Method for planning capacity of multiple machines aiming at OLTP application

Abstract

The invention discloses a method which aims at implementing capacity planning over different machine types to OLTP applications and includes: executing flexibility evaluation of the OLTP application to determine whether the OLTP application has flexibility problem; if the method determines that the OLTP application does not have flexibility problem, a performance comparison test is executed amongdifferent machine types to acquire the running performances of the different machine types; computing performance ratio among the different machine types according to the acquired running performances of the different machine types; acquiring the performance values of various machine types in different series, which is necessarily based on the performance ratios among different machine types acquired from the performance comparison test, and the acquired performance values are input into a parameter database for capacity planning, consequently a new parameter database for capacity planning isformed, and performance comparison and performance prediction can be implemented over different machine types; furthermore, the new parameter database for capacity planning can be used by a modeling method for capacity planning and a capacity planning tool to implement capacity planning over different machine types which aims at the OLTP application.

Description

technical field [0001] The invention generally relates to host capacity planning for IT system planning and design, in particular to a cross-model capacity planning method based on performance comparison test for specific OLTP (online transaction processing) applications. Background technique [0002] Capacity planning refers to the process of matching the system load to the most suitable server hardware required to support this load in order to ensure that the system can work efficiently in the future, and is used to guide capacity design and investment to ensure the rationality of IT expenses. Capacity planning requires corresponding technical means to predict the service level that the system can provide under the assumed hardware configuration when the load or number of users is increased. This is often achieved by establishing a capacity analysis model with the help of capacity planning tools. [0003] In the process of IT system planning and design, it is necessary to ...

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Problems solved by technology

[0004] 1. Different types (manufacturers) of models have great differences in motherboard structure, CPU design, and IO design, and the performance of running different applications will change, which leads to the performance gap between the two machines in different benchmark tests There are often large changes in the indicators;

[0005] 2. Various published benchmark indicators are obtained after careful and sufficient performance optimization by the original manufacturer, and the existing deployed systems often cannot meet the indicators;

[0006] 3. In the process of actual capacity measurement, the specific OLTP application’s definition of transactions or tasks is inconsistent with the definition in the benchmark test, and the proportional relationship is not clear. It is only estimated based on experience, and the error is large. For example, the user’s OLTP system. A transaction is inconsistent with the benchmark transaction in the TPC-C test;

[0007] 4. Some manufacturers did not participate in all the benchmark tests, which also caused great difficulties for model selection reference. For example, SUN has not participated in the TPC-C test in recent years;

[0008] 5. The mainstream capacity planning tools (such as TeamQuest Model) can better complete the performance prediction of machines of the same type and different grades through the capacity planning parameter library provided by the manufacturer, but based on the same reason as point 1, they cannot provide accurate cross-machine Type of performance prediction requires the user to input the performance ratio of different types of machines

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