A Generic Efficient Biased Optimizer for Consensus Protocols

Consensus is one of the most fundamental distributed computing problems. In particular, it serves as a building block in many replication based fault-tolerant systems and in particular in multiple recent blockchain solutions. Depending on its exact variant and other environmental assumptions, solving consensus requires multiple communication rounds. Yet, there are known optimistic protocols that guarantee termination in a single communication round under favorable conditions. In this paper we present a generic optimizer than can turn any consensus protocol into an optimized protocol that terminates in a single communication round whenever all nodes start with the same predetermined value and no Byzantine failures occur (although node crashes are allowed). This is regardless of the network timing assumptions and additional oracle capabilities assumed by the base consensus protocol being optimized. In the case of benign failures, our optimizer works whenever the number of faulty nodes $f<n/2$. For Byzantine behavior, our optimizer's resiliency depends on the validity variant sought. In the case of classical validity, it can accommodate $f<n/4$ Byzantine failures. With the more recent external validity function assumption, it works whenever $f<n/3$. Either way, our optimizer only relies on oral messages, thereby imposing very light-weight crypto requirements

FireLedger: A High Throughput Blockchain Consensus Protocol

Blockchains are distributed secure ledgers to which transactions are issued continuously and each block of transactions is tightly coupled to its predecessors. Permissioned blockchains place special emphasis on transactions throughput. In this paper we present FireLedger, which leverages the iterative nature of blockchains in order to improve their throughput in optimistic execution scenarios. FireLedger trades latency for throughput in the sense that in FireLedger the last f + 1 blocks of each node's blockchain are considered tentative, i.e., they may be rescinded in case one of the last f + 1 blocks proposers was Byzantine. Yet, when optimistic assumptions are met, a new block is decided in each communication step, which consists of a proposer that sends only its proposal and all other participants are sending a single bit each. Our performance study demonstrates that in a single Amazon data-center, FireLedger running on 10 mid-range Amazon nodes obtains a throughput of up to 160K transactions per second for (typical Bitcoin size) 512 bytes transactions. In a 10 nodes Amazon geo-distributed setting with 512 bytes transactions, FireLedger obtains a throughput of 30K tps. Moreover, on higher end Amazon machines, FireLedger obtains $20$ better throughput than state of the art protocols like HotStuff and BFT-SMaRt, depending on the exact configuration.Comment: The name of the protocol was changed from TOY to FireLedger. Protocol presentation and related work sections were improved and some typos were fixe

Cost-Effective Data Feeds to Blockchains via Workload-Adaptive Data Replication

Feeding external data to a blockchain, a.k.a. data feed, is an essential task to enable blockchain interoperability and support emerging cross-domain applications, notably stablecoins. Given the data-intensive feeds in real life (e.g., high-frequency price updates) and the high cost in using blockchain, namely Gas, it is imperative to reduce the Gas cost of data feeds. Motivated by the constant-changing workloads in finance and other applications, this work focuses on designing a dynamic, workload-aware approach for cost effectiveness in Gas. This design space is understudied in the existing blockchain research which has so far focused on static data placement. This work presents GRuB, a cost-effective data feed that dynamically replicates data between the blockchain and an off-chain cloud storage. GRuB's data replication is workload-adaptive by monitoring the current workload and making online decisions w.r.t. data replication. A series of online algorithms are proposed that achieve the bounded worst-case cost in blockchain's Gas. GRuB runs the decision-making components on the untrusted cloud off-chain for lower Gas costs, and employs a security protocol to authenticate the data transferred between the blockchain and cloud. The overall GRuB system can autonomously achieve low Gas costs with changing workloads. We built a GRuB prototype functional with Ethereum and Google LevelDB, and supported real applications in stablecoins. Under real workloads collected from the Ethereum contract-call history and mixed workloads of YCSB, we systematically evaluate GRuB's cost which shows a saving of Gas by 10% ~ 74%, with comparison to the baselines of static data-placement.Comment: Blockchain storage replication, Data feed, GRuB, 20 pages, Middleware 202