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ScaleDB is a serializable in-memory transactional database that achieves excellent scalability on multi-core machines by asynchronously updating range indexes. We find that asynchronous range index updates can significantly improve database scalability by applying updates in batches, reducing contention on critical sections. To avoid stale reads, ScaleDB uses small hash indexlets to hold delayed updates. We use indexlets to design ACC, an asynchronous concurrency control protocol providing serializability. With ACC, it is possible to delay range index updates without adverse performance effects on transaction execution in the common case. ACC delivers scalable serializable isolation for transactions, with high throughput and low abort rate. Evaluation on a dual-socket server with 36 cores shows that ScaleDB achieves 9.5× better query throughput than Peloton on the YCSB benchmark and 1.8× better transaction throughput than Cicada on the TPC-C benchmark. 

ScaleDB is a serializable in-memory transactional database that achieves excellent scalability on multi-core machines by asynchronously updating range indexes. We find that asynchronous range index updates can significantly improve database scalability by applying updates in batches, reducing contention on critical sections. To avoid stale reads, ScaleDB uses small hash indexlets to hold delayed updates. We use in- dexlets to design ACC, an asynchronous concurrency control protocol providing serializability. With ACC, it is possible to delay range index updates without adverse performance effects on transaction execution in the common case. ACC delivers scalable serializable isolation for transactions, with high throughput and low abort rate. Evaluation on a dual- socket server with 36 cores shows that ScaleDB achieves 9.5× better query throughput than Peloton on the YCSB bench- mark and 1.8× better transaction throughput than Cicada on the TPC-C benchmark. 

FlexTOE is a flexible, yet high-performance TCP offload engine (TOE) to SmartNICs. FlexTOE eliminates almost all host data-path TCP processing and is fully customizable. FlexTOE interoperates well with other TCP stacks, is robust under adverse network conditions, and supports POSIX sockets. FlexTOE focuses on data-path offload of established connections, avoiding complex control logic and packet buffering in the NIC. FlexTOE leverages fine-grained parallelization of the TCP data-path and segment reordering for high performance on wimpy SmartNIC architectures, while remaining flexible via a modular design. We compare FlexTOE on an Agilio-CX40 to host TCP stacks Linux and TAS, and to the Chelsio Terminator TOE. We find that Memcached scales up to 38% better on FlexTOE versus TAS, while saving up to 81% host CPU cycles versus Chelsio. FlexTOE provides competitive performance for RPCs, even with wimpy SmartNICs. FlexTOE cuts 99.99th-percentile RPC RTT by 3.2× and 50% versus Chelsio and TAS, respectively. FlexTOE's data-path parallelism generalizes across hardware architectures, improving single connection RPC throughput up to 2.4× on x86 and 4× on BlueField. FlexTOE supports C and XDP programs written in eBPF. It allows us to implement popular data center transport features, such as TCP tracing, packet filtering and capture, VLAN stripping, flow classification, firewalling, and connection splicing. 

It is becoming increasingly popular for distributed systems to exploit offload to reduce load on the CPU. Remote Direct Memory Access (RDMA) offload, in particular, has become popular. However, RDMA still requires CPU intervention for complex offloads that go beyond simple remote memory access. As such, the offload potential is limited and RDMA-based systems usually have to work around such limitations. We present RedN, a principled, practical approach to implementing complex RDMA offloads, without requiring any hardware modifications. Using self-modifying RDMA chains, we lift the existing RDMA verbs interface to a Turing complete set of programming abstractions. We explore what is possible in terms of offload complexity and performance with a commodity RDMA NIC. We show how to integrate these RDMA chains into applications, such as the Memcached key-value store, allowing us to offload complex tasks such as key lookups. RedN can reduce the latency of key-value get operations by up to 2.6× compared to state-of-the-art KV designs that use one-sided RDMA primitives (e.g., FaRM-KV), as well as traditional RPC-over-RDMA approaches. Moreover, compared to these baselines, RedN provides performance isolation and, in the presence of contention, can reduce latency by up to 35× while providing applications with failure resiliency to OS and process crashes. 

This paper demonstrates that it is possible to achieve µs-scale latency using Linux kernel storage stack, even when tens of latency-sensitive applications compete for host resources with throughput-bound applications that perform read/write operations at throughput close to hardware capacity. Furthermore, such performance can be achieved without any modification in applications, network hardware, kernel CPU schedulers and/or kernel network stack. We demonstrate the above using design, implementation and evaluation of blk-switch, a new Linux kernel storage stack architecture. The key insight in blk-switch is that Linux’s multi-queue storage design, along with multi-queue network and storage hardware, makes the storage stack conceptually similar to a network switch. blk-switch uses this insight to adapt techniques from the computer networking literature (e.g., multiple egress queues, prioritized processing of individual requests, load balancing, and switch scheduling) to the Linux kernel storage stack. blk-switch evaluation over a variety of scenarios shows that it consistently achieves µs-scale average and tail latency (at both 99th and 99.9th percentiles), while allowing applications to near-perfectly utilize the hardware capacity. 

This paper demonstrates that it is possible to achieve μs-scale latency using Linux kernel storage stack, even when tens of latency-sensitive applications compete for host resources with throughput-bound applications that perform read/write operations at throughput close to hardware capacity. Furthermore, such performance can be achieved without any modification in applications, network hardware, kernel CPU schedulers and/or kernel network stack. We demonstrate the above using design, implementation and evaluation of blk-switch, a new Linux kernel storage stack architecture. The key insight in blk-switch is that Linux's multi-queue storage design, along with multi-queue network and storage hardware, makes the storage stack conceptually similar to a network switch. blk-switch uses this insight to adapt techniques from the computer networking literature (e.g., multiple egress queues, prioritized processing of individual requests, load balancing, and switch scheduling) to the Linux kernel storage stack. blk-switch evaluation over a variety of scenarios shows that it consistently achieves μs-scale average and tail latency (at both 99th and 99.9th percentiles), while allowing applications to near-perfectly utilize the hardware capacity. 

This paper demonstrates that it is possible to achieve μs-scale latency using Linux kernel storage stack, even when tens of latency-sensitive applications compete for host resources with throughput-bound applications that perform read/write operations at throughput close to hardware capacity. Furthermore, such performance can be achieved without any modification in applications, network hardware, kernel CPU schedulers and/or kernel network stack. We demonstrate the above using design, implementation and evaluation of blk-switch, a new Linux kernel storage stack architecture. The key insight in blk-switch is that Linux's multi-queue storage design, along with multi-queue network and storage hardware, makes the storage stack conceptually similar to a network switch. blk-switch uses this insight to adapt techniques from the computer networking literature (e.g., multiple egress queues, prioritized processing of individual requests, load balancing, and switch scheduling) to the Linux kernel storage stack. blk-switch evaluation over a variety of scenarios shows that it consistently achieves μs-scale average and tail latency (at both 99th and 99.9th percentiles), while allowing applications to near-perfectly utilize the hardware capacity.