Improving Data Availability for Better Access Performance: A Study on Caching Scientific Data on Distributed Desktop Workstations

Client-side data caching serves as an excellent mechanism to store and analyze the rapidly growing scientific data, motivating distributed, client-side caches built from unreliable desktop storage contributions to store and access large scientific data. They offer several desirable properties, such as performance impedance matching, improved space utilization, and high parallel I/O bandwidth. In this context, we are faced with two key challenges: (1) the finite amount of contributed cache space is stretched by the ever increasing scientific dataset sizes and (2) the transient nature of volunteered storage nodes impacts data availability. In this article, we address these challenges by exploiting the existence of external, primary copies of datasets. We propose a novel combination of prefix caching, collective download, and remote partial data recovery (RPDR), to deal with optimal cache space consumption and storage node volatility. Our evaluation, performed on our FreeLoader prototype, indicates that prefix caching can significantly improve the cache hit rate and partial data recovery is better than (or comparable to) many persistent-data availability techniques.