The new S language: a programming environment for data analysis and graphics
The new S language: a programming environment for data analysis and graphics
Quality Engineering Using Robust Design
Quality Engineering Using Robust Design
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Before software systems are shipped, they are tuned to optimize their field performance. This process is called performance tuning. Performance tuning is used to find the best settings for a set of tunable, or changeable, parameters like buffer space, disk file allocation, main memory partition, I/O priority, process scheduling quantum, etc. Examples of performance measures to be optimized are: query or transaction loss, throughput rate, response time, etc. Improperly tuned systems can create field problems even if there are no software faults in the product. Hence, it is important that software systems be tuned for optimal performance before they are delivered. However, optimal performance tuning is quite complex because of: exponentially many alternatives, unknown functional relationships between parameters and performance measures, stochastically fluctuating system performance, and expensive empirical experiments. For these reasons, tuning is typically practiced as an art and depends heavily on the intuitions of experts. In this paper, we examine a method for tuning which is repeatable and produces consistently superior results across many different applications. This method, based upon Robust Experimental Design, has revolutionized design optimization in hardware systems. The methodology consists of conducting a few carefully chosen experiments and using the associated analysis technology to help extract the maximum possible information for performance optimization. Specifically we give some background on statistical experimental design and demonstrate it on an actual software system that provides network database services which had experienced occasional query losses. Focusing on nine carefully chosen parameters, 12 experiments were conducted. This number of experiments is far fewer and consequently far less costly in time and effort than what would be required for collecting the same amount of information by traditional methods. The selection of the experiments took into account ideas from accelerated life testing and ideas from the Robust Experimental Design. Based on the analysis of this data, new settings for the parameters in software system were implemented. All tests done with the new settings have shown that the query loss problem has been totally controlled.