Building Distributed Systems with Non-Volatile Main Memories and RDMA Networks

High-performance, byte-addressable non-volatile main memories (NVMMs) allow application developers to combine storage and memory into a single layer. These high-performance storage systems would be especially useful in large-scale data center environments where data is distributed and replicated across multiple servers.Unfortunately, existing approaches of providing remote storage access rest on the assumption that storage is slow, so the cost of the software and protocols is acceptable. Such assumption no longer holds for the fast NVMM. As a result, taking full advantage of NVMMs’ potential will require changes in system software and networking protocol. This thesis focuses on accessing remote NVMM efficiently using remote direct memory access (RDMA) network. RDMA enables a client to directly access memory on a remote machine without involving its local CPU.This thesis first presents Mojim, a system that provides replicated, reliable, and highly-available NVMM as an operating system service. Applications can access data in Mojim using normal load and store instructions while controlling when and how updates propagate to replicas using system calls. Our evaluation shows Mojim adds little overhead to the un-replicated system and provides 0.4x to 2.7x the throughput of the un-replicated system.This thesis then presents Orion, a distributed file system designed from for NVMM and RDMA networks. Traditional distributed file systems are designed for slower hard drives. These slower media incentivizes complex optimizations (e.g., queuing, striping, and batching) around disk accesses. Orion combines file system functions and network operations into a single layer. It provides low latency metadata accesses and outperforms existing distributed file systems by a large margin.Finally, an NVMM application can map files backed by an NVMM file system into its address space, and accesses them using CPU instructions. In this case, RDMA and NVMM file systems introduce duplication of effort on permissions, naming, and address translation. We introduce two changes to the existing RDMA protocol: the file memory region (FileMR) and range based address translation. By eliminating redundant translations, FileMR minimizes the number of translations done at the NIC, reducing the load on the NIC’s translation cache and resulting in application performance improvement by 1.8x - 2.0x

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

Simurgh: a fully decentralized and secure NVMM user space file system

The availability of non-volatile main memory (NVMM) has started a new era for storage systems and NVMM specific file systems can support extremely high data and metadata rates, which are required by many HPC and data-intensive applications. Scaling metadata performance within NVMM file systems is nevertheless often restricted by the Linux kernel storage stack, while simply moving metadata management to the user space can compromise security or flexibility. This paper introduces Simurgh, a hardware-assisted user space file system with decentralized metadata management that allows secure metadata updates from within user space. Simurgh guarantees consistency, durability, and ordering of updates without sacrificing scalability. Security is enforced by only allowing NVMM access from protected user space functions, which can be implemented through two proposed instructions. Comparisons with other NVMM file systems show that Simurgh improves metadata performance up to 18x and application performance up to 89% compared to the second-fastest file system.This work has been supported by the European Comission’s BigStorage project H2020-MSCA-ITN2014-642963. It is also supported by the Big Data in Atmospheric Physics (BINARY) project, funded by the Carl Zeiss Foundation under Grant No.: P2018-02-003.Peer ReviewedPostprint (author's final draft

Improving the Performance of Big Data Analytics Platforms by Task and I/O Granularity Adjustment

Department of Computer Science and EngineeringWith the massive increase in the amount of semi-structured and unstructured web data, big data analytics platforms have emerged and started to evolve rapidly. Apache Hadoop has been developed for batch processing on a large dataset, and systems for interactive and general purpose applications have been developed alongside NoSQL databases. Numerous efforts have been made to improve the performance of Hadoop and NoSQL databases, including utilizing a new device called NVMM for NoSQL databases. Nonetheless, their performance is still far from satisfactory due to inadequate granularity for tasks and I/O. In this dissertation, we present novel techniques to improve the performance of Apache Hadoop and NVMM-based LSM-tree by adjusting task and I/O granularity. First, we analyze YARN container overhead and present dynamic input split size adjustment scheme, which can logically combine multiple HDFS blocks and increase the input size of each container, thereby enabling a single map wave and reducing the number of containers and their initialization overhead. Experimental results shows that we can avoid recurring container overhead by selecting the right size for input splits and reducing the number of containers. Second, we present a novel HDFS block coalescing scheme that mitigates the YARN con tainer overhead. Our assorted block coalescing scheme combines multiple HDFS blocks and creates large input splits of various sizes, reducing the number of containers and their initializa tion overhead. Our experimental study shows the block coalescing scheme significantly reduces the container overhead while it achieves good load balancing and job scheduling fairness without impairing the degree of overlap between map phase and reduce phase. Third, we discuss design choice of using NVMM for indexing structure in NoSQL databases and present ZipperDB, a key-value store that redesigns LSM-tree for byte-addressable persistent memory. To benefit from the byte-addressability of persistent memory, ZipperDB employs byte addressable persistent SkipLists and performs Zipper Compaction, a novel in-place compaction algorithm that merges two adjacent persistent SkipLists without compromising the failure atomicity. The byte-addressable compaction helps mitigate the write amplification problem, which is known to be the root cause of the write stall problem in LSM-tree. Finally, we present ListDB, a write-optimized key-value store for NVMM to overcome the gap between DRAM and NVMM write latencies and thereby, resolve the write stall problem. ListDB consists of three novel techniques: (i) byte-addressable Index-Unified Logging, which incrementally converts write-ahead logs into SkipLists, (ii) Braided SkipList, a simple NUMA aware SkipList that effectively reduces the NUMA effects of NVMM, and (iii) NUMA-aware Zipper Compaction. Using the three techniques, ListDB makes background flush and com paction fast enough to resolve the infamous write stall problem and shows 1.6x and 25x higher write throughputs than PACTree and Intel Pmem-RocksDB, respectively.ope

Persistent Memory File Systems:A Survey

Persistent Memory (PM) is non-volatile byte-addressable memory that offers read and write latencies in the order of magnitude smaller than flash storage, such as SSDs. This survey discusses how file systems address the most prominent challenges in the implementation of file systems for Persistent Memory. First, we discuss how the properties of Persistent Memory change file system design. Second, we discuss work that aims to optimize small file I/O and the associated meta-data resolution. Third, we address how existing Persistent Memory file systems achieve (meta) data persistence and consistency

Enabling Recovery of Secure Non-Volatile Memories

Emerging non-volatile memories (NVMs), such as phase change memory (PCM), spin-transfer torque RAM (STT-RAM) and resistive RAM (ReRAM), have dual memory-storage characteristics and, therefore, are strong candidates to replace or augment current DRAM and secondary storage devices. The newly released Intel 3D XPoint persistent memory and Optane SSD series have shown promising features. However, when these new devices are exposed to events such as power loss, many issues arise when data recovery is expected. In this dissertation, I devised multiple schemes to enable secure data recovery for emerging NVM technologies when memory encryption is used. With the data-remanence feature of NVMs, physical attacks become easier; hence, emerging NVMs are typically paired with encryption. In particular, counter-mode encryption is commonly used due to its performance and security advantages over other schemes (e.g., electronic codebook encryption). However, enabling data recovery in power failure events requires the recovery of security metadata associated with data blocks. Naively writing security metadata updates along with data for each operation can further exacerbate the write endurance problem of NVMs as they have limited write endurance and very slow write operations. Therefore, it is necessary to enable the recovery of data and security metadata (encryption counters) but without incurring a significant number of writes. The first work of this dissertation presents an explanation of Osiris, a novel mechanism that repurposes error correcting code (ECC) co-located with data to enable recovery of encryption counters by additionally serving as a sanity-check for encryption counters used. Thus, by using a stop-loss mechanism with a limited number of trials, ECC can be used to identify which encryption counter that was used most recently to encrypt the data and, hence, allow correct decryption and recovery. The first work of this dissertation explores how different stop-loss parameters along with optimizations of Osiris can potentially reduce the number of writes. Overall, Osiris enables the recovery of encryption counters while achieving better performance and fewer writes than a conventional write-back caching scheme of encryption counters, which lacks the ability to recover encryption counters. Later, in the second work, Osiris implementation is expanded to work with different counter-mode memory encryption schemes, where we use an epoch-based approach to periodically persist updated counters. Later, when a crash occurs, we can recover counters through test-and-verification to identify the correct counter within the size of an epoch for counter recovery. Our proposed scheme, Osiris-Global, incurs minimal performance overheads and write overheads in enabling the recovery of encryption counters. In summary, the findings of the present PhD work enable the recovery of secure NVM systems and, hence, allows persistent applications to leverage the persistency features of NVMs. Meanwhile, it also minimizes the number of writes required in meeting this crash consistency requirement of secure NVM systems