Abstract. The requirements of wide-area distributed database systems differ dramatically from those of local-area network systems. In a wide-area network (WAN) configuration, individual sites usually report to different system administrators, have different access and charging algorithms, install site-specific data type extensions, and have different constraints on servicing remote requests. Typical of the last point are production transaction environments, which are fully engaged during normal business hours, and cannot take on additional load. Finally, there may be many sites participating in a WAN distributed DBMS.In this world, a single program performing global query optimization using a cost-based optimizer will not work well. Cost-based optimization does not respond well to sitespecific type extension, access constraints, charging algorithms, and time-of-day constraints. Furthermore, traditional cost-based distributed optimizers do not scale well to a large number of possible processing sites. Since traditional distributed DBMSs have all used cost-based optimizers, they are not appropriate in a WAN environment, and a new architecture is required.We have proposed and implemented an economic paradigm as the solution to these issues in a new distributed DBMS called Mariposa. In this paper, we present the architecture and implementation of Mariposa and discuss early feedback on its operating characteristics.

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Configuring redundant disk arrays is a black art. To configure an array properly, a system administrator must understand the details of both the array and the workload it will support. Incorrect understanding of either, or changes in the workload over time, can lead to poor performance. We present a solution to this problem: a two-level storage hierarchy implemented inside a single disk-array controller. In the upper level of this hierarchy, two copies of active data are stored to provide full redundancy and excellent performance. In the lower level, RAID 5 parity protection is used to provide excellent storage cost for inactive data, at somewhat lower performance. The technology we describe in this article, know as HP AutoRAID, automatically and transparently manages migration of data blocks between these two levels as access patterns change. The result is a fully redundant storage system that is extremely easy to use, is suitable for a wide variety of workloads, is largely insensitive to dynamic workload changes, and performs much better than disk arrays with comparable numbers of spindles and much larger amounts of front-end RAM cache. Because the implementation of the HP AutoRAID technology is almost entirely in software, the additional hardware cost for these benefits is very small. We describe the HP AutoRAID technology in detail, provide performance data for an embodiment of it in a storage array, and summarize the results of simulation studies used to choose algorithms implemented in the array.

The Mariposa distributed data manager uses an economic model for managing the allocation of both storage objects and queries to servers. In this paper, we present extensions to the economic model which support replica management, as well as our mechanisms for propagating updates among replicas. We show h o w our replica control mechanism can be used to provide consistent, although potentially stale, views of data across many machines without expensive per-transaction synchronization. We present a rule-based conict resolution mechanism, which can be used to enhance traditional time-stamp serialization. We discuss the eects of our replica system on query processing for both read-only and read-write queries. We further demonstrate how the replication model and mechanisms naturally support name service in Mariposa.

As chip multiprocessor (CMP) has become the mainstream in processor architectures, Intel and AMD have introduced their dual-core processors. In this paper, performance measurement on an Intel Core 2 Duo, an Intel Pentium D and an AMD Athlon 64 Â 2 processor are reported. According to the design specifications, key derivations exist in the critical memory hierarchy architecture among these dualcore processors. In addition to the overall execution time and throughput measurement using both multi-program-med and multithreaded workloads, this paper provides detailed analysis on the memory hierarchy performance and on the performance scalability between single and dual cores. Our results indicate that for better performance and scalability, it is important to have (1) fast cacheto-cache communication, (2) large L2 or shared capacity, (3) fast L2 to core latency, and (4) fair cache resource sharing. Three dual-core processors that we studied have shown benefits of some of these factors, but not all of them. Core 2 Duo has the best performance for most of the workloads because of its microarchitecture features such as the shared L2 cache. Pentium D shows the worst performance in many aspects due to its technology-remap of Pentium 4 without taking the advantage of on-chip communication.