How Fast Can One Scale Down a Distributed File System?

For efficient Big Data processing, efficient resource utilization becomes a major concern as large-scale computing infrastructures such as supercomputers or clouds keep growing in size. Naturally, energy and cost savings can be obtained by reducing idle resources. Malleability, which is the possibility for resource managers to dynamically increase or reduce the resources of jobs, appears as a promising means to progress towards this goal. However, state-of-the-art parallel and distributed file systems have not been designed with malleability in mind. This is mainly due to the supposedly high cost of storage decommission, which is considered to involve expensive data transfers. Nevertheless, as network and storage technologies evolve, old assumptions on potential bottlenecks can be revisited. In this study, we evaluate the viability of malleability as a design principle for a distributed file system. We specifically model the duration of the decommission operation, for which we obtain a theoretical lower bound. Then we consider HDFS as a use case and we show that our model can explain the measured decommission times. The existing decommission mechanism of HDFS is good when the network is the bottleneck, but could be accelerated by up to a factor 3 when the storage is the limiting factor. With the highlights provided by our model, we suggest improvements to speed up decommission in HDFS and we discuss open perspectives for the design of efficient malleable distributed file systems.