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z/OS performance and cost analysis

From your operational data we measure where load is created, where it becomes cost-relevant and what can be capped without losing throughput — and we tell you what it costs if you do.

Starting point

Capacity is measured. Cost is calculated. These are not the same thing.

Most z/OS sites know precisely how busy their machines are. Far less often is it known which share of that utilisation actually enters the invoice — and which workload simply happens to run at the wrong time.

Under classic sub-capacity pricing the highest rolling 4-hour average of a month is billed. Anything outside that window matters operationally but is cost-neutral. That distinction is the core of our analysis.

Important in 2026: under Tailored Fit Pricing the logic reverses — actual consumption across the year is billed rather than the peak. A what-if calculation that does not reflect the customer's pricing model can be wrong by orders of magnitude.
oldest interval drops out new interval enters 16 × 15 minutes = 4 hours Only two values change the average — which is why it reacts slowly and predictably.
The rolling 4-hour average per partition: a sliding window of 16 intervals. With every new interval the oldest drops out. Only sustained load lifts the average noticeably.

Areas of investigation

What we look at

Six areas that contain potential or risk in almost every environment.

Capping & capacity limits

Capacity limits per partition, group limits, distribution via the weights, share of capped intervals, effect of soft versus hard capping.

WLM policy and goal attainment

Goal attainment per service class, execution velocity, response time distribution, use of the priority levels, homogeneity of workload within classes.

Contributors at job level

Rankings by hour and interval, elapsed versus CPU time, I/O profiles — down to the single job that triggers a capping event.

Specialty engines

Utilisation of specialty engines, crossover onto general purpose processors, view per service class — the basis for deciding whether additional capacity is economically justified.

Dispatching and hypervisor

Distribution and parking of logical processors, operating system versus partition utilisation, capture ratio, hypervisor overhead, consistency of weights.

Batch window and scheduling

What runs when, and how much of it falls into the cost-relevant window? Frequently the most effective lever, because it requires no technical change at all.

Analysis

More than 150 analyses — and an assessment with them

We work with our own analysis tool. It does not run alongside the live system; it reads operational data that has already been written, condenses it and provides time series that can be compared across weeks and months.

Resolvable level by level

From the machine via the partition and the service class down to the individual job or transaction — without resetting the time range.

Explained, not just displayed

Below every analysis is a note on what it shows and what to watch for. That keeps values interpretable for people who do not see them daily.

Recurring reports

Any set of analyses can be bundled and generated as a PDF on a schedule — to verify the effect after a change.

What you end up holding

  • An assessed sequence of results — every analysis with professional interpretation, not just the picture
  • Concrete threshold proposals for capacity limits, group limits and weights
  • Named trade-offs — which workload would be delayed, when, and under what condition that is acceptable
  • A staged implementation plan rather than one large switch-over
  • WLM findings: goals set too weakly or unattainably, unused priority levels, inhomogeneous service classes
  • A results workshop including a fundamentals section, so everyone speaks the same language
Excerpt — how a finding is derived
# group level Group limit reached → capping active, 2 events └─ not the partition limit — the group limit applies # partition level Priority distribution → trigger is the lowest level └─ service class batch, not the online workload # contributor Job analysis → a single job across seven hours └─ question: can it start later? # effect Guarantee today 47 MSU · effectively used 65 MSU Proposal 35 MSU · effectively usable 45 MSU

Pricing models

Your contract determines which optimisation works at all

We model what you actually pay for — otherwise the calculation misses reality.

Sub-capacity / R4HA

The highest rolling 4-hour average per partition and product in a month is billed. Levers: peak shifting, capping, weights.

Tailored Fit Pricing

Under the Software Consumption Solution, actual consumption across the year is what counts. The economically decisive moment is the baseline negotiation — optimisation belongs before it, not after.

Full capacity

Under the Enterprise Capacity Solution there is no reporting obligation; the lever shifts from peak management to capacity sizing and workload placement.

We are not a licence reseller and do not negotiate contracts. We provide the measurement basis you or your licensing partner can negotiate with.

FAQ

Questions about performance and cost analysis

What data do you need from us?

The operational data your system writes anyway, over a contiguous period of ideally two months, plus the active WLM service definition. Which components are needed in detail depends on the question and is agreed in the initial conversation. Preparation requires neither an agent nor an installation in the running system.

Does data have to leave our premises?

No. The analysis tool can run entirely inside your data centre. Alternatively we work from an anonymised extract. What is practical for your environment is agreed up front and recorded in writing.

How long does an analysis take?

Effort depends on scope and the number of partitions. Data collection runs at your site across the agreed observation period; analysis, assessment and preparation follow and lead into a results workshop. We give a reliable frame after the initial conversation.

How much can be saved?

We can only answer that responsibly once we have seen the data — and we deliberately avoid blanket percentages. What matters is the ratio of average to peak load in your environment and whether your peaks sit in shiftable time windows. Both are directly measurable from your data.

Does this replace our monitoring?

No. Monitoring answers “what is happening right now”. We answer “what happened structurally over weeks, what did it cost and what follows from it”. The two complement each other.

What does your curve look like?

Send us your question — we will tell you which data answers it and how we would proceed.