A Neural Network to Estimate Isolated Performance from Multi-Program Execution

When multiple applications are running on a platform with shared resources like multicore CPUs, the behaviour of the running application can be altered by the co-runners. In this case, the system resources need to be managed (e.g. by repartitioning the cache space, re-schedule applications in distinct cores, modifying the prefetcher configuration, etc.) to reduce the inter-application interference in order to minimize the performance losses over isolated execution. In this context, a main challenge in different computing scenarios like the public cloud or soft real-time systems is knowing the performance impact of a given management action on each application with respect to its isolated execution. With this aim, in this work we present a neural network-based approach that estimates the performance an application would have had in isolation from multi-program executions. Experimental results show that the proposal dynamically adapts to changes in application behavior. On average, the predicted performance presents an error deviation by 11.7% and 2.3% for MAPE and MSE respectively.