With the emergence of Non-Volatile Memories (NVMs) and their shortcomings such as limited endurance and high power consumption in write requests, several studies have suggested hybrid memory architecture employing both Dynamic Random Access Memory (DRAM) and NVM in a memory system. By conducting a comprehensive experiments, we have observed that such studies lack to consider very important aspects of hybrid memories including the effect of: a) data migrations on performance, b) data migrations on power, and c) the granularity of data migration. This paper presents an efficient data migration scheme at the Operating System level in a hybrid DRAM-NVM memory architecture. In the proposed scheme, two Least Recently Used (LRU) queues, one for DRAM section and one for NVM section, are used for the sake of data migration. With careful characterization of the workloads obtained from PARSEC benchmark suite, the proposed scheme prevents unnecessary migrations and only allows migrations which benefits the system in terms of power and performance. The experimental results show that the proposed scheme can reduce the power consumption up to 79% compared to DRAM-only memory and up to 48% compared to the state-of-the art techniques.

In recent years, Solid-State Drives (SSDs) have gained tremendous attention in computing and storage systems due to significant performance improvement over Hard Disk Drives (HDDs). The cost per capacity of SSDs, however, prevents them from entirely replacing HDDs in such systems. One approach to effectively take advantage of SSDs is to use them as a caching layer to store performance critical data blocks in order to reduce the number of accesses to HDD-based disk subsystem. Due to characteristics of Flash-based SSDs such as limited write endurance and long latency on write operations, employing caching algorithms at the Operating System (OS) level necessitates to take such characteristics into consideration. Previous OS-level caching techniques are optimized towards only one type of application, which affects both generality and applicability. In addition, they are not adaptive when the workload pattern changes over time. This paper presents an efficient Reconfigurable Cache Architecture (ReCA) for storage systems using a comprehensive workload characterization to find an optimal cache configuration for I/O intensive applications. For this purpose, we first investigate various types of I/O workloads and classify them into five major classes. Based on this characterization, an optimal cache configuration is presented for each class of workloads. Then, using the main features of each class, we continuously monitor the characteristics of an application during system runtime and the cache organization is reconfigured if the application changes from one class to another class of workloads. The cache reconfiguration is done online and workload classes can be extended to emerging I/O workloads in order to maintain its efficiency with the characteristics of I/O requests. Experimental results obtained by implementing ReCA in a 4U rackmount server with SATA 6Gb/s disk interfaces running Linux 3.17.0 show that the proposed architecture improves performance and lifetime up to 24% and 33%, respectively.

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Emerging Non-Volatile Memories (NVMs) have promising advantages (e.g., lower idle power, higher density, and nonvolatility) over the existing predominant main memory technology, DRAM. Yet, NVMs also have disadvantages (e.g., longer latencies, higher active power, and limited endurance). System architects are therefore examining hybrid DRAM-NVM main memories to enable the advantages of NVMs while avoiding the disadvantages as much as possible. Unfortunately, the hybrid memory design space is very large and complex due to the existence of very different types of NVMs and their rapidly-changing characteristics. Therefore, optimization of performance and lifetime of hybrid memory based computing platforms and their experimental evaluation using traditional simulation methods can be very time-consuming and sometimes even impractical. As such, it is necessary to develop a fast and flexible analytical model to estimate the performance and lifetime of hybrid memories on various workloads. This paper presents an analytical model for hybrid memories based on Markov decision processes. The proposed model estimates the hit ratio and lifetime for various configurations of DRAM-NVM hybrid main memories. Our model also provides accurate estimation of the effect of data migration policies on the hybrid memory hit ratio (i.e., percentage of accesses supplied by either DRAM or NVM), one of the most important factors in hybrid memory performance and lifetime. Such an analytical model can aid designers to tune hybrid memory configurations to improve performance and/or lifetime. We present several optimizations that make our model more efficient while maintaining its accuracy. Our experimental evaluations conducted using the PARSEC benchmark suite show that the proposed model (a) accurately predicts the hybrid memory hit ratio compared to the state-of-the-art hybrid memory simulators with an average (maximum) error of 4.61% (13.6%) on a commodity server (equipped with 192GB main memory and quad-core Xeon processor), (b) accurately estimates the NVM lifetime with an average (maximum) error of 2.93% (8.8%), and (c) is on average (up to) 4x (10x) faster than conventional state-of-the-art simulation platforms for hybrid memories.

Storage subsystem is considered as the performance bottleneck of computer systems in data-intensive applications. Solid-State Drives (SSDs) are emerging storage devices which unlike Hard Disk Drives (HDDs), do not have mechanical parts and therefore, have superior performance compared to HDDs. Due to the high cost of SSDs, entirely replacing HDDs with SSDs is not economically justified. Additionally, SSDs can endure a limited number of writes before failing. To mitigate the shortcomings of SSDs while taking advantage of their high performance, SSD caching is practiced in both academia and industry. Previously proposed caching architectures have only focused on either performance or endurance and neglected to address both parameters in suggested architectures. Moreover, the cost, reliability, and power consumption of such architectures is not evaluated. This paper proposes a hybrid I/O caching architecture that while offers higher performance than previous studies, it also improves power consumption with a similar budget. The proposed architecture uses DRAM, Read-Optimized SSD (RO-SSD), and Write-Optimized SSD (WO-SSD) in a three-level cache hierarchy and tries to efficiently redirect read requests to either DRAM or RO-SSD while sending writes to WO-SSD. To provide high reliability, dirty pages are written to at least two devices which removes any single point of failure. The power consumption is also managed by reducing the number of accesses issued to SSDs. The proposed architecture reconfigures itself between performance-and enduranceoptimized policies based on the workload characteristics to maintain an effective tradeoff between performance and endurance. We have implemented the proposed architecture on a server equipped with industrial SSDs and HDDs. The experimental results show that as compared to state-of-the-art studies, the proposed architecture improves performance and power consumption by an average of 8% and 28%, respectively, and reduces the cost by 5% while increasing the endurance cost by 4.7% and negligible reliability penalty.