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While machine learning has been adopted across various fields, its ability to outperform traditional heuristics in operating systems is often met with justified skepticism. Concerns about unsafe decisions, opaque debugging processes, and the challenges of integrating ML into the kernel—given its stringent latency constraints and inherent complexity — make practitioners understandably cautious. This paper introduces Guardrails for the OS, a framework that allows kernel developers to declaratively specify system-level properties and define corrective actions to address property violations. The framework facilitates the compilation of these guardrails into monitors capable of running within the kernel. In this work, we establish the foundation for Guardrails, detailing its core abstractions, examining the problem space, and exploring potential solutions. 

Interface resistance has become a significant bottleneck for solid-state batteries (SSBs). Most studies of interface resistance have focused on extrinsic mechanisms such as interface reactions and imperfect contact between electrodes and solid electrolytes. Interface potentials are an important intrinsic mechanism that is often ignored. Here, we highlight Kelvin probe force microscopy (KPFM) as a tool to image the local potential at interfaces inside SSBs, examining the existing literature and discussing challenges in interpretation. Drawing analogies with electron transport in metal/semiconductor interfaces, we showcase a formalism that predicts intrinsic ionic resistance based on the properties of the contacting phases, and we emphasize that future battery designs should start from material pairs with low intrinsic resistance. We conclude by outlining future directions in the study of interface potentials through both theory and experiment. 

Modern memory hierarchies are increasingly complex, with more memory types and richer topologies. Unfortunately kernel memory managers lack the extensibility that many other parts of the kernel use to support diversity. This makes it difficult to add and deploy support for new memory configurations, such as tiered memory: engineers must navigate and modify the monolithic memory management code to add support, and custom kernels are needed to deploy such support until it is upstreamed. We take inspiration from filesystems and note that VFS, the extensible interface for filesystems, supports a huge variety of filesystems for different media and different use cases, and importantly, has interfaces for memory management operations such as controlling virtual-to-physical mapping and handling page faults. We propose writing memory management systems as filesystems using VFS, bringing extensibility to kernel memory management. We call this idea File-Based Memory Management (FBMM). Using this approach, many recent memory management extensions, e.g., tiering support, can be written without modifying existing memory management code. We prototype FBMM in Linux to show that the overhead of extensibility is low (within 1.6%) and that it enables useful extensions. 

Persistent memory (PM) can be accessed directly from userspace without kernel involvement, but most PM filesystems still perform metadata operations in the kernel for secuity and rely on the kernel for cross-process synchronization. We present per-file virtualization, where a virtualization layer implements a complete set of file functionalities, including metadata management, crash consistency, and concurrency control, in userspace. We observe that not all file metadata need to be maintained by the kernel and propose embedding insensitive metadata into the file for userspace management. For crash consistency, copy-on-write (CoW) benefits from the embedding of the block mapping since the mapping can be efficiently updated without kernel involvement. For cross-process synchronization, we introduce lockfree optimistic concurrency control (OCC) at user level, which tolerates process crashes and provides better scalability. Based on per-file virtualization, we implement MadFS, a library PM filesystem that maintains the embedded metadata as a compact log. Experimental results show that on concurrent workloads, MadFS achieves up to 3.6× the throughput of ext4-DAX. For real-world applications, MadFS provides up to 48% speedup for YCSB on LevelDB and 85% for TPC-C on SQLite compared to NOVA. 

Persistent memory (PMem) is a low-latency storage technology connected to the processor memory bus. The Direct Access (DAX) interface promises fast access to PMem, mapping it directly to processes' virtual address spaces. However, virtual memory operations (e.g., paging) limit its performance and scalability. Through an analysis of Linux/x86 memory mapping, we find that current systems fall short of what hardware can provide due to numerous software inefficiencies stemming from OS assumptions that memory mapping is for DRAM. In this paper we propose DaxVM, a design that extends the OS virtual memory and file system layers leveraging persistent memory attributes to provide a fast and scalable DAX-mmap interface. DaxVM eliminates paging costs through pre-populated file page tables, supports faster and scalable virtual address space management for ephemeral mappings, performs unmappings asynchronously, bypasses kernel-space dirty-page tracking support, and adopts asynchronous block pre-zeroing. We implement DaxVM in Linux and the ext4 file system targeting x86-64 architecture. DaxVM mmap achieves 4.9× higher throughput than default mmap for the Apache webserver and up to 1.5× better performance than read system calls. It provides similar benefits for text search. It also provides fast boot times and up to 2.95× better throughput than default mmap for PMem-optimized key-value stores running on a fragmented ext4 image. Despite designed for direct access to byte-addressable storage, various aspects of DaxVM are relevant for efficient access to other high performant storage mediums. 

Persistent memory (PMem) is a low-latency storage technology connected to the processor memory bus. The Direct Access (DAX) interface promises fast access to PMem, mapping it directly to processes' virtual address spaces. However, virtual memory operations (e.g., paging) limit its performance and scalability. Through an analysis of Linux/x86 memory mapping, we find that current systems fall short of what hardware can provide due to numerous software inefficiencies stemming from OS assumptions that memory mapping is for DRAM. In this paper we propose DaxVM, a design that extends the OS virtual memory and file system layers leveraging persistent memory attributes to provide a fast and scalable DAX-mmap interface. DaxVM eliminates paging costs through pre-populated file page tables, supports faster and scalable virtual address space management for ephemeral mappings, performs unmappings asynchronously, bypasses kernel-space dirty-page tracking support, and adopts asynchronous block pre-zeroing. We implement DaxVM in Linux and the ext4 file system targeting x86-64 architecture. DaxVM mmap achieves 4.9× higher throughput than default mmap for the Apache webserver and up to 1.5× better performance than read system calls. It provides similar benefits for text search. It also provides fast boot times and up to 2.95× better throughput than default mmap for PMem-optimized key-value stores running on a fragmented ext4 image. Despite designed for direct access to byte-addressable storage, various aspects of DaxVM are relevant for efficient access to other high performant storage mediums. 

Persistent memory enables a new class of applications that have persistent in-memory data structures. Recoverability of these applications imposes constraints on the ordering of writes to persistent memory. But, the cache hierarchy and memory controllers in modern systems may reorder writes to persistent memory. Therefore, programmers have to use expensive flush and fence instructions that stall the processor to enforce such ordering. While prior efforts circumvent stalling on long latency flush instructions, these designs under-perform in large-scale systems with many cores and multiple memory controllers.We propose ASAP, an architectural model in which the hardware takes an optimistic approach by persisting data eagerly, thereby avoiding any ordering stalls and utilizing the total system bandwidth efficiently. ASAP avoids stalling by allowing writes to be persisted out-of-order, speculating that all writes will eventually be persisted. For correctness, ASAP saves recovery information in the memory controllers which is used to undo the effects of speculative writes to memory in the event of a crash.Over a large number of representative workloads, ASAP improves performance over current Intel systems by 2.3 on average and performs within 3.9% of an ideal system.