Repositori URV

Identificador: imarina:6389373

Handle: https://hdl.handle.net/20.500.11797/imarina6389373

Autors:
Gracia-Tinedo RZou CSanchez-Artigas MGarcia-Lopez P

Resum:
© 1990-2012 IEEE. In many online storage services, end-users mainly interact with the system via 'fat' storage clients that integrate complex functionality. This means that to obtain a complete performance evaluation of one of such systems we may need to generate workloads on the client side that reproduce the behavior of real users. Unfortunately, this remains as an open research challenge today. We present BenchBox: A distributed performance evaluation framework for fat-client storage systems. On the one hand, BenchBox can generate workloads directly in storage clients that mimic users exhibiting a certain behavior, namely, user stereotypes. To this end, the framework enables to plug-in workload models and feed them with compact recipes that capture the behavior of user stereotypes (e.g., storage activity, type of file contents, data sharing links). On the other hand, BenchBox provides researchers with management and monitoring facilities to deploy experiments and analyze the performance of groups of storage clients. To demonstrate our framework, we equipped BenchBox with a 2-layer workload model that reproduces both the activity-e.g., types of operations, frequency-and data-e.g., file sizes, data types-of users in a Personal Cloud. We used this model to generate workloads based on user stereotypes that we identified in real traces (UbuntuOne). Our experiments with public providers show how distinct types of users impact on the performance and efficiency of Personal Clouds, which may guide their optimization.