What type of performance does “On-Storage Processing” in Exadata aim to improve?

"On-Storage Processing" in Exadata is primarily designed to improve data querying efficiency. This technique allows certain operations to be executed directly on the storage layer, rather than moving vast amounts of data back and forth between the storage and the database servers. This minimizes data transfer and leverages the computational power of the storage cells, allowing for faster data processing and query execution.

By enabling data filtering and aggregation operations at the storage level, Exadata significantly reduces the volume of data that needs to be sent over the network to the database server for further processing. As a result, queries can be executed more efficiently and at a higher performance level.

In the context of the other options, while disk read/write speeds and network latency may be relevant factors in the overall performance of a database system, they are not the primary focus of on-storage processing techniques. Additionally, virtual machine scaling does not relate to the functionality of Exadata regarding on-storage processing, which specifically targets optimization of data querying processes.