I've found that when I've had to tune for lower latency vs throughput, I've tuned nr_requests down from it's default (to as low as 32). The idea being smaller batches equals lower latency.
Also for read_ahead_kb I've found that for sequential reads/writes, increasing this value offers better throughput, but I've found that this option really depends on your workload and IO pattern. For example on a database system that I've recently tuned I changed this value to match a single db page size which helped to reduce read latency. Increasing or decreasing beyond this value proved to hurt performance in my case.
As for other options or settings for block device queues:
max_sectors_kb = I've set this value to match what the hardware allows for a single transfer (check the value of the max_hw_sectors_kb (RO) file in sysfs to see what's allowed)
nomerges = this lets you disable or adjust lookup logic for merging io requests. (turning this off can save you some cpu cycles, but I haven't seen any benefit when changing this for my systems, so I left it default)
rq_affinity = I haven't tried this yet, but here is the explanation behind it from the kernel docs
If this option is '1', the block layer will migrate request completions to the
cpu "group" that originally submitted the request. For some workloads this
provides a significant reduction in CPU cycles due to caching effects.
For storage configurations that need to maximize distribution of completion
processing setting this option to '2' forces the completion to run on the
requesting cpu (bypassing the "group" aggregation logic)"
scheduler = you said that you tried deadline and noop. I've tested both noop and deadline, but have found deadline win's out for the testing I've done most recently for a database server.
NOOP performed well, but for our database server I was still able to achieve better performance adjusting the deadline scheduler.
Options for deadline scheduler located under /sys/block/{sd,cciss,dm-}*/queue/iosched/ :
fifo_batch = kind of like nr_requests, but specific to the scheduler. Rule of thumb is tune this down for lower latency or up for throughput. Controls the batch size of read and write requests.
write_expire = sets the expire time for write batches default is 5000ms. Once again decrease this value decreases your write latency while increase the value increases throughput.
read_expire = sets the expire time for read batches default is 500ms. Same rules apply here.
front_merges = I tend to turn this off, and it's on by default. I don't see the need for the scheduler to waste cpu cycles trying to front merge IO requests.
writes_starved = since deadline is geared toward reads the default here is to process 2 read batches before a write batch is processed. I found the default of 2 to be good for my workload.
Best Answer
Just recently I read an article by one of Godaddy's engineers about this very topic: Learning to Deal with Learning
On their hardware (Dell PERC cards) battery learning cycle happens every 90 days, but no way to know when exactly it'll happen, ie during peak or off-peak hours.
They talked about different solutions:
Outright disable Battery Learning. Problem with this option is that you won't know the status of your battery and how long and how much it can hold charge, so in the case of outage you can risk data loss.
Use different hardware. Some controllers have 2 batteries and flip between them during such learning cycles. Additionally, there are RAID controllers(such as Dell H710) that do not need batteries but instead use non-volatile NVRAM to store uncommitted data.
Force write-back(caching) regardless of the status of your batteries. Like the 1st solution, you are risking data loss.
Ultimately, they setup crons for off-peak hours that monitor for the next learn cycle and if it is within the next 24 hours, they force it to happen immediately. That way they keep the benefit of exercising batteries yet without running it at peak-usage times.