Intelligence for the Modern
IBM i

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Identifying early warning signs in IBM i systems is crucial to preventing major outages. Key indicators include increasing response times, unusual batch job behavior, and rising lock contention. I/O bottlenecks are also significant, affecting overall performance by hindering data read/write capabilities. Changes in workload relationships and anomalies in affinity scores suggest misaligned resources. The integration of AI-driven analysis enhances detection of subtle patterns signaling potential crises. As system complexity grows, regular architecture evaluations and process simplifications are essential. Recognizing these signs provides a strategic advantage, ensuring business continuity and enhancing operational efficiency, while safeguarding the organization's reputation.

It's official. After months of R&D, we are bringing true observability to the AS/400 ecosystem. Discover how i-Rays seamlessly integrates with Dynatrace to finally make your IBM i a first-class citizen of the modern IT stack. No more black boxes.

iBM i can run for years "without issues"... until one day everything looks like an issue at once. This post explains why threshold-based monitoring often misses the real causes of IBM i incidents - and how a behavioural approach (baseline + context) reduces alert noise and speeds up root-cause diagnosis.

In today's fast-paced digital world, IT teams are under constant pressure to keep their IBM i systems running smoothly and prevent disruptions before they impact business operations. Two concepts- observability and monitoring-are often mentioned together, but they are not the same thing.

iBM i can run for years "without issues"... until one day everything looks like an issue at once. This post explains why threshold-based monitoring often misses the real causes of IBM i incidents - and how a behavioural approach (baseline + context) reduces alert noise and speeds up root-cause diagnosis.

iBM i can run for years "without issues"... until one day everything looks like an issue at once. This post explains why threshold-based monitoring often misses the real causes of IBM i incidents - and how a behavioural approach (baseline + context) reduces alert noise and speeds up root-cause diagnosis.

iBM i can run for years "without issues"... until one day everything looks like an issue at once. This post explains why threshold-based monitoring often misses the real causes of IBM i incidents - and how a behavioural approach (baseline + context) reduces alert noise and speeds up root-cause diagnosis.