$\varepsilon$-differential agreement: A Parallel Data Sorting Mechanism for Distributed Information Processing System

TL;DR

This paper introduces the -differential agreement (EDA), a novel parallel data sorting mechanism for distributed systems that uses collaborative consensus based on statistical principles, offering customizable fault tolerance and delay features.

Contribution

The paper presents a new collaborative consensus mechanism, EDA, for parallel data sorting in distributed systems, differing from traditional competition-based methods.

Findings

Variable fault-tolerant rates observed under different configurations

Consensus delay varies with system parameters

EDA enables flexible, multi-center decision clustering

Abstract

The order of the input information plays a very important role in a distributed information processing system (DIPS). This paper proposes a novel data sorting mechanism named the {\epsilon}-differential agreement (EDA) that can support parallel data sorting. EDA adopts the collaborative consensus mechanism which is different from the traditional consensus mechanisms using the competition mechanism, such as PoS, PoW, etc. In the system that employs the EDA mechanism, all participants work together to compute the order of the input information by using statistical and probability principles on a proportion of participants. Preliminary results show variable fault-tolerant rates and consensus delay for systems that have different configurations when reaching consensus, thus it suggests that it is possible to use EDA in a system and customize these parameters based on different requirements. With the unique mechanism, EDA can be used in DIPS of multi-center decision cluster, not just the rotating center decision cluster.