Emerging memory interconnect (such as CXL and HCCS) promises rack-scale machine to become a reality, as the interconnect enables load/store accessible memory shared across the entire rack. However, the rack-scale shared memory poses two unique challenges on the operating system, primarily because of synchronization bottleneck and reliability issue. First, hardware cache coherence is not guaranteed, thus existing lock-based approach is ineffective to synchronize cross-node memory access. Second, memory faults significantly increase, and additional interconnect hops and switches expand fault surface and radius. As a result, current systems cannot efficiently leverage in-rack shared memory and instead manage rack resource in a disaggregated way, suffering from unnecessary networking/RDMA transmission overhead and redundant data copies. This paper proposes FlacOS, a shared operating system for memory-interconnected rack-scale architecture. FlacOS fully exploits the scalability, elasticity, and capacity advantages of rack-scale machine through shared memory. FlacOS strategically extracts and places kernel data structures in the shared memory to achieve uniform and shared operating system functionalities within the rack. FlacOS co-designs lock-free synchronization algorithms and system-wide fault tolerance mechanism to simultaneously ensure high performance and reliability. Experiments using Redis on a physical 640-core rack machine illustrate that FlacOS achieves a latency reduction of 1.75-2.4 times compared to network-based solutions.
Cache systems are widely used to speed up data retrieving. Modern HPC, data analytics, and AI/ML workloads generate vast, multi-dimensional datasets, and those data are accessed via complex queries. However, the probability of requesting the exact same data across different queries is low, leading to limited performance improvement when a traditional key-value cache is applied. In this paper, we present MosaicCache, a proactive and general caching framework that enables applications with efficient partial overlapped data reuse through novel overlap-aware cache interfaces for fast content-level reuse. The core components include a metadata manager leveraging customizable indexing for fast overlap lookups, an adaptive fetch planner for dynamic cache-tostorage decisions, and an async merger to reduce cache fragmentation and redundancy. Evaluations on real-world HPC datasets show that Mosaic-Cache improves overall performance by up to 4.1x over traditional key-value-based cache while adding minimal overhead in worst-case scenarios.
Hazard monitoring systems rely on micro datacenters (MicroDCs) for local data processing and real-time response in resource- and energy-constrained environments. These MicroDCs often host diverse, multitenant applications-such as object detection and sensor data ingestion-that contend for shared memory. Through a case study of compute-intensive and I/O-intensive applications, we show that different applications use memory differently (e.g., heap vs. OS-managed page cache), leading to asymmetric performance degradation under memory pressure. Our findings highlight the limitations of existing OS-level resource management approaches and motivate the need for cross-layered coordination between applications and the operating system to treat all memory uses as first-class citizens and adapt to changing workload demands in MicroDCs.
SPDK, as one of the most efficient I/O storage software, is capable of delivering the lowest I/O latency. Unfortunately, the polling mechanism in SPDK wastes tremendous CPU clock cycles, especially under small I/O operations and low queue depths. Although SPDK supports the conventional interrupt method, it does not improve power efficiency under such circumstances. To address this issue, we propose SPDK+, which enables the user interrupt feature in the SPDK to achieve both low latency and high power efficiency. Specifically, SPDK+ employs user interrupt handling to directly process MSI-X interrupts from SSD devices and utilizes user wait instructions during IO wait periods to conserve power. The comprehensive evaluation results show that SPDK+ achieves up to 49.5% power efficiency improvement while keeping the I/O latency almost unchanged compared with SPDK.
In-memory caches play a critical role in storage environments by reducing data access latencies and loads on backend data stores. A cache's eviction policy significantly impacts its attained miss ratio, and recent modeling techniques allow for efficient evaluation of different eviction policies at runtime. However, modern in-memory caches lack the ability to switch between eviction policies at runtime, except for Redis that can only switch between LRU and LFU. We present PaperCache, an in-memory cache capable of switching between multiple different eviction policies at runtime. Our evaluation shows that immediately after an eviction policy switch, PaperCache's behavior closely mirrors that of a cache implementing the target policy exactly (with a miss ratio typically within 1%) for a short period of time, after which PaperCache's behavior is fully inline with an exact policy implementation. Further, PaperCache is able to periodically and automatically switch to the policy exhibiting the lowest miss ratio, reducing the overall miss ratio by up to 48.5%.
