Fault Injection Techniques and Tools

  • Authors:

  • Mei-Chen Hsueh;Timothy K. Tsai;Ravishankar K. Iyer

  • Affiliations:

  • -;-;-

  • Venue:
  • Computer
  • Year:
  • 1997

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Abstract

Dependability evaluation involves the study of failures and errors. The destructive nature of a crash and long error latency make it difficult to identify the causes of failures in the operational environment. It is particularly hard to recreate a failure scenario for a large, complex system. To identify and understand potential failures, the authors use an experiment-based approach for studying system dependability. This approach is applied during the conception, design, prototype, and operational phases. To take an experiment-based approach, you must first understand a system's architecture, structure, and behavior. You need to know its tolerance for faults and failures, including its built-in detection and recovery mechanisms,and you need specific instruments and tools to inject faults, create failures or errors, and monitor their effects. Engineers most often use low-cost, simulation-based fault injection to evaluate the dependability of a system that is in the conceptual and design phases. At this point, the system under study is only a series of high-level abstractions; implementation details have yet to be determined. Thus the system is simulated on the basis of simplified assumptions. Simulation-based fault injection, which assumes that errors or failures occur according to predetermined distribution, is useful for evaluating the effectiveness of fault-tolerant mechanisms and a system's dependability; it does provide timely feedback to system engineers. However, it requires accurate input parameters, which are difficult to supply: Design and technology changes often complicate the use of past measurements. Testing a prototype, on the other hand, allows you to evaluate the system without any assumptions about system design. Instead of injecting faults, engineers can directly measure operational systems as they handle real workloads.Measurement-based analysis uses actual data, which contains much information about naturally occurring errors and failures and sometimes about recovery attempts. Although these three experimental methods have limitations, their unique values complement one another and allow for a wide spectrum of dependability studies.