New strategies for characterizing and improving high performance I/O architectures

1 June, 2016


Nowadays, cluster and grid computing are increasing its role due to fast evolution on computer networks and communication technologies. It entails the need of storing and managing huge amounts of data efficiently. Storage subsystem performance is one of the major concerns that arise on this kind of large computing networks. The I/O system is usually a system bottleneck in most of the computing systems. Detecting the cause of the problem could be an easy task on a single computer or a small network, but detecting the problems and their causes in a large computing network is not a trivial task. The major goal of this dissertation is to identify and discover strategies to improve the performance of large storage networks, their scalability, efficient resource management, etc. To perform those tasks, we have developed a parallel simulator called SIMCAN. Using parallel simulations we are not limited to the resources that a single computer can supply using sequential models. The main goal of SIMCAN is to simulate large complex storage networks. Moreover, with this simulator, high performance applications can be modelled in large distributed environments. Thus, SIMCAN can be used for evaluating and predicting the impact of high performance applications on overall system performance.


author={Alberto Núñez Covarrubias},
title={New strategies for characterizing and improving high performance I/O architectures},
school={Universidad Carlos III de Madrid}