se resulven las instancias reales planteadas a los autores de este trabajo

A hybrid ant algorithm for scheduling independent jobs in heterogeneous computing environments

The efficient scheduling of independent computational jobs in a heterogeneous computing (HC) environment is an important problem in domains such as grid computing. Finding optimal schedules for such an environment is (in general) an NP-hard problem, and so heuristic approaches must be used. In this paper we describe an ant colony optimisation (ACO) algorithm that, when combined with local and tabu search, can find shorter schedules on benchmark problems than other techniques found in the literature