Method and apparatus for online sample interval determination

Abstract

In one embodiment, functional system elements are added to an autonomic manager to enable automatic online sample interval selection. In another embodiment, a method for determining the sample interval by continually characterizing the system workload behavior includes monitoring the system data and analyzing the degree to which the workload is stationary. This makes the online optimization method less sensitive to system noise and capable of being adapted to handle different workloads. The effectiveness of the autonomic optimizer is thereby improved, making it easier to manage a wide range of systems.

Description

BACKGROUND [0001] The present invention relates generally to computing systems, and relates more particularly to performance and systems management of computing systems. Specifically, the invention is a method and apparatus for online determination of sample intervals for optimization and control operations in a dynamic, on-demand computing environment. [0002]FIG. 1 is a block diagram illustrating a typical data processing system 10. The data processing system 10 comprises a database server 100 which serves one or more database clients 150. The database server 100 includes a plurality of memory pools 121-125 that is adapted to cache data in a plurality of storage media 111-119. Database agents 101-109 access copies of storage media data through the memory pools 121 to 125 in order to serve the clients 150. [0003] Central to the performance of the data processing system 10 is the management of the memory pools 121-125. Increasing the size of a memory pool 121-125 can dramatically red...

AI Technical Summary

Benefits of technology

[0007] In one embodiment, the present invention is a system for online determination of sample intervals for dynamic (i.e., non-stationary) workloads. In one embodiment, functional system elements are added to an autonomic manager to enable automatic online sample interval selection. In another embodiment, a method for determining the sample interval by continually characterizing the system workload behavior includes monitoring the system data and analyzing the degree to which the workload is stationary. This makes the online optimization method less sensitive to system noise and capable of being adapted to handle different workloads. The effectiveness of the autonomic optimizer is thereby improved, making it easier to manage a wide range of systems.

Problems solved by technology

Due to the stochastic and dynamic nature of computing systems, the size of these sample intervals can be critical.

For example, too small a sample interval may yield an insufficient collection of samples, and significant measurement noise may be generated during optimization, resulting in controller-introduced oscillation.

On the other hand, too large a sample interval may reduce the optimization responsiveness as measured by time-response characteristics, such as system settling time.

A drawback of conventional systems for determining sample intervals, such as the benefit reporter and memory tuner system discussed above, is that the determinations tend to be based on static workloads.

However, in a dynamic, on-demand environment, the workload characteristics and system configurations change drastically with time, and statically derived intervals may therefore yield less than optimal results.

Images