Towards a Fault-Injection Benchmarking Suite for Evaluating Fault Tolerance Mechanisms

Core Concepts

The research community lacks a dedicated benchmarking suite for evaluating fault tolerance (FT) mechanisms and fault-injection (FI) frameworks, leading to limited comparability, overlapping benchmarks, and suboptimal configurability.

Abstract

The paper discusses the need for a dedicated benchmarking suite for the fault tolerance (FT) and fault-injection (FI) research domain. Currently, researchers often resort to using benchmarking suites from other domains, such as TACLeBench for worst-case execution time (WCET) research or MiBench for embedded systems, which are not optimized for FT/FI evaluation. The authors propose several preferable properties for an FT/FI benchmarking suite: Different Granularities: The suite should include benchmarks at both the isolated algorithm level and the integrated system level to enable targeted and realistic evaluations. Relevant Benchmark Selection: Benchmarks should be categorized based on their program characteristics (e.g., memory usage, runtime, fault space) and application domains to avoid redundancy and enable representative experiment setups. Resource-Efficient Fault Injection: The suite should be designed with heavy FI evaluations in mind, featuring a lightweight infrastructure and configurability to adjust the fault space size as needed. Self-contained Runtime: The benchmarking suite should be self-contained and portable, reducing dependencies on external runtime support and improving comparability across studies. The authors provide a preliminary evaluation demonstrating the potential benefits of such an FT/FI-specific benchmarking suite. They show that the same MiBench benchmarks can exhibit vastly different fault space characteristics when compiled with different runtimes, highlighting the need for a dedicated suite.

Stats

The number of dynamic instructions for the benchmarks ranges from 10^3 to 10^7. The number of unique memory access locations ranges from 10^2 to 10^6. The Silent Data Corruption (SDC) count varies significantly across the benchmarks.

Quotes

"To test their FT and FI mechanisms, researchers usually resort to benchmarking suites from other domains such as TACLeBench [1] for Worst-Case Execution Time (WCET) research or MiBench [2] for embedded systems. These benchmarking suites are designed for different purposes and metrics than those relevant in the FT/FI domain." "Notably the eCos variant of crc has 97% more SDCs, 801% more timeouts and 1491% more CPU exceptions compared to the picolibc variant."

Key Insights Distilled From

Towards a Fault-Injection Benchmarking Suite

by Tianhao Wang... at arxiv.org 04-01-2024

https://arxiv.org/pdf/2403.20319.pdf

Deeper Inquiries

What specific program characteristics and metrics would be most relevant for evaluating fault tolerance mechanisms and fault-injection frameworks?

In evaluating fault tolerance mechanisms and fault-injection frameworks, specific program characteristics and metrics play a crucial role. Some relevant metrics include the number of dynamic instructions, unique memory-access locations, Silent Data Corruption (SDC) count, stack and heap usage, branching behavior, and memory-access granularity. These metrics help in understanding the behavior of programs under fault conditions, identifying vulnerabilities, and assessing the effectiveness of fault tolerance mechanisms. For fault-injection frameworks, metrics related to fault space characteristics, runtime behavior, and resource usage are essential for comprehensive evaluation.

How can the proposed benchmarking suite be designed to ensure comprehensive coverage of the fault tolerance domain while maintaining a manageable size and complexity?

To ensure comprehensive coverage of the fault tolerance domain while managing size and complexity, the benchmarking suite should be designed with specific considerations. Firstly, including benchmarks in different granularities, such as isolated algorithm implementations and integrated systems, allows for targeted analysis and realistic use case demonstrations. Secondly, categorizing benchmarks based on program characteristics like memory usage, runtime, and fault space characteristics helps in avoiding redundant benchmarks and achieving a representative experiment setup. Additionally, the suite should prioritize resource-efficient fault injection, lightweight infrastructure, and self-contained runtime to enable large-scale fault-injection experiments without excessive computational overhead.

What are the potential challenges in developing and maintaining a dedicated fault tolerance benchmarking suite, and how can the research community collaborate to address them?

Developing and maintaining a dedicated fault tolerance benchmarking suite may pose several challenges. These challenges could include selecting relevant benchmarks, ensuring benchmark diversity, managing benchmark complexity, and maintaining benchmark relevance over time. To address these challenges, the research community can collaborate by establishing standardized criteria for benchmark selection, sharing benchmarking resources and tools, conducting benchmark validation studies, and regularly updating the benchmark suite to reflect advancements in fault tolerance research. Collaboration among researchers, industry partners, and standardization bodies can help in creating a robust and widely accepted fault tolerance benchmarking suite that benefits the entire research community.