Additional Dimension to Multi-Dimensional Scaling (Resource Pools)

I am logging this MB for tracking purposes (say in the future) we can consider an additional dimension to Multi-Dimensional Scaling (MDS) [Resource Pooling]

Today our MDS implementation requires parity  in node capacity within the Services Types - Index, Query and Data.

Though you can deploy mixed node sizes there is a single Memory / Core Configuration for all node types - Index , Data, Query. Deploying disparate machine types just skews the usage of resources across the cluster.

There is no disagreement that Data node types need to be 100% identical since they are hash partitioned. Query and Indexes however are stateless and non-partitioned services, there are advantages/benefits in having disparity in the instance types e.g. with resource pooling

e.g. 

• High Capacity Index nodes with Larger Memory, Larger Core Counts (Would not require index partitioning)
•  Higher or Lower Core / Memory Query Services can be used to cater to other use cases without interrupting production usage

Note: from a customer standpoint this makes a significant impact on the TCO and ease of use conversation. e.g. I don't have to have all i2 Instances which are exorbitantly expensive