Bizur is a consensus algorithm exposing a key-value interface. It is used by a distributed file-system that scales to 100s of servers, delivering millions of IOPS, both data and metadata, with consistent low-latency. Bizur is aimed for services that require strongly consistent state, but do not require a distributed log; for example, a distributed lock manager or a distributed service locator. By avoiding a distributed log scheme, Bizur outperforms distributed log based consensus algorithms, producing more IOPS and guaranteeing lower latencies during normal operation and especially during failures. Paxos-like algorithms (e.g., Zab and Raft) which are used by existing distributed file-systems, can have artificial contention points due to their dependence on a distributed log. The distributed log is needed when replicating a general service, but when the desired service is key-value based, the contention points created by the distributed log can be avoided. Bizur does exactly that, by reaching consensus independently on independent keys. This independence allows Bizur to handle failures more efficiently and to scale much better than other consensus algorithms, allowing the file-system that utilizes Bizur to scale with it.
Consensus algorithms have attracted a lot of attention in areas requiring high-availability and fault tolerance. Mainly because they provide the ability to replicate a service across multiple servers, in a way that allows the service to continue working when some of the servers fail. This scheme of using consensus to achieve a replicated service is called state machine replication'' (SMR) @cite_17 .
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We initiate the study of probabilistic quorum systems, a technique for providing consistency of replicated data with high levels of assurance despite the failure of data servers. We show that this technique offers effective load reduction on servers and high availability. We explore probabilistic quorum systems both for services tolerant of benign server failures and for services tolerant of arbitrary (Byzantine) ones. We also prove bounds on the server load that can be achieved with these techniques. A fault-scalable service can be configured to tolerate increasing numbers of faults without significant decreases in performance. The Query Update (Q U) protocol is a new tool that enables construction of fault-scalable Byzantine fault-tolerant services. The optimistic quorum-based nature of the Q U protocol allows it to provide better throughput and fault-scalability than replicated state machines using agreement-based protocols. A prototype service built using the Q U protocol outperforms the same service built using a popular replicated state machine implementation at all system sizes in experiments that permit an optimistic execution. Moreover, the performance of the Q U protocol decreases by only 36 as the number of Byzantine faults tolerated increases from one to five, whereas the performance of the replicated state machine decreases by 83 . Researchers have made great strides in improving the fault tolerance of both centralized and replicated systems against arbitrary (Byzantine) faults. However, there are hard limits to how much can be done with entirely untrusted components; for example, replicated state machines cannot tolerate more than a third of their replica population being Byzantine. In this paper, we investigate how minimal trusted abstractions can push through these hard limits in practical ways. We propose Attested Append-Only Memory (A2M), a trusted system facility that is small, easy to implement and easy to verify formally. A2M provides the programming abstraction of a trusted log, which leads to protocol designs immune to equivocation -- the ability of a faulty host to lie in different ways to different clients or servers -- which is a common source of Byzantine headaches. Using A2M, we improve upon the state of the art in Byzantine-fault tolerant replicated state machines, producing A2M-enabled protocols (variants of Castro and Liskov's PBFT) that remain correct (linearizable) and keep making progress (live) even when half the replicas are faulty, in contrast to the previous upper bound. We also present an A2M-enabled single-server shared storage protocol that guarantees linearizability despite server faults. We implement A2M and our protocols, evaluate them experimentally through micro- and macro-benchmarks, and argue that the improved fault tolerance is cost-effective for a broad range of uses, opening up new avenues for practical, more reliable services. Service replication distributes an application over many processes for tolerating faults, attacks, and misbehavior among a subset of the processes. The established state-machine replication paradigm inherently requires the application to be deterministic. This paper distinguishes three models for dealing with non-determinism in replicated services, where some processes are subject to faults and arbitrary behavior (so-called Byzantine faults): first, a modular approach that does not require any changes to the potentially non-deterministic application (and neither access to its internal data); second, a master-slave approach, in which ties are broken by a leader and the other processes validate the choices of the leader; and finally, a treatment of applications that use cryptography and secret keys. Cryptographic operations and secrets must be treated specially because they require strong randomness to satisfy their goals. The paper also introduces two new protocols. The first uses the modular approach for filtering out non-de -ter -min -istic operations in an application. It ensures that all correct processes produce the same outputs and that their internal states do not diverge. The second protocol implements cryptographically secure randomness generation with a verifiable random function and is appropriate for certain security models. All protocols are described in a generic way and do not assume a particular implementation of the underlying consensus primitive.