Using Runtime Measurements and Historical Traces for Acquiring Knowledge in Parallel Applications

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.