UC Santa Cruz

Non-volatile memory (NVRAM) storage devices are increasingly used in both consumer and enterprise systems, providing high performance and low energy consumption compared to hard drives. Unfortunately, an inflexible block interface and multiple I/O subsystems designed for slow hard drives make it difficult to utilize NVRAMs in a portable and efficient manner. For example, in the OS storage stack, I/O workloads are reshaped and throttled to generate a small number of concurrent I/O requests, though NVRAMs can handle much higher workloads, and unnecessary address mapping is performed in a file system. Using NVRAM as memory can be difficult with a block interface because of small reads and writes that are common in mem- ory workloads. At a device level, NVRAM devices need to perform a number of heuristics to attempt to recover filesystem-level semantics lost during the transmission through a block interface. As a result of these legacy interfaces, the current architecture is limited in achieving portability, efficiency, and extensibility, leaving behind more complicated problems, such as supporting heterogeneous NVRAM devices and utilizing device features.

This thesis proposes the use of the object-based storage model as a way of addressing the shortfalls of the current NVRAM architecture. More specifically, we show that the on- device NVRAM management layer can be as efficient as that of a native file system designed for a specific type of NVRAM while providing tightly coupled hardware optimizations and drop-in replacement for new types of NVRAMs. To explore the design flexibility given by this model, we investigate several data placement policies for flash memory exploiting the rich metadata coming from an object-interface. We also design an object-based byte-addressable NVRAM device to demonstrate that the object model allows more reliable, efficient NVRAM management by integrating its core data structures and a wear-leveling policy together. Our wear-leveling policy enables a low overhead, multi-granularity wear-level tracking for byte- addressable memories by exploiting the uniformity of a random function. We also investigate its extensibility and efficiency by designing Muninn, a object-based versioning key-value store for flash memory.

Realizing the object storage model requires changes in several subsystems of oper- ating systems and hardware components, but no further system changes will be required to support new types of NVRAM. Even with the current storage stack, we hypothesize that some of the features of this model can be adopted to better utilize device hardware and improve over- all efficiency of the storage system. To demonstrate these capabilities, we introduce Smart SSD, which pairs in-device processing with a powerful host system capable of handling data-oriented tasks without modifying operating system code. Our system is built on a real SATA-based SSD and extends the Hadoop MapReduce framework to support in-storage processing. Our experi- ments show that total energy consumption is reduced by 50% due to the low-power processing inside a Smart SSD. Moreover, a system with a Smart SSD can outperform host-side processing by a factor of two or three by efficiently utilizing internal parallelism when applications have light traffic to the device DRAM under the current architecture.