Now let’s check out the performance of our arrays with higher workload:

The increase in the workload doesn’t really change the performance picture. The graphs, however, are shaped up by the arrays algorithms rather than HDD algorithms, which was the case under linear workload. The single HDD graphs remained practically the same, because it reflects only the lazy write algorithms of the HDD.

RAID0 array graphs look similar to the single HDD graph and are proportional to the number of hard drives in the array. However, unlike linear workload, we can seethe proportion in RandomRead mode already and as the number of write requests increases it becomes more evident. Only the 7-HDD graphs doesn’t fit into this picture, because we can see significant performance drops at 0% of writes and 60% of writes.

RAID1 and RAID10 mirrored arrays speed up significantly in RandomRead mode, because the absence of write requests and sufficient amount of read requests with random address let Twinstor technology show its real best. As the share of write requests increases, Twinstor technology loses its efficiency, because it can only optimize read requests. On the other hand, the lazy write algorithms of the hard disk drives start showing more with the increase of write requests share, so the performance of RAID10 array in RandomWrite mode gets even higher. All in all, RAID1 array is always faster than a single HDD when working with 16 request queue, and the RAID10 array of 2n HDDs is always faster than RAID0 array of n HDDs in all modes.

RAID5 arrays process read requests as efficiently as RAID0 arrays. However, as the number of writes starts growing, RAID5 arrays slow down significantly. RAID5 performance is directly proportional to the number of hard drives in the array. Only the RAID5 array of 3HDD doesn’t belong to the overall picture.