Data Freshness in Leader-Based Replicated Storage

TL;DR

This paper analyzes how the number of leaders in leader-based replicated storage affects data freshness, using Age of Information as a metric, and derives formulas to optimize the number of leaders for minimal data age.

Contribution

It provides a mathematical characterization of data freshness in leader-based storage systems and identifies the optimal number of leaders to minimize data age.

Findings

Optimal number of leaders depends on relative write speeds.

Closed-form expression for average age with exponential follower write times.

Numerical results show increasing leaders can improve freshness under certain conditions.

Abstract

Leader-based data replication improves consistency in highly available distributed storage systems via sequential writes to the leader nodes. After a write has been committed by the leaders, follower nodes are written by a multicast mechanism and are only guaranteed to be eventually consistent. With Age of Information (AoI) as the freshness metric, we characterize how the number of leaders affects the freshness of the data retrieved by an instantaneous read query. In particular, we derive the average age of a read query for a deterministic model for the leader writing time and a probabilistic model for the follower writing time. We obtain a closed-form expression for the average age for exponentially distributed follower writing time. Our numerical results show that, depending on the relative speed of the write operation to the two groups of nodes, there exists an optimal number of leaders which minimizes the average age of the retrieved data, and that this number increases as the relative speed of writing on leaders increases.