The constant growth in the number of cores in SoCs implies an important issue: scalability. NoC-based MPSoCs offer scalability at the hardware level. However, the management of the MPSoC resources requires also scalable methods, to effectively extract the computational power offered by dozens of processors. State-of-the-art proposals adopt different approaches to tackle such problem, using the MPSoC clustering as the most common approach. The present work proposes a distributed mapping approach, using a clustering method, having as main goal to evaluate its pros and cons. Evaluation is carried-out using cycle accurate simulation, in large MPSoCs (up to 144 processors). Results show an important reduction in the total execution time of the applications running in the MPSoC, even if some processors are reserved for resources management.