Resource Usage Monitoring using AI

 HELLO GUYS,

The computational demands of modern AI techniques are immense, and as the number of practical applications grow, there will be an increasing burden on shared computing infrastructure. We can envision a forthcoming era of "AI Systems" research where reducing resource consumption, reasoning about transient resource availability, trading off resource consumption for accuracy and managing contention on specialized hardware will become the community's main research focus.

Adaptive workload management was created to more intelligently allocate data resources across workloads either automatically or by providing an alert based on the business’s preference. Machine learning is an integral part of this process, monitoring expected and actual runtimes so that predictions for future workloads can be made and resources can be adjusted accordingly. If it is likely that an issue will occur, adaptive workload management can notify users of the finding or automatically take steps to correct the problem. This is in contrast to previous techniques for allocating resource allocation where a pre-determined limit for workload number or size was set by users. These limits would then need to be monitored and regularly adjusted manually in an attempt to keep at top efficiency. IBM testing has found the better utilization possible with adaptive workload management to result in database performance improvements up to 30 percent.

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