LaKe: The Power of In-Network Computing

In-network computing accelerates applications natively running on the host by executing them within network devices. While in-network computing offers significant performance improvements, its limitations and design trade-offs have not been explored. To usefully and efficiently run applications within the network, we first need to understand the implications of their design. In this work we introduce LaKe, a Layered Key-Value Store design, running as an in-network application. LaKe is a scalable design, enabling the exploration of design decisions and their effect on throughput, latency and power efficiency. LaKe achieves full line rate throughput, while maintaining a latency of 1.1μs and better power efficiency than existing hardware based memcached designs.This work was supported by JSPS Research Fellowship and Keio University Research Grant for Young Researcher’s Program. This work was supported by JST CREST Grant Number JPMJCR1785, Japan. We acknowledge the support of the Leverhulme Trust (ECF-2016-289) and the Isaac Newton Trust

HovercRaft: Achieving Scalability and Fault-tolerance for microsecond-scale Datacenter Services

Cloud platform services must simultaneously be scalable, meet low tail latency service-level objectives, and be resilient to a combination of software, hardware, and network failures. Replication plays a fundamental role in meeting both the scalability and the fault-tolerance requirement, but is subject to opposing requirements: (1) scalability is typically achieved by relaxing consistency; (2) fault-tolerance is typically achieved through the consistent replication of state machines. Adding nodes to a system can therefore either in- crease performance at the expense of consistency, or increase resiliency at the expense of performance. We propose HovercRaft, a new approach by which adding nodes increases both the resilience and the performance of general-purpose state-machine replication. We achieve this through an extension of the Raft protocol that carefully eliminates CPU and I/O bottlenecks and load balances requests. Our implementation uses state-of-the-art kernel-bypass techniques, datacenter transport protocols, and in-network programmability to deliver up to 1 million operations/second for clusters of up to 9 nodes, linear speedup over unreplicated configuration for selected workloads, and a 4× speedup for the YCSBE-E benchmark running on Redis over an unreplicated deployment