Scaling Blockchains and the Case for Ethereum

Abstract

A Confirmation Rule, within blockchain networks, refers to an algorithm implemented by network nodes that determines (either probabilistically or deterministically) the permanence of certain blocks on the blockchain. An example of Confirmation Rule is the Bitcoin's longest chain Confirmation Rule where a block b is confirmed (with high probability) when it has a sufficiently long chain of successors, its siblings have notably shorter successor chains, and network synchrony holds.In this work, we devise a Confirmation Rule for Ethereum's consensus protocol, Gasper. Initially, our focus is on developing a rule specifically for LMD-GHOST-the component of Gasper responsible for ensuring dynamic availability. This is done independently of the influence of FFG-Casper, which is designed to finalize the blocks produced by LMD-GHOST. Subsequently, we build upon this rule to consider FFG-Casper's impact, aiming to achieve fast block confirmations through a heuristic that balances confirmation speed with a trade-off in safety guarantees. This refined Confirmation Rule could potentially standardize fast block confirmation within Gasper. * Work done while at the Ethereum Foundation. † Work done while at Consensys.Gossiping. We assume that any honest validator immediately gossip (i.e., broadcast) any message that they receive.View. Thew view of a validator corresponds to the set of all the messages that the validator has received. More specifically, we use V v,t to denote the set of all messages received by validator v at time t. GasperGasper is a proof-of-stake consensus protocol made of two components [18], namely LMD-GHOST-HFC and FFG-Casper [7]. The former is a synchronous consensus protocol that works under dynamic participation and outputs a canonical chain, while the latter is a partially synchronous protocol, also referred to as finality gadget, whose role is to finalize blocks in the canonical chain and preserve safety of such finalized blocks during asynchronous periods. In the following, we summarise the concepts and properties pertaining to Gasper that are need in the remaining part of this work. Time and Slots. Time is organized into a consecutive sequence of slots. We denote the time at which a slot s begins with st(s), and use slot(t) to denote the slot associated with time t, i.e., slot(t) = s implies that t ∈ [st(s), st(s + 1)). Epochs. A sequence of E consecutive slots forms an epoch where E ≥ 2. Epochs are numbered starting from 0. We use first slot(e) and last slot(e) to denote the first slot and last slot of epoch e, respectively, i.e., first slot(e) := eE and last slot(e) := (e + 1)E − 1. We write epoch(s) for the epoch associated with slot s, i.e., epoch(s) = e implies s ∈ [first slot(e), last slot(e)]. Also we define epoch(t) := epoch(slot(t)). Finally, we let st(e) := st(first slot(e)).Validator Sets and Committees. According to the view of an honest validator v at time t, only a finite subset of all the validators are active for each epoch e. We denote such set as Ŵe,v,t and refer to it as the valdiat...

A Scalable Sharding Protocol Based on Cross-Shard Dynamic Transaction Confirmation for Alliance Chain in Intelligent Systems

“…The system proposed an additional set of nodes acting as coordinators, employing classic two-phase commit (2PC) and two-phase locking (2PL) protocols to handle cross-shard transactions. The application is limited by insufficient scalability and an unbalanced workload (Asgaonkar, 2022). Amiri et al (2019) proposed Sharper, a sharding protocol for permissioned blockchain.…”

Dynamic Transaction Confirmation Sharding Protocol for Alliance Chain

“…Dang proposed AHL (Attested HyperLedger) [36] to promote the sharding protocol to applications in permissioned environments beyond cryptocurrencies. The optimization of BFT (Byzantine Fault Tolerance) consensus in AHL reduces the maximum number of nodes required for a single shard to improve the throughput of large-scale permissioned chains, but the actual application is limited by insufficient scalability and an unbalanced workload [37]. Amiri proposes Sharper [7], which uses a hash-based sharding strategy and a BFT-based algorithm to ensure fast transaction distribution processing and process crossshard transactions through non-overlapping committees in parallel computing, showing excellent scalability and load balancing.…”