Elevating commodity storage with the SALSA host translation layer

To satisfy increasing storage demands in both capacity and performance, industry has turned to multiple storage technologies, including Flash SSDs and SMR disks. These devices employ a translation layer that conceals the idiosyncrasies of their mediums and enables random access. Device translation layers are, however, inherently constrained: resources on the drive are scarce, they cannot be adapted to application requirements, and lack visibility across multiple devices. As a result, performance and durability of many storage devices is severely degraded. In this paper, we present SALSA: a translation layer that executes on the host and allows unmodified applications to better utilize commodity storage. SALSA supports a wide range of single- and multi-device optimizations and, because is implemented in software, can adapt to specific workloads. We describe SALSA's design, and demonstrate its significant benefits using microbenchmarks and case studies based on three applications: MySQL, the Swift object store, and a video server.Comment: Presented at 2018 IEEE 26th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS

Memory Subsystems for Security, Consistency, and Scalability

In response to the continuous demand for the ability to process ever larger datasets, as well as discoveries in next-generation memory technologies, researchers have been vigorously studying memory-driven computing architectures that shall allow data-intensive applications to access enormous amounts of pooled non-volatile memory. As applications continue to interact with increasing amounts of components and datasets, existing systems struggle to eÿciently enforce the principle of least privilege for security. While non-volatile memory can retain data even after a power loss and allow for large main memory capacity, programmers have to bear the burdens of maintaining the consistency of program memory for fault tolerance as well as handling huge datasets with traditional yet expensive memory management interfaces for scalability. Today’s computer systems have become too sophisticated for existing memory subsystems to handle many design requirements. In this dissertation, we introduce three memory subsystems to address challenges in terms of security, consistency, and scalability. Specifcally, we propose SMVs to provide threads with fne-grained control over access privileges for a partially shared address space for security, NVthreads to allow programmers to easily leverage nonvolatile memory with automatic persistence for consistency, and PetaMem to enable memory-centric applications to freely access memory beyond the traditional process boundary with support for memory isolation and crash recovery for security, consistency, and scalability

CATTmew: Defeating Software-only Physical Kernel Isolation

All the state-of-the-art rowhammer attacks can break the MMU-enforced inter-domain isolation because the physical memory owned by each domain is adjacent to each other. To mitigate these attacks, physical domain isolation, introduced by CATT, physically separates each domain by dividing the physical memory into multiple partitions and keeping each partition occupied by only one domain. CATT implemented physical kernel isolation as the first generic and practical software-only defense to protect kernel from being rowhammered as kernel is one of the most appealing targets. In this paper, we develop a novel exploit that could effectively defeat the physical kernel isolation and gain both root and kernel privileges. Our exploit can work without exhausting the page cache or the system memory, or relying on the information of the virtual-to-physical address mapping. The exploit is motivated by our key observation that the modern OSes have double-owned kernel buffers (e.g., video buffers and SCSI Generic buffers) owned concurrently by the kernel and user domains. The existence of such buffers invalidates the physical kernel isolation and makes the rowhammer-based attack possible again. Existing conspicuous rowhammer attacks achieving the root/kernel privilege escalation exhaust the page cache or even the whole system memory. Instead, we propose a new technique, named memory ambush. It is able to place the hammerable double-owned kernel buffers physically adjacent to the target objects (e.g., page tables) with only a small amount of memory. As a result, our exploit is stealthier and has fewer memory footprints. We also replace the inefficient rowhammer algorithm that blindly picks up addresses to hammer with an efficient one. Our algorithm selects suitable addresses based on an existing timing channel.Comment: Preprint of the work accepted at the IEEE Transactions on Dependable and Secure Computing 201

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