The STRESS Method for Boundary-point Performance Analysis of End-to-end Multicast Timer-Suppression Mechanisms

TL;DR

This paper introduces a systematic method using fault-oriented test generation to automatically synthesize worst and best-case boundary scenarios for evaluating multicast protocol performance, addressing limitations of traditional random or intuition-based testing.

Contribution

The paper presents a novel FSM-based algorithm extended with timing semantics and virtual LAN modeling for automatic boundary-case scenario synthesis in multicast protocols.

Findings

Method effectively synthesizes worst-case scenarios automatically.

Simulation results show significant differences from average-case analysis.

Approach is scalable for complex protocol evaluation.

Abstract

Evaluation of Internet protocols usually uses random scenarios or scenarios based on designers' intuition. Such approach may be useful for average-case analysis but does not cover boundary-point (worst or best-case) scenarios. To synthesize boundary-point scenarios a more systematic approach is needed.In this paper, we present a method for automatic synthesis of worst and best case scenarios for protocol boundary-point evaluation. Our method uses a fault-oriented test generation (FOTG) algorithm for searching the protocol and system state space to synthesize these scenarios. The algorithm is based on a global finite state machine (FSM) model. We extend the algorithm with timing semantics to handle end-to-end delays and address performance criteria. We introduce the notion of a virtual LAN to represent delays of the underlying multicast distribution tree. The algorithms used in our method utilize implicit backward search using branch and bound techniques and start from given target events. This aims to reduce the search complexity drastically. As a case study, we use our method to evaluate variants of the timer suppression mechanism, used in various multicast protocols, with respect to two performance criteria: overhead of response messages and response time. Simulation results for reliable multicast protocols show that our method provides a scalable way for synthesizing worst-case scenarios automatically. Results obtained using stress scenarios differ dramatically from those obtained through average-case analyses. We hope for our method to serve as a model for applying systematic scenario generation to other multicast protocols.