QUANTAS: Quantitative User-friendly Adaptable Networked Things Abstract Simulator

TL;DR

QUANTAS is an abstract, user-friendly simulator designed for quick and architecture-independent performance analysis of distributed algorithms across various classes like blockchains and consensus.

Contribution

It introduces an accessible simulation framework that enables rapid evaluation of distributed algorithms without being affected by specific network or OS details.

Findings

Implemented and compared two algorithms from four classes

Demonstrated ease of use and versatility of QUANTAS

Provided performance insights for distributed algorithm categories

Abstract

We present QUANTAS: a simulator that enables quantitative performance analysis of distributed algorithms. It has a number of attractive features. QUANTAS is an abstract simulator, therefore, the obtained results are not affected by the specifics of a particular network or operating system architecture. QUANTAS allows distributed algorithms researchers to quickly investigate a potential solution and collect data about its performance. QUANTAS programming is relatively straightforward and is accessible to theoretical researchers. To demonstrate QUANTAS capabilities, we implement and compare the behavior of two representative examples from four major classes of distributed algorithms: blockchains, distributed hash tables, consensus, and reliable data link message transmission.