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Towards an Autonomie Storage System to Improve Parallel I/O

Abstract:

In this paper, we present a mechanism able to pbkp_redict the performance a given workload will achieve when running on a given storage device. This mechanism is composed by two modules. The first one is able to learn the behavior of a workload in order to be able to reproduce its behavior later on, without a new execution, even when the storage drives or data placement are modified. The second module is a drive modeler that is able to learn how a storage drive works in an automatic way, just executing some synthetic tests. Once we have the workload and drive models, we can pbkp_redict how well the application will perform on the selected storage device or devices or when the data placement is modified. The results presented in this paper will show that this pbkp_rediction system achieves errors below 10% when compared to the real performance obtained. It is important to notice that the two modules will treat both the application and the storage device as black boxes and will need no previous information about them.