  This paper presents a performance model of the ATA/ATAPI SSD Trim command
under various types of user workloads, including a uniform random workload, a
workload with hot and cold data, and a workload with N temperatures of data. We
first examine the Trim-modified uniform random workload to predict utilization,
then use this result to compute the resultant level of effective
overprovisioning. This allows modification of models previously suggested to
predict write amplification of a non-Trim uniform random workload under greedy
garbage collection. Finally, we expand the theory to cover a workload
consisting of hot and cold data (and also N temperatures of data), providing
formulas to predict write amplification in these scenarios.
