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The TPC-D benchmark was designed to test the performance of database systems on decision-support queries Decision-support systems are becoming increasingly important today The TPC-A, TPC-B, and TPC-C benchmarks measure performance on transaction-processing workloads, and should not be used as a measure of performance on decision-support queries The D in TPC-D stands for decision support The TPC-D benchmark schema models a sales/distribution application, with parts, suppliers, customers, and orders, along with some auxiliary information The sizes of the relations are de ned as a ratio, and database size is the total size of all the relations, expressed in gigabytes TPC-D at scale factor 1 represents the TPC-D benchmark on a 1-gigabyte database, while scale factor 10 represents a 10-gigabyte database The benchmark workload consists of a set of 17 SQL queries modeling common tasks executed on decision-support systems Some of the queries make use of complex SQL features, such as aggregation and nested queries The benchmark s users soon realized that the various TPC-D queries could be signi cantly speeded up by using materialized views and other redundant information There are applications, such as periodic reporting tasks, where the queries are known in advance and materialized view can be carefully selected to speed up the queries It is necessary, however, to account for the overhead of maintaining materalized views The TPC-R benchmark (where R stands for reporting) is a re nement of the TPC-D benchmark The schema is the same, but there are 22 queries, of which 16 are from TPC-D In addition, there are two updates, a set of inserts and a set of deletes The database running the benchmark is permitted to use materialized views and other redundant information In contrast the TPC-H benchmark (where H represents ad hoc) uses the same schema and workload as TPC-R but prohibits materialized views and other redundant information, and permits indices only on primary and foreign keys This benchmark models ad hoc querying where the queries are not known beforehand, so it is not possible to create appropriate materialized views ahead of time Both TPC-H and TPC-R measure performance in this way: The power test runs the queries and updates one at a time sequentially, and 3600 seconds divided by geometric mean of the execution times of the queries (in seconds) gives a measure of queries per hour The throughput test runs multiple streams in parallel, with each stream executing all 22 queries There is also a parallel update stream Here the total time for the entire run is used to compute the number of queries per hour The composite query per hour metric, which is the overall metric, is then obtained as the square root of the the product of the power and throughput metrics A composite price/performance metric is de ned by dividing the system price by the composite metric The TPC-W Web commerce benchmark is an end-to-end benchmark that models Web sites having static content (primarily images) and dynamic content generated from a database Caching of dynamic content is speci cally permitted, since it is very useful for speeding up Web sites The benchmark models an electronic bookstore, and like other TPC benchmarks, provides for different scale factors The primary performance metrics are Web interactions per second (WIPS) and price per WIPS.

In the preceding example, the following translation policies are configured: When traffic goes across the site-to-site VPN tunnel to the Corporate office, it should not be translated: the access-list and nat (inside) 0 commands implement this policy When traffic goes from the SOHO to the Internet locations, it will be translated using PAT: the nat (inside) 1 and global (outside) 1 commands implement this policy

The McGraw Hill Companies, 2001

SOHO VPN (NAT 0)

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Internet 10100100/24 Non-VPN 101000/16