The NFTAPE is a software implemented, highly flexible fault injection environment for conducting automated fault/error injection-based dependability characterization. NFTAPE: (1) enables a user: (i) to specify a fault/error injection plan, (ii) to carry out injection experiments, and (iii) to collect the experimental results for analysis; (2) targets assessment of a broad set of dependability metrics, e.g., availability, reliability, coverage; (3) operates in a distributed environment; (4) can be configured to implement a variety of fault/error injection strategies and thus to serve multiple users and target systems; (5) imposes minimal disturbance of target systems.

This paper uses fault injection to compare the ability of two fault-tolerant software architectures to protect an application from faults. These two architectures are Voltan, which uses process duplication, and Chameleon ARMORs, which use self-checking. The target application is a Dynamic Host Configuration Protocol (DHCP) server, a widely used application for managing IP addresses. NFTAPE, a software-based fault injection environment, is used to inject three classes of faults, namely random memory bit-flip, control-flow and high-level target specific faults, into each software architecture and into baseline Solaris and Linux versions.

This paper presents a dependability study of high-speed, switched Local Area Networks (LANs) using Myrinet as an example testbed (with theoretical speeds of 2.56 Gbps). The study uses results of two fault injection methods, simulated fault injection and software-implemented fault injection (SWIFI), to analyze the application-level impact of transient faults injected into the network interface hardware. These results include a number of errors, such as dropped or corrupt messages, host interface or host resets, and local or remote host interface hangs. The paper presents the study in two parts: First, the results from the SWIFI method in the real system are used as a basis to validate the simulation and identify the major factors leading to differences between the methods. A comparison between the two injection methods shows that they agree for 83 percent of the fault injections. The results, however, vary greatly, depending on the fault type considered. The study also presents an analysis of the effects of varying workload intensity, host platform, and interface function targeted by the injection. An example of this analysis is to show that the function targeted has a significant impact on the fault activation rate. Finally, the study identifies two mechanisms by which faults may propagate from the interface to other parts of the network; in one example, this propagation caused the interface's host computer to reboot, while another caused a remote interface in the network to hang.