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Monitoring and Administration Needed for CICS Effectiveness


A key task is identification of high utilizations, because as device or path usage increases linearly, service time increases exponentially. A queue is a waiting line, and queuing theory examines the effect of wait time on system throughput and response, plus cost tradeoffs of waiting versus resources to reduce wait time. Anyone who’s driven the Kennedy Expressway into Chicago on a Monday morning has endured the queue phenomenon, much to their dismay, and the very same effect applies to computer resource usage. As utilization increases, wait time, queue length and congestion increase; the following determinants control how values change based on activity:

  • Arrival rate and size: The frequency in which units of work arrive and the size of the unit of work
  • Servers and server size:. These provide exits from a queue and the speed with which they can expedite the exit
  • Distributions: The pattern of entrances into and exits from queues
  • Arrival rates: The frequency and pattern at which units enter queues
  • Service rates depend on server speed, which describe the time from when a unit leaves a queue until it moves to the next path segment
  • A path may consist of one or more queues and one or more servers
  • Two types of queues with different characteristics exist. A single-server queue processes one unit of work at a time and all units behind it wait, while a multi-server queue can process two or more units concurrently. When any server completes a unit, the next unit in the queue is accepted.
  • The math to calculate throughput of a single-server is different—and simpler—than the math used to calculate multi-server throughput

Believe it or not, thanks to an Operations Research degree, years ago I crunched queuing numbers with a calculator while performing tuning reviews, but these days numerous calculators are on the internet. More importantly, many tuning tools include queuing logic for generating performance projections. While for the most part it’s useful to let the tool do the work, it’s a worthwhile exercise to do an Internet search on “queuing theory calculator,” several appear. It’s fascinating and amazingly informative to see how a small arrival rate reduction can result in major performance improvements and vice versa, how sometimes minor service time improvements make major performance improvements and many variations that often aren’t so obvious.

Queuing theory’s vital for performance tuning. It illustrates the nature of information processing and the complex path a transaction takes to accomplish its work. The lessons learned from it are sometimes almost counterintuitive. For example, increasing servers probably won’t cut response time much if service time is relatively high, yet a slight reduction in arrival rate may significantly improve response time when utilization is high. Queuing theory points the way to improved performance.

It Ain’t Easy

Performance tuning is a highly complex process. It’s not just a matter of product knowledge, but also Operations Research principles such as queuing theory, modeling techniques and simulations. Just as important is the acquisition and usage of performance tools, diligent monitoring and reporting, and hard-won experience. Maybe a little luck, too.

Tuning takes patience, persistence, studious attention to numbers and creative intuition, because with all the working parts and interdependencies, problems are rife and answers are conditional solutions that keep shifting and changing. While a CICS performance issue may be due to internal processing and parameter settings, outside influences are bereft, and frequently the cause of degradation. The mixture of work that goes on within a mainframe, LINUX/AIX/UNIX-based or Windows/TXSeries system is varied and complex, and CICS tuning is only part of the solution in well-performing, interactive online processing.

Jim Schesvold is a technical editor for IBM Systems Magazine. Jim can be reached at

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