This guide details the six steps proactive IT operations teams have taken to streamline automating IT resource management.
As digital transformation sweeps across the enterprise, IT operations are scrambling to keep up with the pace of change, respond to competitive challenges, and deliver world-class customer experiences. SysAdmins need to know how much IT capacity is needed to not only meet current workloads but also withstand sudden spikes in demand and support future growth. That’s far from easy in today’s modern cloud, hybrid, and virtual IT environments, where mission-critical services are deployed across containers and system dependencies are obscured through multiple layers of IT infrastructure. So how do we go about automating IT resource management? What are the steps that need to be taken?
Formerly, monitoring tools were simply not proficient at seizing vast quantities of performance data, forcing organizations to pick and choose which systems and applications they wanted to monitor. Now, with the rise of big data analytics, organizations are able to collect performance metrics on every system running in the IT environment. Automating IT resource management has changed the reach of IT operations.
Until recently, IT capacity planning has been more art than science, translating developer assumptions into workload projections and hypothetical IT requirements. Most companies incur excessive costs to over-provision servers or auto-provision cloud instances to address unexpected demand, instead of planning proactively. But advances in machine learning and predictive analytics have made it possible to automate this once painstaking, manual process, eliminate the guesswork, and accurately forecast how much capacity is available. This guide details the six proactive steps IT operations teams have taken to reduce excessive provisioning costs and automate the capacity planning process.