Browsing by Author "Arun, Balaji"

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    A Low-latency Consensus Algorithm for Geographically Distributed Systems
    Arun, Balaji (Virginia Tech, 2017-02-28)
    This thesis presents Caesar, a novel multi-leader Generalized Consensus protocol for geographically replicated systems. Caesar is able to achieve near-perfect availability, provide high performance - low latency and high throughput compared to the existing state-of-the- art, and tolerate replica failures. Recently, a number of state-of-the-art consensus protocols that implement the Generalized Consensus definition have been proposed. However, the major limitation of these existing approaches is the significant performance degradation when application workload produces conflicting requests. Caesar's main goal is to overcome this limitation by changing the way a fast decision is taken: its ordering protocol does not reject a fast decision for a client request if a quorum of nodes reply with different dependency sets for that request. It only switches to a slow decision if there is no chance to agree on the proposed order for that request. Caesar is able to achieve this using a combination of wait condition and logical time stamping. The effectiveness of Caesar is demonstrated through an evaluation study performed on Amazon's EC2 infrastructure using 5 geo-replicated sites. Caesar outperforms other multi-leader (e.g., EPaxos) competitors by as much as 1.7x in presence of 30% conflicting requests, and single-leader (e.g., Multi-Paxos) by as much as 3.5x. The protocol is also resistant to heavy client loads unlike existing protocols.
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    Scalable Byzantine State Machine Replication: Designs, Techniques, and Implementations
    Arun, Balaji (Virginia Tech, 2021-07-02)
    State machine replication (SMR) is one of the most widely studied and used methodology for building highly available distributed applications and services. SMR replicates a service across a set of computing hosts, and executes client operations on the replicas in an agreed- upon total order, ensuring linearizability of the replicated shared state. The problem of determining a total order reduces to one of computing consensus. State-of-the-art consensus protocols are inadequate for newer classes of applications such as Blockchains and for geographically distributed infrastructures. The widely used Crash Fault Tolerance (CFT) fault model of consensus protocols is prone to malicious and adversarial behaviors as well as non-crash faults such as software bugs. The Byzantine fault-tolerance (BFT) model and its trust-based variant, the hybrid model, permit stronger failure adversaries. However, state-of-the-art Byzantine and hybrid consensus protocols have performance limitations in geographically distributed environments: they designate a primary replica for proposing total-orders, which becomes a bottleneck and yields sub-optimal latencies for faraway clients. Additionally, they do not scale to hundreds of replicas and provide consistent performance as the system size grows. To overcome these limitations and develop highly scalable SMR solutions, this dissertation presents two leaderless consensus protocols, namely ezBFT and Dester, for the Byzantine and hybrid models, respectively. These protocols enable every replica to receive and order client commands. Additionally, they exchange command dependencies to collectively order commands without relying on a primary. Our experimental evaluations in a 7-node geographically distributed setup reveals that ezBFT improves client-side latency by as much as 40% over state-of-the-art BFT protocols including PBFT, FaB, and Zyzzyva. Dester, for the hybrid model, reduces latency by as much as 30% over ezBFT. Next, the dissertation presents a new paradigm called DQBFT for designing consensus protocols that can scale to hundreds of nodes in geographically distributed environments. Since leaderless protocols exchange command dependencies, they do not scale to hundreds of nodes. DQBFT overcomes this scalability limitation by decentralizing only the heavy task of replicating commands and centralizing the process of ordering the commands. While DQBFT can be used to enhance existing primary-based protocols, Destiny is a hybrid instantiation of the DQBFT paradigm using linear communication for better scalability than naive instantiations. Experimental evaluations in a 193-node geographically distributed setup reveal that Destiny achieves ≈ 3× better throughput and ≈50% better latency than state-of-the-art BFT protocols including Hotstuff, SBFT, and Hybster. Lastly, the dissertation presents two techniques for designing and implementing BFT protocols with reduced development costs. The dissertation presents Bumblebee, a methodology for manually transforming CFT protocols to tolerate Byzantine faults using trusted execution environments that are increasingly available in commodity hardware. Bumblebee is based on the observation that CFT protocols are incapable of tolerating non-malicous non-crash faults, but they are nevertheless deployed in many production systems. Bumblebee provides a Generic Algorithm that can represent protocols in both CFT and hybrid fault models, thus allowing easy construction of hybrid protocols using CFT protocols as baselines. The dissertation constructs hybrid instantiations of CFT protocols including Paxos, Raft, and M2Paxos. Experimental evaluations of the hybrid variants reveal that they perform at par with native hybrid protocols, but incur a 30% overhead over their CFT counterparts. Hybrid protocols rely on the integrity of trusted execution environments, which are increasingly subject to security exploits. To withstand exploits, the dissertation presents DuoBFT, a protocol that exposes both the BFT and hybrid fault models within a single consensus protocol. This enables consensus under both fault models within the same protocol and without additional redundancy, allowing DuoBFT to achieve the performance of hybrid protocols and the security of BFT protocols. Experimental evaluations reveal that DuoBFT achieves the best of both hybrid and BFT fault models with less than 10% overhead.
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    Taming the Contention in Consensus-Based Distributed Systems
    Arun, Balaji; Peluso, Sebastiano; Palmieri, Roberto; Losa, Giuliano; Ravindran, Binoy (IEEE, 2021-11-01)
    Contention plays a crucial role in the design of consensus protocols. State-of-the-art solutions optimize their performance for either very low or high contention situations. We propose Caesar, a novel multi-leader Generalized Consensus protocol, most suitable for geographical replication, that is optimized for low-to-moderate contention. With an evaluation study, we show that Caesar outperforms other multi-leader (e.g., EPaxos) and single-leader (e.g., Multi-Paxos) competitors by up to 1.7x and 3.5x, respectively, in the presence of 30 percent conflicting requests, in a geo-replicated setting. Furthermore, we acknowledge that there is no one-size-fits- all consensus solution, especially for all levels of contentious workloads. Thus, we also propose Spectrum, a consensus framework that is able to switch consensus protocols at runtime to enable a dynamic reaction to changes in the workload and deployment characteristics. We show empirically that Spectrum can guarantee high availability even during periods of transition between consensus protocols.