Supporting atomic durability of updates for persistent memories is typically achieved with Write-Ahead Logging (WAL). WAL flushes log entries to persistent memory before making the actual data persistent to ensure that a consistent state can be recovered if a crash occurs. Performing WAL in hardware is attractive because it makes most aspects of log management transparent to software, and it completes log persist operations (LPOs) and data persist operations (DPOs) in the background, overlapping them with the execution of other instructions.

Using flash-based solid state drives (SSDs) as main memory has been proposed as a practical solution towards scaling memory capacity for data-intensive applications. However, almost all existing approaches rely on the paging mechanism to move data between SSDs and host DRAM. This inevitably incurs significant performance overhead and extra I/O traffic. Thanks to the byte-addressability supported by the PCIe interconnect and the internal memory in SSD controllers, it is feasible to access SSDs in both byte and block granularity today. Exploiting the benefits of SSD's byte-accessibility in today's memory-storage hierarchy is, however, challenging as it lacks systems support and abstractions for programs. In this paper, we present FlatFlash, an optimized unified memory-storage hierarchy, to efficiently use byte-addressable SSD as part of the main memory. We extend the virtual memory management to provide a unified memory interface so that programs can access data across SSD and DRAM in byte granularity seamlessly. We propose a lightweight, adaptive page promotion mechanism between SSD and DRAM to gain benefits from both the byte-addressable large SSD and fast DRAM concurrently and transparently, while avoiding unnecessary page movements. Furthermore, we propose an abstraction of byte-granular data persistence to exploit the persistence nature of SSDs, upon which we rethink the design primitives of crash consistency of several representative software systems that require data persistence, such as file systems and databases. Our evaluation with a variety of applications demonstrates that, compared to the current unified memory-storage systems, FlatFlash improves the performance for memory-intensive applications by up to 2.3x, reduces the tail latency for latency-critical applications by up to 2.8x, scales the throughput for transactional database by up to 3.0x, and decreases the meta-data persistence overhead for file systems by up to 18.9x. FlatFlash also improves the cost-effectiveness by up to 3.8x compared to DRAM-only systems, while enhancing the SSD lifetime significantly.

The rate of network packets encapsulating requests from clients can significantly affect the utilization, and thus performance and sleep states of processors in servers deploying a power management policy. To improve energy efficiency, servers may adopt an aggressive power management policy that frequently transitions a processor to a low-performance or sleep state at a low utilization. However, such servers may not respond to a sudden increase in the rate of requests from clients early enough due to a considerable performance penalty of transitioning a processor from a sleep or low-performance state to a high-performance state. This in turn entails violations of a service level agreement (SLA), discourages server operators from deploying an aggressive power management policy, and thus wastes energy during low-utilization periods. For both fast response time and high energy-efficiency, we propose NCAP, Network-driven, packet Context-Aware Power management for client-server architecture. NCAP enhances a network interface card (NIC) and its driver such that it can examine received and transmitted network packets, determine the rate of network packets containing latency-critical requests, and proactively transition a processor to an appropriate performance or sleep state. To demonstrate the efficacy, we evaluate on-line data-intensive (OLDI) applications and show that a server deploying NCAP consumes 37~61% lower processor energy than a baseline server while satisfying a given SLA at various load levels.