We present a method to locate faults in service-based software systems hosted on mobile ad hoc networks (MANETs). In such systems, computations are structured as interdependent services distributed across the network, collaborating to satisfy client requests. Faults, which may occur at either or both the service and network layers, propagate by cascading through some subset of the services, from their root causes back to the clients that initiate requests. Fault localization in this environment is especially challenging because the systems are typically subject to a wider variety and higher incidence of faults than those deployed in fixed networks, the resources available to collect and store analysis data are severely limited, and many of the sources of faults are by their nature transient. Our method makes use of service-dependence and fault data that are harvested in the network through decentralized, run-time observations of service interactions and fault symptoms. We have designed timingand Bayesian-based reasoning techniques to analyze the data in the context of a specific fault propagation model. The analysis provides a ranked list of candidate fault locations. Through extensive simulations, we evaluate the performance of our method in terms of its accuracy in correctly ranking root causes under a wide range of operational conditions.

The dependencies among the components of service-oriented software applications hosted in a mobile ad hoc network (MANET) are difficult to determine due to the inherent loose coupling of the services and the transient communication topologies of the network. Yet understanding these dependencies is critical to making good management decisions, since dependence data underlie important analyses such as fault localization and impact analysis. Current methods for discovering dependencies, developed primarily for fixed networks, assume that dependencies change only slowly and require relatively long monitoring periods as well as substantial memory and communication resources, all of which are impractical in the MANET environment. We describe a new dynamic dependence discovery method designed specifically for this environment, yielding dynamic snapshots of dependence relationships discovered through observations of service interactions. We evaluate the performance of our method in terms of the accuracy of the discovered dependencies, and draw insights on the selection of critical parameters under various operational conditions. Although operated under more stringent conditions, our method is shown to provide results comparable to or better than existing methods.

Fault localization in general refers to a technique for identifying the likely root causes of failures observed in systems formed from components. Fault localization in systems deployed on mobile ad hoc networks (MANETs) is a particularly challenging task because those systems are subject to a wider variety and higher incidence of faults than those deployed in fixed networks, the resources available to track fault symptoms are severely limited, and many of the sources of faults in MANETs are by their nature transient. We present a method for localizing the faults occurring in service-based systems hosted on MANETs. The method is based on the use of dependence data that are discovered dynamically through decentralized observations of service interactions. We employ both Bayesian and timing-based reasoning techniques to analyze the data in the context of a specific fault propagation model, deriving a ranked list of candidate fault locations. We present the results of an extensive set of experiments exploring a wide range of operational conditions to evaluate the accuracy of our method.