14-18 October 2013
Amsterdam, Beurs van Berlage
Europe/Amsterdam timezone

DPM - efficient storage in diverse environments

15 Oct 2013, 13:30
Administratiezaal (Amsterdam, Beurs van Berlage)


Amsterdam, Beurs van Berlage

Oral presentation to parallel session Data Stores, Data Bases, and Storage Systems Data Stores, Data Bases, and Storage Systems


Martin Philipp Hellmich (University of Edinburgh (GB))


Recent developments, including low power devices, cluster file systems and cloud storage, represent an explosion in the possibilities for deploying and managing grid storage. In this paper we present how different technologies can be leveraged to build a storage service with differing cost, power, performance, scalability and reliability profiles, using the popular DPM/dmlite storage solution as the enabling technology. The storage manager DPM is designed for these new environments, allowing users to scape up and down as they need it, and optimizing their computing centers energy efficiency and costs. DPM runs on high-performance machines, profiting from multi-core and multi-CPU setups. It supports separating the database from the head node, largely reducing its hard disk requirements. Since version 1.8.6, DPM is released in EPEL and Fedora, simplifying distribution and maintenance, but also supporting the ARM architecture beside i386 and x86_64, allowing it to run the smallest low-power machines such as the raspberry pi or the CuBox. This usage is facilitated by the possibility to scale horizontally using a main database and a distributed memcached-powered namespace cache. Additionally, DPM supports a variety of storage pools in the backend, most importantly HDFS, S3-enabled storage, and cluster file systems, allowing users to fit their DPM installation exactly to their needs. In this paper, we investigate the power-efficiency and total cost of ownership of various DPM configurations. We develop metrics to evaluate the expected performance of a setup both in terms of namespace and disk access considering the overall cost including equipment, power consumptions, or data/storage fees. The setups tested range from the lowest scale using raspberry pies with only 700MHz single cores and a 100Mbps network connections, over conventional multi-core servers to typical virtual machine instances in cloud settings. We evaluate the combinations of different name server setups, for example load-balanced clusters, with different storage setups, from using a classic local configuration to private and public clouds.

Primary author

Martin Philipp Hellmich (University of Edinburgh (GB))


Presentation Materials