QUANTAS 2 An Abstract, Concrete and Byzantine Simulator

2026-05-08

AI Summary

This work addresses the lack of a unified platform in existing research on distributed algorithms, which hinders seamless transitions among abstract modeling, real-world deployment, and Byzantine fault tolerance evaluation. The paper proposes an integrated simulation framework that uniquely combines round-based abstract simulation, socket-driven concrete execution, and composable Byzantine fault injection within a single system. This design enables flexible testing of diverse adversarial failures without modifying the underlying algorithmic code. Built upon a round-based execution model, socket-based communication, a JSON-based experiment description language, and modular fault-strategy interfaces, the framework has been successfully applied to case studies including PoW parasitic chain attack scanning, PBFT spoofing, Raft crash scenarios, and scalability experiments with Chord and Kademlia. These evaluations demonstrate its capability to ensure cross-mode consistency and support rigorous performance analysis.

Abstract

We present QUANTAS 2: a new distributed algorithm simulator and quantitative performance analysis tool. We use the original QUANTAS as a foundation. QUANTAS 2 can perform fast abstract exploration, concrete validation, and adversarial fault injection while preserving a compact implementation model for distributed algorithm researchers. The original QUANTAS was designed as an abstract, round-based simulator, which allows researchers to separate algorithmic behavior from the artifacts of a particular operating system, network stack, or physical deployment. QUANTAS 2 extends that design in two directions. First, QUANTAS 2 supports a concrete socket-based execution mode, allowing the same algorithm implementations and JSON experiment descriptions to run across local or distributed computers. Second, QUANTAS 2 adds a reusable Byzantine-fault interface in which Byzantine behavior is encoded as composable fault strategy that substitutes correct sends, receives, and local computation. This allows researchers to simulate crash, equivocation, selfish-mining, and other adversarial behaviors without rewriting the simulated algorithm. We demonstrate the resulting platform on blockchain, consensus, distributed hash table, and reliable data link algorithms. We perform parasite-chain sweeps for proof-of-work blockchains, PBFT equivocation experiments, Raft crash experiments, and Chord/Kademlia scale experiments over both abstract and concrete modes.

Problem

• distributed algorithm simulation
• Byzantine faults
• abstract and concrete execution
• fault injection
• performance analysis

Innovation

• distributed algorithm simulation
• Byzantine fault injection
• abstract-concrete execution
• composable fault strategy
• quantitative performance analysis