Technical Report: Benefits of Stabilization versus Rollback in Self-Stabilizing Graph-Based Applications on Eventually Consistent Key-Value Stores

TL;DR

This paper compares self-stabilization and rollback techniques for handling consistency violations in distributed graph algorithms running on eventually consistent key-value stores, analyzing their performance across various scenarios.

Contribution

It provides an empirical evaluation of stabilization versus rollback approaches in self-stabilizing graph algorithms on weakly consistent storage systems, highlighting their relative benefits.

Findings

Self-stabilization effectively recovers from arbitrary faults without rollback.

Rollback improves performance in certain fault scenarios but may introduce overhead.

Different graph problems and system conditions influence the effectiveness of each approach.

Abstract

In this paper, we evaluate and compare the performance of two approaches, namely self-stabilization and rollback, to handling consistency violating faults (\cvf) that occur when a self-stabilizing distributed graph-based program is executed on an eventually consistent key-value store. Consistency violating faults are caused by reading wrong values due to weaker level of consistency provided by the key-value store. One way to deal with these faults is to utilize rollback whereas another way is to rely on the property of self-stabilization that is expected to provide recovery from arbitrary states. We evaluate both these approaches in different case studies --planar graph coloring, arbitrary graph coloring, and maximal matching-- as well as for different problem dimensions such as input data characteristics, workload partition, and network latency. We also consider the effect of executing non-stabilizing algorithm with rollback with a similar stabilizing algorithm that does not utilize rollback.