Intelligent workload management distributes application workloads across data center and cloud-based infrastructure based on the resources available to run the applications and process the data. Workloads, the current ways an application is working on both physical and virtual servers, require space and energy, and server performance will slow if overloaded. Intelligent workload management learns the resource needs of a workload and then assigns it to an environment where it can run effectively. Good intelligent workload management systems manage virtual environments, data centers, and an organization’s activity in the cloud.
Cloud workload management
Different operating environments make workload management more challenging. Company data and processes are scattered across public clouds, private clouds, and on-premise servers. To successfully control workloads, businesses need management systems that know how and when to transfer applications and workloads.
In a hybrid cloud environment, workload balancers funnel workloads into the best location. Hybrid application workload management can transfer workloads between multiple cloud and on-premise environments. Load balancing moves workloads between servers dependent on their availability and efficiency.
Artificial intelligence and workload management
As data centers expand their infrastructure to accommodate larger amounts of data, artificial intelligence and machine learning require sizable workloads and plenty of data. This puts further strain on workload management processes as workloads increase in complexity. But on the other side of the coin, artificial intelligence can also be deployed in intelligent workload management, learning how to identify the best places to transfer applications, locate slowdowns and prescribe solutions, or automatically begin adjusting workload patterns.
