Reduce the purchasing and operational costs of the IT datacenter infrastructure while protecting service levels, by increasing visibility and control, right-sizing hardware, and rebalancing workloads and applications.
By assessing exactly what is necessary for your SAN and z/OS environment to provide adequate service, you ensure that you are spending only on options that truly support your business needs, without endangering service levels. By understanding exactly what is going on inside your infrastructure, purchases can often be postponed by rebalancing on existing hardware. When it turns out that a purchase is necessary, being in control makes it possible to take informed decisions without having to rely on hardware vendor’s statements.
IntelliMagic Vision’s continual, daily detection of configuration errors, imbalances and bottlenecks in your SAN and z/OS datacenters prevents premature hardware investments. It also prevents pricey emergency purchases while at the same time protecting safety margins much better than by oversizing. IntelliMagic identifies underutilized components and opportunities for cost reduction based on where applications could run.
For new storage purchasing projects, IntelliMagic can act as your independent advisor. Using IntelliMagic Direction, a software product that is based on 25 years of experience with predictive modeling of storage environments, IntelliMagic can help you pick the best value for money given your service level requirements, instead of having to rely on vendor statements or artificial benchmark data.
For z/OS mainframe environments, IntelliMagic has a brand new services offering that identifies large software cost reduction opportunities.
Health Insights and Change Detection
To help enhance the value you can derive from that SMF data, IntelliMagic Vision provides two complementary types of automated anomaly detection: health insights and change detection.
CICS Max Tasks: Automated Change Detection Use Case 7
Change Detection and Health Assessments (based on pre-defined best practice conditions) play complementary roles, but there can be scenarios where they both flag issues. This use case with CICS Max Tasks is one such example.
Batch Job Elapsed Time: Automated Change Detection Use Case 6
Elapsed and CPU times can be of interest for batch jobs, especially those on the critical path for key business job cycles. This use case reflects an abnormally long run time for a daily batch job, and illustrates how potential causes might be investigated.