Instant Quality Control of Large Batch Processing Jobs

The most common way to identify success or failure of a job running in a batch-processing mode is by examining a completion code sent by the job to the host operating system. Yet, for a variety of reasons the completion code may inaccurately indicate a successful termination of the job. This article describes a different approach to monitoring the quality of batch processing jobs while in operation. A pattern of behavior is suggested for a program. The pattern reflects ratios of consumption of various hardware resources. The ratios are determined by collecting historical performance variables of the job and analyzing the data by means of statistical methods. Once a pattern is set, the performance variables of every individual run of the program are compared with the precalculated pattern of behavior and if the deviation is beyond certain limits an alarm is triggered. The proposed quality control technique has been tested on real applications, as well as on some artificial programs. The findings suggest that the technique is reliable in that # successfully distinguishes between proper and malfunctioning runs of a program. Keywords’:Quality control, batch processing, computing management ACM Categories: D.4.5., D.4.8, K.6.2 Introduction The rapid proliferation of online computer applications has not eliminated the wide-spread use of batch processing jobs. Batch processing is still the backbone of many computer installations, operating the mass production transaction processing such as payroll, bookkeeping, invoicing, and the like. Yet what assurance does the IS manager have that transaction processing is functioning reliably? An answer to this question is crucial in large batch-processing operations. A large batch-processing application is usually characterized by a long sequence of jobs, where the successful completion of earlier jobs is prerequisite to the beginning of subsequent ones. Failure to recognize a malfunction could result in high costs later, when a long series of dependent jobs must be rerun and files containing wrong data must be corrected. Many organizations maintain a large application base of batch jobs and there is extreme pressure to manage these effectively and to keep costs down. Many batch jobs are run at night with minimal human supervision. Much effort is, therefore, invested in quality assurance of batch-processing programs. Basically, the effort is directed along two avenues: quality assurance during the development cycle and quality control while the job is in operation. The first category of quality assurance relates to various software engineering methodologies dealing with system analysis and design, programming, and testing [6] (e.g., structured analysis and design, structured programming, structured walkthrough, chief programming team, and documentation). Practices of managing the development process of IS may also be included in that category. Although these methodologies and practices undoubtedly improve the quality of the end product (i.e., the program), they cannot guarantee that a program transferred from development to operation is foolproof. For example, a program that processes three inputs, each composed of only 16 bits, and consumes 100 milliseconds to produce an output record, will require about 900 years of CPU time to check all the possible input combinations. The second category, quality control, relates to manual and automated measures imposed MIS Quarterly~September 1987 313