A Compute Express Link (CXL) pod is a group of hosts that share CXL-attached memory. A memory allocator for a CXL pod faces novel challenges: (1) CXL devices may not fully support inter-host hardware cache coherence (HWcc), (2) the allocator may be concurrently accessed from different processes, and (3) with more hosts, failures become more likely.

Current stream processing systems provide exactly-once semantics using checkpointing or a combination of logging and checkpointing. These approaches can introduce high overhead, significantly increasing the latency for normal stream processing because maintaining exactly-once semantics requires coordination across distributed nodes and streams to capture a globally consistent state. We observe that modern distributed shared logs offer a promising solution for maintaining exactly-once semantics with a small overhead. We propose Impeller, a stream processing system that uses a distributed shared log for data storage and exactly-once processing. To maintain exactly-once semantics, Impeller includes a novel and efficient progress marking protocol based on string tags and selective reads in a shared log. The key idea is to leverage the log's record-tagging feature to atomically mark progress across all streams. The experiments over the NEXMark benchmark show that Impeller achieves 1.3× to 5.4× lower p50 latency, or 1.3× to 5.0× higher saturation throughput than Kafka Streams.

Ease of use and transparent access to elastic resources have attracted many applications away from traditional platforms toward serverless functions. Many of these applications, such as machine learning, could benefit significantly from GPU acceleration. Unfortunately, GPUs remain inaccessible from serverless functions in modern production settings. We present DGSF, a platform that transparently enables serverless functions to use GPUs through general purpose APIs such as CUDA. DGSF solves provisioning and utilization challenges with disaggregation, serving the needs of a potentially large number of functions through virtual GPUs backed by a small pool of physical GPUs on dedicated servers. Disaggregation allows the provider to decouple GPU provisioning from other resources, and enables significant benefits through consolidation. We describe how DGSF solves GPU disaggregation challenges including supporting API transparency, hiding the latency of communication with remote GPUs, and load-balancing access to heavily shared GPUs. Evaluation of our prototype on six workloads shows that DGSF's API remoting optimizations can improve the runtime of a function by up to 50% relative to unoptimized DGSF. Such optimizations, which aggressively remove GPU runtime and object management latency from the critical path, can enable functions running over DGSF to have a lower end-to-end time than when running on a GPU natively. By enabling GPU sharing, DGSF can reduce function queueing latency by up to 53%. We use DGSF to augment AWS Lambda with GPU support, showing similar benefits.

We introduce TxFS, a transactional file system that builds upon a file system’s atomic-update mechanism such as journaling. Though prior work has explored a number of transactional file systems, TxFS has a unique set of properties: a simple API, portability across different hardware, high performance, low complexity (by building on the file-system journal), and full ACID transactions. We port SQLite, OpenLDAP, and Git to use TxFS and experimentally show that TxFS provides strong crash consistency while providing equal or better performance.

Users of modern data-processing services such as tax preparation or genomic screening are forced to trust them with data that the users wish to keep secret. Ryoan 1 protects secret data while it is processed by services that the data owner does not trust. Accomplishing this goal in a distributed setting is difficult, because the user has no control over the service providers or the computational platform. Confining code to prevent it from leaking secrets is notoriously difficult, but Ryoan benefits from new hardware and a request-oriented data model. Ryoan provides a distributed sandbox, leveraging hardware enclaves (e.g., Intel’s software guard extensions (SGX) [40]) to protect sandbox instances from potentially malicious computing platforms. The protected sandbox instances confine untrusted data-processing modules to prevent leakage of the user’s input data. Ryoan is designed for a request-oriented data model, where confined modules only process input once and do not persist state about the input. We present the design and prototype implementation of Ryoan and evaluate it on a series of challenging problems including email filtering, health analysis, image processing and machine translation.