Optimizing cloud environments using AIOps

The two major benefits of AIOps are, first, the speed and accuracy in detecting anomalies and responding to them without human intervention. Second, AIOps can be used for capacity optimization. Most cloud providers offer some form of scale-out/-up mechanism driven by metrics, already available natively within the platform. AIOps can optimize this scaling since it knows what thresholds are required to do this, whereas the cloud provider requires engineers to define and hardcode it.

Since the system is learning, it can help in predicting when and what resources are needed. The following diagram shows the evaluation of operations, from descriptive to prescriptive. Most monitoring tools are descriptive, whereas AIOps is predictive:

Figure 19.1: Evolution of monitoring to AIOps

Monitoring simply registers what’s happening. With log analytics, companies can set a diagnosis of events and take remediation actions based on...