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This paper will discuss some of the issues involved in building an Expert System that embodies tuning rules for IBM's MVS/XA operating system. To understand the components of an Expert System and their functions, PROLOG on an IBM PC (Turbo-PROLOG from Borland International) was chosen as the development environment. The paper will begin by defining the key concepts about Expert Systems, Knowledge Engineering, and Knowledge Acquisition. The reader will be given a brief overview of PROLOG, from which we can explain how an inference mechanism was developed. Finally, the paper will describe the Expert System that was developed, and additionally will provide a set of key issues that should be addressed in the future. It is our overall objective to provide new insight into the application of AI to CPE.

Historically, workload characterization, and cluster analysis in particular, has been a proven technique when applied to performance evaluation / capacity planning studies. Given the problem of constructing a synthetic workload that represents a production workload, our goal is to use this technique to identify a concise , yet accurate set of work units that will compose the workload. For IMS, these work units are transactions. Yet the selection of transactions must be done with care; for an additional goal must be to identify a concise , yet accurate set of databases that are required by the transactions. This paper will review clustering techniques, and apply them to drive the transaction selection process. An algorithm is also presented that identifies the technique behind database selection. A case study follows that illustrates the implementation of the methodology.