The emergence of new memory technologies provides us with opportunity to enhance the properties of existing memory architectures. One such technology is Phase Change Memory (PCM) which boasts superior scalability, power savings, non-volatility, and a performance competitive to Dynamic Random Access Memory (DRAM). In this paper, we propose a write buffer architecture for Solid-State Drives (SSDs) which attempts to exploit PCM as a DRAM alternative while alleviating its issues such as long write latency, high write energy, and finite endurance. To this end and based on thorough I/O characterization of desktop and enterprise applications, we propose a hybrid DRAM-PCM SSD cache design with an intelligent data movement scheme. This architecture manages to improve energy efficiency while enhancing performance and endurance. To study the design trade-offs between energy, performance, and endurance, we augmented Microsoft's DiskSim SSD model with a detailed hybrid cache using PCM and DRAM parameters from a rigorous survey of device prototypes. We study the design choices of implementing different PCM and DRAM arrays to achieve the best trade-off between energy and performance. The results display up to 77 percent power savings compared to a DRAM cache and up to percent reduction in request response time for a variety of workloads, while greatly improving disk endurance.

Disk traces are typically used to analyze real-life workloads and for replay-based evaluations. This approach benefits from capturing important details such as varying behavior patterns, bursty activity, and diurnal patterns of system activity, which are often missing from the behavior of workload synthesis tools. However, accurate capture of such details requires recording traces containing long durations of system activity, which are difficult to use for replay-based evaluation. One way of solving the problem of long storage trace duration is the use of disk simulators. While publicly available disk simulators can greatly accelerate experiments, they have not kept up with technological innovations in the field. The variety, complexity, and opaque nature of storage hardware make it very difficult to implement accurate simulators. The alternative, replaying the whole traces on real hardware, suffers from either long run-time or required manual reduction of experimental time, potentially at the cost of reduced accuracy. On the other hand, burstiness, auto-correlation, and complex spatio-temporal properties of storage workloads make the known methods of sampling workload traces less effective.