AIOps is gearing up to become the next big thing in IT management. It’s the convergence of AI and traditional IT operations. Like all other domains, AI is going to have a significant impact on system operations and administration. When the power of AI is applied to operations, it will redefine the way infrastructure is managed.
IT infrastructure generates a lot of data. From the temperature of the chassis to the latency rate of an API call, it is possible to acquire data from disparate layers of the stack. When aggregated, normalized, and analyzed, this data becomes a rich source to derive insights.
Here are five use cases of AIOps:
1) Capacity Planning
Even though capacity planning has become dynamic with cloud infrastructure, architects still find it hard to map workloads to the right servers and VM configurations.
Mainstream cloud providers such as AWS, Azure, and Google have dozens of configurations for running VMs. Infrastructure architects will have to choose from a variety of parameters ranging from CPU types, available memory, network throughput, disk types, disk I/O, and VM placement. As enterprise workloads start to migrate to the cloud, cloud providers will continue to add new configurations, which will only increase the complexity.
By applying AI, workloads can be mapped to the right configuration of servers and virtual machines. After running the workload in its peak state, AIOps can recommend the correct instance family type, storage choices, network configuration, and even the IO throughput of storage. This takes the guesswork out of the equation by aligning the workload characteristics with an appropriate IT resource configuration.