Predict IT Performance Degradation Before It Occurs
symptoms enables Klaxon to detect problems an hour or more in advance of critical events. Once alerted, Klaxon also provides tools to determine the source of performance degradation, enabling targeted notification of only those technical resources relevant to the problem at hand.
DataMi's solution perspective is that smart IT management in the present requires prediction based on past, present, and future performance models. Our solution encompasses:
Monitoring Log File Aggregation - real-time aggregation of disparate IT monitoring files for a unified view of all mission-critical systems. Point-in-time linkage of performance parameters from all systems is the most important part of this, enabling a comprehensive view of what's happening with all systems at any given point in time.
Utilization of Past, Present, and Future Performance - We ensure robust prediction by tracking , the currently evolving situation, for signs of poor IT performance in the future, and a hybrid / model to incorporate past predictions of what should be happening now. Concurrence across all three of these perspectives provides a level of certainty unparalleled by any other ITOPs solution.
Problem Source Identification - Once alerted to a potential issue, IT operators need to know who to notify for root cause analysis and repair. Klaxon's advanced time series tools make it easy to find the sustained causes of performance degradation over time informing ITOPS of which support teams to notify and where the likely
issue is for them to fix.
Are you running mission-critical applications on a cloud platform? Do you have clients and end users complaining of slow performance when there have been no HW or SW failures? DataMi has predictive models that both alert you to upcoming incidents and tell you where the source of trouble is coming from.
Klaxon is DataMi's causal modeling solution designed to track and predict when, where and why IT performance degradation has, or soon will, occur. Klaxon uses the performance output from commercially available SW monitoring systems and can simultaneously operate multiple models, each tuned to a different operational cause. This focus on causes rather than
IT performance degradation typically develops
over time, and is often the result of correctable
resource contention. Lower (often SAN) level
obstructions can block or delay higher level
(Cluster or ESX Host) functions, overwhelming
dynamic resource allocation and blocking
resource availability at the VM level.
Real-Time Flow Through Modeling - recalculation of performance simulation models at every reporting interval, as SW performance parameters are being delivered. This enables continually updated IT status assessment for more reliable alerts.
Machine Learning for Model Tuning - for implementations with feedback loops, DataMi's technical approach supports automatic re-calibration of IT performance models based on the consequences of issued and missed alerts.