6 research outputs found
A Dual Digraph Approach for Leaderless Atomic Broadcast (Extended Version)
Many distributed systems work on a common shared state; in such systems,
distributed agreement is necessary for consistency. With an increasing number
of servers, these systems become more susceptible to single-server failures,
increasing the relevance of fault-tolerance. Atomic broadcast enables
fault-tolerant distributed agreement, yet it is costly to solve. Most practical
algorithms entail linear work per broadcast message. AllConcur -- a leaderless
approach -- reduces the work, by connecting the servers via a sparse resilient
overlay network; yet, this resiliency entails redundancy, limiting the
reduction of work. In this paper, we propose AllConcur+, an atomic broadcast
algorithm that lifts this limitation: During intervals with no failures, it
achieves minimal work by using a redundancy-free overlay network. When failures
do occur, it automatically recovers by switching to a resilient overlay
network. In our performance evaluation of non-failure scenarios, AllConcur+
achieves comparable throughput to AllGather -- a non-fault-tolerant distributed
agreement algorithm -- and outperforms AllConcur, LCR and Libpaxos both in
terms of throughput and latency. Furthermore, our evaluation of failure
scenarios shows that AllConcur+'s expected performance is robust with regard to
occasional failures. Thus, for realistic use cases, leveraging redundancy-free
distributed agreement during intervals with no failures improves performance
significantly.Comment: Overview: 24 pages, 6 sections, 3 appendices, 8 figures, 3 tables.
Modifications from previous version: extended the evaluation of AllConcur+
with a simulation of a multiple datacenters deploymen
Hermes: a Fast, Fault-Tolerant and Linearizable Replication Protocol
Today's datacenter applications are underpinned by datastores that are
responsible for providing availability, consistency, and performance. For high
availability in the presence of failures, these datastores replicate data
across several nodes. This is accomplished with the help of a reliable
replication protocol that is responsible for maintaining the replicas
strongly-consistent even when faults occur. Strong consistency is preferred to
weaker consistency models that cannot guarantee an intuitive behavior for the
clients. Furthermore, to accommodate high demand at real-time latencies,
datastores must deliver high throughput and low latency.
This work introduces Hermes, a broadcast-based reliable replication protocol
for in-memory datastores that provides both high throughput and low latency by
enabling local reads and fully-concurrent fast writes at all replicas. Hermes
couples logical timestamps with cache-coherence-inspired invalidations to
guarantee linearizability, avoid write serialization at a centralized ordering
point, resolve write conflicts locally at each replica (hence ensuring that
writes never abort) and provide fault-tolerance via replayable writes. Our
implementation of Hermes over an RDMA-enabled reliable datastore with five
replicas shows that Hermes consistently achieves higher throughput than
state-of-the-art RDMA-based reliable protocols (ZAB and CRAQ) across all write
ratios while also significantly reducing tail latency. At 5% writes, the tail
latency of Hermes is 3.6X lower than that of CRAQ and ZAB.Comment: Accepted in ASPLOS 202
Using Automated Performance Modeling to Find Scalability Bugs in Complex Codes
Many parallel applications suffer from latent performance limitations that may prevent them from scaling to larger machine sizes. Often, such scalability bugs manifest themselves only when an attempt to scale the code is actually being made—a point where remediation can be difficult. However, creating analytical performance models that would allow such issues to be pinpointed earlier is so laborious that application developers attempt it at most for a few selected kernels, running the risk of missing harmful bottlenecks. In this paper, we show how both coverage and speed of this scalability analysis can be substantially improved. Generating an empirical performance model automatically for each part of a parallel program, we can easily identify those parts that will reduce performance at larger core counts. Using a climate simulation as an example, we demonstrate that scalability bugs are not confined to those routines usually chosen as kernels
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