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. 1

The requirements for storage space and computational power of largescale applications are increasing rapidly. Clusters seem to be the most attractive architecture for such applications, due to their low costs and high scalability. On the other hand, smart disk systems, with their large storage capacities and growing computational power are becoming increasingly popular. In this work, we compare the performance of these architectures with a single host-based system using representative queries from the Decision Support System (DSS) databases. We show how to implement individual database operations in the smart disk system and also show how to optimize the execution of the whole query by bundling frequently occurring operations together and executing the bundle in a single invocation. Besides decreasing the overall execution time, operation bundling also offers an easy-to-program and easy-to-use interface to access the data on smart disks. We also present a protocol for minimizing the communication time in the smart disk based system. To measure the response times, we have developed the DBsim, an accurate simulator which can simulate the database operations for the single host-based, cluster-based and smart disk based systems. Using this simulator, we illustrate that the smart disk architecture offers substantial benefits in terms of overall query execution times of the TPC-D benchmark suite. In particular, the average response time of the smart disk architecture for the representative queries from the TPC-D benchmark in our base configuration is 71 % smaller than the response time on the single host-based system and 4:2 % smaller than the response time on the fastest cluster architecture. We also demonstrate the effectiveness of the operation bundling. 1.

this paper, we present a detailed quantitative evaluation of a smart disk based architecture. To achieve this, we compare the performances of a smart disk system, two types of cluster systems and a single host system for whole database queries. The main contributions of this paper are as follows: ffl We present how a whole database query can be executed on a smart disk system. ffl We present and evaluate a method called operation bundling for reducing the execution time of the database queries in smart disk architecture

Smart disk systems with large storage capacities and growing computational power are becoming increasingly attractive. The idea is to perform parallel and filtering-type of data intensive computations on disks, close to data, thereby offloading the host processor and increasing the aggregate system power. In this

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.

Disk I/O is increasingly the performance bottleneck in computer systems despite rapidly increasing disk data transfer rates. In this paper, we propose Automatic Locality-Improving Storage (ALIS), an introspective storage system that automatically reorganizes selected disk blocks based on the dynamic reference stream to increase effective storage performance. ALIS is based on the observations that sequential data fetch is far more efficient than random access, that improving seek distances produces only marginal performance improvements, and that the increasingly powerful processors and large memories in storage systems have ample capacity to reorganize the data layout and redirect the accesses so as to take advantage of rapid sequential data transfer. Using trace-driven simulation with a large set of real workloads, we demonstrate that ALIS considerably outperforms prior techniques, improving the average read performance by up to 50% for server workloads and by about 15% for personal computer workloads. We also show that the performance improvement persists as disk technology evolves. Since disk performance in practice is increasing by only about 8% per year [18], the benefit of ALIS may correspond to as much as several years of technological progress.