The monolithic server model where a server is the unit of deployment, operation, and failure is meeting its limits in the face of several recent hardware and application trends. To improve resource utilization, elasticity, heterogeneity, and failure handling in datacenters, we believe that datacenters should break monolithic servers into disaggregated, network-attached hardware components. Despite the promising benefits of hardware resource disaggregation, no existing OSes or software systems can properly manage it.We propose a new OS model called the splitkernel to manage disaggregated systems. Splitkernel disseminates traditional OS functionalities into loosely-coupled monitors, each of which runs on and manages a hardware component. A splitkernel also performs resource allocation and failure handling of a distributed set of hardware components. Using the splitkernel model, we built LegoOS, a new OS designed for hardware resource disaggregation. LegoOS appears to users as a set of distributed servers. Internally, a user application can span multiple processor, memory, and storage hardware components. We implemented LegoOS on x86-64 and evaluated it by emulating hardware components using commodity servers. Our evaluation results show that LegoOS' performance is comparable to monolithic Linux servers, while largely improving resource packing and reducing failure rate over monolithic clusters.

/pdf/legoos-a-disseminated-distributed-os-for-hardware-resource-56a3bq4osk.pdf

LegoOS: a disseminated, distributed OS for hardware resource disaggregation

We report on experiences with Swift congestion control in Google datacenters. Swift targets an end-to-end delay by using AIMD control, with pacing under extreme congestion. With accurate RTT measurement and care in reasoning about delay targets, we find this design is a foundation for excellent performance when network distances are well-known. Importantly, its simplicity helps us to meet operational challenges. Delay is easy to decompose into fabric and host components to separate concerns, and effortless to deploy and maintain as a congestion signal while the datacenter evolves. In large-scale testbed experiments, Swift delivers a tail latency of

pdf/swift-delay-is-simple-and-effective-for-congestion-control-5gg2vpmr5h.pdf

Swift: Delay is Simple and Effective for Congestion Control in the Datacenter

Machine learning (ML) workflows are extremely complex. The typical workflow consists of distinct stages of user interaction, such as preprocessing, training, and tuning, that are repeatedly executed by users but have heterogeneous computational requirements. This complexity makes it challenging for ML users to correctly provision and manage resources and, in practice, constitutes a significant burden that frequently causes over-provisioning and impairs user productivity. Serverless computing is a compelling model to address the resource management problem, in general, but there are numerous challenges to adopt it for existing ML frameworks due to significant restrictions on local resources. This work proposes Cirrus---an ML framework that automates the end-to-end management of datacenter resources for ML workflows by efficiently taking advantage of serverless infrastructures. Cirrus combines the simplicity of the serverless interface and the scalability of the serverless infrastructure (AWS Lambdas and S3) to minimize user effort. We show a design specialized for both serverless computation and iterative ML training is needed for robust and efficient ML training on serverless infrastructure. Our evaluation shows that Cirrus outperforms frameworks specialized along a single dimension: Cirrus is 100x faster than a general purpose serverless system [36] and 3.75x faster than specialized ML frameworks for traditional infrastructures [49].

https://dl.acm.org/doi/pdf/10.1145/3357223.3362711

Cirrus: a Serverless Framework for End-to-end ML Workflows

Modern block-addressable NVMe SSDs provide much higher bandwidth and similar performance for random and sequential access. Persistent key-value stores (KVs) designed for earlier storage devices, using either Log-Structured Merge (LSM) or B trees, do not take full advantage of these new devices. Logic to avoid random accesses, expensive operations for keeping data sorted on disk, and synchronization bottlenecks make these KVs CPU-bound on NVMe SSDs. We present a new persistent KV design. Unlike earlier designs, no attempt is made at sequential access, and data is not sorted when stored on disk. A shared-nothing philosophy is adopted to avoid synchronization overhead. Together with batching of device accesses, these design decisions make for read and write performance close to device bandwidth. Finally, maintaining an inexpensive partial sort in memory produces adequate scan performance. We implement this design in KVell, the first persistent KV able to utilize modern NVMe SSDs at maximum bandwidth. We compare KVell against available state-of-the-art LSM and B tree KVs, both with synthetic benchmarks and production workloads. KVell achieves throughput at least 2x that of its closest competitor on read-dominated workloads, and 5x on write-dominated workloads. For workloads that contain mostly scans, KVell performs comparably or better than its competitors. KVell provides maximum latencies an order of magnitude lower than the best of its competitors, even on scan-based workloads.

KVell: the design and implementation of a fast persistent key-value store

Remote access to NVMe Flash enables flexible scaling and high utilization of Flash capacity and IOPS within a datacenter. However, existing systems for remote Flash access either introduce significant performance overheads or fail to isolate the multiple remote clients sharing each Flash device. We present ReFlex, a software-based system for remote Flash access, that provides nearly identical performance to accessing local Flash. ReFlex uses a dataplane kernel to closely integrate networking and storage processing to achieve low latency and high throughput at low resource requirements. Specifically, ReFlex can serve up to 850K IOPS per core over TCP/IP networking, while adding 21us over direct access to local Flash. ReFlex uses a QoS scheduler that can enforce tail latency and throughput service-level objectives (SLOs) for thousands of remote clients. We show that ReFlex allows applications to use remote Flash while maintaining their original performance with local Flash.

https://dl.acm.org/doi/pdf/10.1145/3093337.3037732

ReFlex: Remote Flash ≈ Local Flash

PCIe-based Flash is commonly deployed to provide datacenter applications with high IO rates. However, its capacity and bandwidth are often underutilized as it is difficult to design servers with the right balance of CPU, memory and Flash resources over time and for multiple applications. This work examines Flash disaggregation as a way to deal with Flash overprovisioning. We tune remote access to Flash over commodity networks and analyze its impact on workloads sampled from real datacenter applications. We show that, while remote Flash access introduces a 20% throughput drop at the application level, disaggregation allows us to make up for these overheads through resource-efficient scale-out. Hence, we show that Flash disaggregation allows scaling CPU and Flash resources independently in a cost effective manner. We use our analysis to draw conclusions about data and control plane issues in remote storage.

/pdf/flash-storage-disaggregation-3tts4xdmvm.pdf

Flash storage disaggregation

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Flash storage disaggregation