Workload Placement
Definition - What does Workload Placement mean?
Workload placement is a term that has developed in reference to managing resources in hardware virtualization systems. It involves the assignment of workloads in such a way that VM efficiency is optimized.
Techopedia explains Workload Placement
Many companies use virtualization to make IT architectures more efficient. In a virtualization setup, physical hardware is partitioned into virtual systems: physical machines become virtual machines (VMs), physical drives become virtual storage facilities, and the rest of the resources like processing power (virtual CPU) and memory (virtual memory) are allocated dynamically within the system.
In this context, workload placement involves assigning a specific workload where it can be easily completed by a VM or another component. Some issues involved here include the allocation of resources, such as how a virtual CPU or a virtual memory is assigned to a VM. Issues like VM density or clustering also apply.
In virtualization workload placement, it is believed that traditional load balancing does not go far enough to ensure the efficient use of virtual resources. Workload placement tools therefore try to address this issue, for instance, by analyzing metrics like CPU readiness and CPU wait time to determine whether different workloads should be put in a particular virtual cluster. This and other management techniques allow a hardware virtualization infrastructure to perform as expected.
In this context, workload placement involves assigning a specific workload where it can be easily completed by a VM or another component. Some issues involved here include the allocation of resources, such as how a virtual CPU or a virtual memory is assigned to a VM. Issues like VM density or clustering also apply.
In virtualization workload placement, it is believed that traditional load balancing does not go far enough to ensure the efficient use of virtual resources. Workload placement tools therefore try to address this issue, for instance, by analyzing metrics like CPU readiness and CPU wait time to determine whether different workloads should be put in a particular virtual cluster. This and other management techniques allow a hardware virtualization infrastructure to perform as expected.
