The items below consider the number of variables in play with a given workload, here are some thoughts on how to run the bottom up testing.
For DSM, RAID'ed, Cached, and “Think Timed” I/O the runs will probably need to be executed several times. The final result will be the average of the runs and standard deviations must be calculated.
Run consolidated workloads with known performance metrics on a DSM together on the same DSM. Observe the results. This should be representative of pure consolidation in which only workload parameters are varying.
Run consolidated workloads with known performance metrics on a DSM together on an SSM consisting of the aggregate disks of the two DSMs. Observe the results. This should be representative of consolidation in which both workload parameters are varying and the underlying disks are varying.
For both Single Model I/O and DSM/SSM I/O, disable all caching in the stack.
For both Single Model I/O and DSM/SSM I/O, enable read caching in the stack.
For both Single Model I/O and DSM/SSM I/O, enable write caching in the stack.
For both Single Model I/O and DSM/SSM I/O, enable read and write caching in the stack.
For both Single Model I/O and DSM/SSM I/O, enable various RAID levels on the underlying disk subsystem. This should be tested out with read cached, write cached, and fully cached I/O tests.
For both Single Model I/O, DSM/SSM I/O, and all of the other variables, think time modeling will be necessary. The worst case of all workloads with no think time will reveal the weak points of the model and the system, however, this is not a realistic scenario.
Single Workload Model Questions
What is the expected performance of a a given single workload, with known parameters,
given a device configuration?
What device configuration and spindle allocation is needed to achieve a certain level of
performance for a given single workload with known parameters?
Device Questions
What will be the impact upon current workloads attached to a device/LUN if additional
spindle is added?
This question's answer should take into account spindle rotational latency and seek times.
Ideally, it should support a heterogeneous mixture of spindles.
What will be the impact upon current workloads attached to a device/LUN if the spindle
RAID level is changed?
Consolidate Workload Model Questions
What will be the impact upon a current workload with a known device configuration should a
new workload, with known parameters, be consolodated onto the existing device with the existing
workload?
In short, how does consolidation of workloads affect the existing performance characteristics.
What amount of spindle is necessary to accomodate a new workload that is to be consolidated with
an existing workload while maintaining given performance requirements.
Small to Large Enterprises, Probably Not Scientific Computing Monolithic Scale Up Arrays and Scale Up Brick Storage Agility of Provisioning (Capacity vs. Performance)
Sequentiality vs. Randomness
That which you think is sequential is really not.
Consider multiuser scenarios (Streaming media to multiple users)
Consider consolidation scenarios (VMWare, multi-database services, etc...)
Hence, single instance sequential * multiple users == random
Exhaustive microbenchmarks
Block Traces
I/O Scheduling
Proportional Share Scheduling for Priority Control
Block Reorganization
Block Replication
DSM - Dedicated Spindle Model
SSM - Shared Spindle Model