Motivated by the first process allocation limitations of the original Route load balancing algorithm, this paper presents RouteGA (Route with Genetic Algorithm support) which considers historical information about parallel application executions in order to optimize the first scheduling. This information is extracted by using monitors and summarized in a knowledge base used to quantify process occupation at the launch moment. Such occupation is used to parameterize a genetic algorithm responsible for optimizing the process allocation on heterogeneous computing environments such as Grids. Results confirm RouteGA overperforms the original Route, which had previously overperformed others from literature.

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Abstract. This paper proposes a new model to predict the process execution behavior on heterogeneous multicomputing environments. This model considers the process execution costs such as processing, hard disk acessing, message transmitting and memory allocation. A simulator of this model was developed which help to predict the execution behavior of processes on distributed environments under different scheduling techniques. Besides the simulator, it was developed a suite of benchmark tools in order to parameterize the proposed model with data collected from real environments. Experiments were conduced to evaluate the proposed model which used a parallel application executing on a heterogeneous system. The obtained results show the model ability to predict the actual system performance, providing an useful model for developing and evaluating techniques for scheduling and resource allocation over heterogeneous and distributed systems.

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Abstract.A new approach for acquiring knowledge of parallel applications regarding resource usage and for searching similarity on workload traces is presented. The main goal is to improve decision making in distributed system software scheduling, towards a better usage of system resources. Resource usage patterns are defined through runtime measurements and a self-organizing neural network architecture, yielding an useful model for classifying parallel applications. By means of an instance-based algorithm, it is produced another model which searches for similarity in workload traces aiming at making predictions about some attribute of a new submitted parallel application, such as run time or memory usage. These models allow effortless knowledge updating at the occurrence of new information. The paper describes these models as well as the results obtained applying these models to acquiring knowledge in both synthetic and real applications traces.