Minimizing downtime has been a long-standing priority for IT leaders. To help achieve this goal, they have turned to artificial intelligence for IT operations (AIOps) solutions to support their enterprises. However, choosing the right AIOps toolset has become more complex as multiple options have emerged, each having varying features.
It isn’t uncommon today for an organization to rely on eight to ten separate monitoring tools. While these tools can gather a lot of data, they often operate in silos rendering them inefficient. IT teams are continuously plagued with various alerts from each system which contribute to alarm fatigue, and most systems don’t provide a clear correlation or root cause analyses. So, the cycle repeats without ever solving the causal issues.
Replacing these tools with AIOps has allowed teams to automate resolution and be more proactive. However, the key to zero downtime is looking for an AIOps tool that leverages preventive healing solutions. This feature measures the effect of workload on system behavior. It can detect when application workload is not following seasonality trends (time of day, day of the month, month of the year, etc.) or is anomalous concerning the number of inbound requests of a service. With this information, IT teams can be alerted if there is concern that the trend might affect service behavior. When models are built to measure and learn this workload-behavior correlation, teams can be warned of an impending issue in an application ahead of time and correct it before it occurs.
When evaluating your next AIOps tool, here are some key questions enterprise decision-makers should consider:
Am I ready to transition to a zero-downtime enterprise?
Ensuring high availability is critical to business success — now more than ever before. Traditionally, to keep a business continuously available required a substantial investment in time and resources to ensure the IT infrastructure was up and running 24x7x365. Shifting to a preventive healing solution can allow an enterprise to start moving toward a zero-downtime / negative mean time to resolve (MTTR) issue resolution paradigm. This is possible because the tools can flag and automatically remediate imminent issues by leveraging various techniques, including:
- optimizing workload on the fly to reduce the load on the underlying infrastructure;
- augmenting infrastructure in a cloud / microservice / containerized set-up; or
- initiating service-centric mechanisms to heal based on time-synchronized contextual data.
These healing mechanisms can be seamlessly integrated with the underlying IT service management (ITSM) orchestration workflows through representational state transfer (REST) interfaces. This allows enterprises to gradually move from minimal to zero downtime, thereby reducing the costs of running IT operations management (ITOM), maximizing customer delight, and keeping operations centers lean.
Can I optimize infrastructure investments while scaling intelligently and effectively?
Making intelligent scaling choices is an essential ingredient to business success. To plan for future workloads, some AIOps solutions can correlate projected workload trends to corresponding infrastructure requirements. In doing so, it’s vital to highlight under-provisioned resources that need to be scaled up and overprovisioned ones that are a drain on business spend and need to be scaled back, which are even more critical in cloud deployments. Running a what-if analysis on projected workload to examine corresponding capacity forecasts is crucial in this process.
Choosing an AIOps tool with preventive healing software can help enterprises make zero downtime a reality. By switching to advanced alerting mechanisms, coupled with contextual data on the state of the system at the time of the anomaly, the IT team is empowered to prevent issues before they cause an interruption to customers and downtime to the organization. 
