Projecting the Performance of Decision Support Workloads on Systems with Smart Storage (SmartSTOR

Recent developments in both hardware and software have made it worthwhile to consider embedding intelligence in storage to handle general purpose processing that can be offloaded from the hosts. In particular, low-cost processing power is now widely available and software can be made robust, secure and mobile. In this paper, we propose a general Smart Storage (SmartSTOR) architecture in which a processing unit that is coupled to one or more disks can be used to perform such offloaded processing. A major part of the paper is devoted to understanding the performance potential of the SmartSTOR architecture for decision support workloads since these workloads are increasingly important commercially and are known to be pushing the limits of current system designs. Our analysis suggests that there is a definite advantage in using fewer but more powerful processors, a result that bolsters the case for sharing a powerful processor among multiple disks. As for software architecture, we find that the offloading of database operations that involve only a single relation to the SmartSTORs is far less promising than the offloading of multiple-relation operations. In general, if embedding intelligence in storage is an inevitable architectural trend, we have to focus on developing parallel software systems that can effectively take advantage of the large number of processing units that will be in the system.