Yes I tried a scenario in which I gave the flavor as 1000MB ram with the ram_allocation_ratio as 1 and due to the HMC rounding property it rounded off this value to 1024MB and launched an instance...but the in the dashboard under the hypervisor stats it is showing as ram-used 1000MB and also ram_filter which is a scheduler filter com pares this condition https://github.com/openstack/nova/blob/4f91ed3a547965ed96a22520edcfb783e7936e95/nova/scheduler/filters/ram_filter.py#L39
even this parameters like memory_mb are all taken from the flavor so...there are two problems in this
1.With the existing code if we allocate the max_memory as 2000MB to be used by a specific host due to HMC rounding property it consumes 2048MB (because even if flavor is provided as 1000 it rounds to 1024 only)...OVER CONSUMPTION OF RAM
2. This also shows wrong hypervisor stats and mislead scheduler ram_filter...
Yes I tried a scenario in which I gave the flavor as 1000MB ram with the ram_allocation_ ratio as 1 and due to the HMC rounding property it rounded off this value to 1024MB and launched an instance...but the in the dashboard under the hypervisor stats it is showing as ram-used 1000MB and also ram_filter which is a scheduler filter com pares this condition /github. com/openstack/ nova/blob/ 4f91ed3a547965e d96a22520edcfb7 83e7936e95/ nova/scheduler/ filters/ ram_filter. py#L39
https:/
even this parameters like memory_mb are all taken from the flavor so...there are two problems in this
1.With the existing code if we allocate the max_memory as 2000MB to be used by a specific host due to HMC rounding property it consumes 2048MB (because even if flavor is provided as 1000 it rounds to 1024 only)...OVER CONSUMPTION OF RAM
2. This also shows wrong hypervisor stats and mislead scheduler ram_filter...