Building Reliable Software for Persistent Memory

Persistent memory (PMEM) technologies preserve data across power cycles and provide performance comparable to DRAM. In emerging computer systems, PMEM will operate on the main memory bus, becoming byte-addressable and cache-coherent. One key feature enabled by persistent memory is to allow software directly accessing durable data using the CPU’s load/store instructions, even from the user-space.However, building reliable software for persistent memory faces new challenges from two aspects: crash consistency and fault tolerance. Maintaining crash consistency requires the ability to recover data integrity in the event of system crashes. Using load/store instructions to access durable data introduces a new programming paradigm, that is prone to new types of programming errors. Fault tolerance involves detecting and recovering from persistent memory errors, including memory media errors and scribbles from software bugs. With direct access, file systems and user-space applications have to explicitly manage these errors, instead of relying on convenient functions from lower I/O stacks.We identify unique challenges in improving reliability for PMEM-based software and propose solutions. The thesis first introduces NOVA-Fortis, a fault-tolerant PMEM file system incorporating replication, checksums, and parity for protecting the file system’s metadata and the user’s file data. NOVA-Fortis is both fast and resilient in the face of corruption due to media errors and software bugs.NOVA-Fortis only protects file data via the read() and write() system calls. When an application memory-maps a PMEM file, NOVA-Fortis has to disable file data protection because mmap() leaves the file system unaware of updates made to the file. For protecting memory-mapped PMEM data, we present Pangolin, a fault-tolerant persistent object library to protect an application’s objects from persistent memory errors.Writing programs to ensure crash consistency in PMEM remains challenging. Recovery bugs arise as a new type of programming error, preventing a post-crash PMEM file from recovering to a consistent state. Thus, we design two debugging tools for persistent memory programming: PmemConjurer and PmemSanitizer. PmemConjurer is a static analyzer using symbolic execution to find recovery bugs without running a compiled program. PmemSanitizer contains compiler instrumentation and run-time recovery bug analysis, compensating PmemConjurer with multi-threading support and store reordering tests

Providing Easy to Use and Fast Programming Support for Non-Volatile Memories

Non-Volatile Memory (NVM) technologies, such as 3D XPoint, offer DRAM-like performance and byte-addressable access to persistent data. NVMs promise an opportunity for fast, persistent data structures, and a wide range of applications stand to benefit from the performance potential of these technologies. These potential benefits are greatest when applications access NVM directly via load/store instructions rather than conventional file-based interfaces. Directly accessing NVM presents several challenges. In particular, applications need guaranteed consistency and safety semantics to protect their data structures in the face of system failures and programming errors.Implementing data structures that meet these requirements is challenging and error-prone. Existing methods for building persistent data structures require either in-depth code changes to an existing data structure or rewriting the data structure from scratch. Unfortunately, both of these methods are labor-intensive and error-prone.Failure-atomicity libraries and programming language extensions can simplify this task. However, all the proposed solutions either require pervasive changes to existing software or incur unacceptable overheads to runtime performance. As a result, porting legacy applications to leverage NVM is likely to be prohibitively difficult and time-consuming.This dissertation first presents Breeze, an NVM toolchain that minimizes the changes necessary to enable legacy code to reap the benefits of directly accessing NVM. In contrast to PMDK and NVM-Direct, Breeze reduces the programming effort of porting Memcached and MongoDB by up to 2.8×, while providing equal or superior performance.Second, it introduces NVHooks, a compiler that automatically annotates NVM accesses and avoids disruptive and error-prone changes to programs. NVHooks reduces the cost of these annotations by applying novel, NVM-specific optimizations to their placement. For our tested benchmarks, NVHooks matches the performance of hand-annotated code while minimizing programmer effort.Finally, it presents Pronto, a new NVM library that reduces the programming effort required to add persistence to volatile data structures. Pronto uses asynchronous semantic logging (ASL) to allow adding persistence to the existing volatile data structure (e.g., C++ Standard Template Library containers) with minor programming effort. ASL moves most durability code off the critical path. Our evaluation shows Pronto data structures outperform highly-optimized NVM data structures by a large margin

ADDING PERSISTENCE TO MAIN MEMORY PROGRAMMING

Unlocking the true potential of the new persistent memories (PMEMs) requires eliminating traditional persistent I/O abstractions altogether, by introducing persistent semantics directly into main memory programming. Such a programming model elevates failure atomicity to a first-class application property in addition to in-memory data layout, concurrency-control, and fault tolerance, and therefore requires redesign of programming abstractions for both program correctness and maximum performance gains. To address these challenges, this thesis proposes a set of system software designs that integrate persistence with main memory programming, and makes the following contributions. First, this thesis proposes a PMEM-aware I/O runtime, NVStream, that supports fast durable streaming I/O. NVStream uses a memory-based I/O interface that integrates with existing I/O data movement operations of an application to accelerate persistent data writes. NVStream carefully designs its persistent data storage layout and crash-consistent semantics to match both application and PMEM characteristics. Specifically, we leverage the streaming nature of I/O in HPC workflows, to benefit from using a log-structured PMEM storage engine design, that uses relaxed write orderings and append-only failure-atomic semantics to form strongly consistent application checkpoints. Furthermore, we identify that optimizing the I/O software stack exposes the PMEM bandwidth limitations as a bottleneck during parallel HPC I/O writes, and propose a novel data movement design – PHX. PHX uses alternative network data movement paths available in datacenters to ease up the bandwidth pressure on the PMEM memory interconnects, all while maintaining the correctness of the persistent data. Next, the thesis explores the challenges and opportunities of using PMEM for true main memory persistent programming – a single data domain for both runtime and persistent applicationstate. Such a programming model includes maintaining ACID properties during each and every update to applications persistent structures. ACID-qualified persistent programming for multi-threaded applications is hard, as the programmer has to reason about both crash-consistency and synchronization – crash-sync – semantics for programming correctness. The thesis contributes new understanding of the correctness requirements for mixing different crash-consistent and synchronization protocols, characterizes the performance of different crash-sync realizations for different applications and hardware architectures, and draws actionable insights for future designs of PMEM systems. Finally, the application state stored on node-local persistent memory is still vulnerable to catastrophic node failures. The thesis proposes a replicated persistent memory runtime, Blizzard, that supports truly fault tolerant, concurrent and persistent data-structure programming. Blizzard carefully integrates userspace networking with byte addressable PMEM for a fast, persistent memory replication runtime. The design also incorporates a replication-aware crash-sync protocol that supports consistent and concurrent updates on persistent data-structures. Blizzard offers applications the flexibility to use the data structures that best match their functional requirements, while offering better performance, and providing crucial reliability guarantees lacking from existing persistent memory runtimes.Ph.D

Leveraging Non-Volatile Memory in Modern Storage Management Architectures

Non-volatile memory technologies (NVM) introduce a novel class of devices that combine characteristics of both storage and main memory. Like storage, NVM is not only persistent, but also denser and cheaper than DRAM. Like DRAM, NVM is byte-addressable and has lower access latency. In recent years, NVM has gained a lot of attention both in academia and in the data management industry, with views ranging from skepticism to over excitement. Some critics claim that NVM is not cheap enough to replace flash-based SSDs nor is it fast enough to replace DRAM, while others see it simply as a storage device. Supporters of NVM have observed that its low latency and byte-addressability requires radical changes and a complete rewrite of storage management architectures. This thesis takes a moderate stance between these two views. We consider that, while NVM might not replace flash-based SSD or DRAM in the near future, it has the potential to reduce the gap between them. Furthermore, treating NVM as a regular storage media does not fully leverage its byte-addressability and low latency. On the other hand, completely redesigning systems to be NVM-centric is impractical. Proposals that attempt to leverage NVM to simplify storage management result in completely new architectures that face the same challenges that are already well-understood and addressed by the traditional architectures. Therefore, we take three common storage management architectures as a starting point, and propose incremental changes to enable them to better leverage NVM. First, in the context of log-structured merge-trees, we investigate the impact of storing data in NVM, and devise methods to enable small granularity accesses and NVM-aware caching policies. Second, in the context of B+Trees, we propose to extend the buffer pool and describe a technique based on the concept of optimistic consistency to handle corrupted pages in NVM. Third, we employ NVM to enable larger capacity and reduced costs in a index+log key-value store, and combine it with other techniques to build a system that achieves low tail latency. This thesis aims to describe and evaluate these techniques in order to enable storage management architectures to leverage NVM and achieve increased performance and lower costs, without major architectural changes.:1 Introduction 1.1 Non-Volatile Memory 1.2 Challenges 1.3 Non-Volatile Memory & Database Systems 1.4 Contributions and Outline 2 Background 2.1 Non-Volatile Memory 2.1.1 Types of NVM 2.1.2 Access Modes 2.1.3 Byte-addressability and Persistency 2.1.4 Performance 2.2 Related Work 2.3 Case Study: Persistent Tree Structures 2.3.1 Persistent Trees 2.3.2 Evaluation 3 Log-Structured Merge-Trees 3.1 LSM and NVM 3.2 LSM Architecture 3.2.1 LevelDB 3.3 Persistent Memory Environment 3.4 2Q Cache Policy for NVM 3.5 Evaluation 3.5.1 Write Performance 3.5.2 Read Performance 3.5.3 Mixed Workloads 3.6 Additional Case Study: RocksDB 3.6.1 Evaluation 4 B+Trees 4.1 B+Tree and NVM 4.1.1 Category #1: Buffer Extension 4.1.2 Category #2: DRAM Buffered Access 4.1.3 Category #3: Persistent Trees 4.2 Persistent Buffer Pool with Optimistic Consistency 4.2.1 Architecture and Assumptions 4.2.2 Embracing Corruption 4.3 Detecting Corruption 4.3.1 Embracing Corruption 4.4 Repairing Corruptions 4.5 Performance Evaluation and Expectations 4.5.1 Checksums Overhead 4.5.2 Runtime and Recovery 4.6 Discussion 5 Index+Log Key-Value Stores 5.1 The Case for Tail Latency 5.2 Goals and Overview 5.3 Execution Model 5.3.1 Reactive Systems and Actor Model 5.3.2 Message-Passing Communication 5.3.3 Cooperative Multitasking 5.4 Log-Structured Storage 5.5 Networking 5.6 Implementation Details 5.6.1 NVM Allocation on RStore 5.6.2 Log-Structured Storage and Indexing 5.6.3 Garbage Collection 5.6.4 Logging and Recovery 5.7 Systems Operations 5.8 Evaluation 5.8.1 Methodology 5.8.2 Environment 5.8.3 Other Systems 5.8.4 Throughput Scalability 5.8.5 Tail Latency 5.8.6 Scans 5.8.7 Memory Consumption 5.9 Related Work 6 Conclusion Bibliography A PiBenc