The advent of multicast and the growth and complexity of the Internet has complicated network protocol design and evaluation. Evaluation of Internet protocols usually uses random scenarios or scenarios based on designers' intuition. Such an 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. Our algorithms utilize implicit backward search using branch and bound techniques and start from given target events. 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 evaluation to other multicast protocols.
@cite_36 , a simulation-based stress testing framework based on heuristics was proposed. However, that method does not provide automatic topology generation, nor does it address performance issues. The VINT @cite_17 tools provide a framework for Internet protocols simulation. Based on the network simulator (NS) @cite_31 and the network animator (NAM) @cite_23 , VINT provides a library of protocols and a set of validation test suites. However, it does not provide a generic tool for generating these tests automatically. Work in this paper is complementary to such studies, and may be integrated with network simulation tools similar to our work in .
@cite_36 @cite_31 @cite_23 @cite_17
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Network researchers must test Internet protocols under varied conditions to determine whether they are robust and reliable. The paper discusses the Virtual Inter Network Testbed (VINT) project which has enhanced its network simulator and related software to provide several practical innovations that broaden the conditions under which researchers can evaluate network protocols. We propose a method for using simulation to analyze the robustness of multiparty (multicast-based) protocols in a systematic fashion. We call our method Systematic Testing of Robustness by Examination of Selected Scenarios (STRESS). STRESS aims to cut the time and effort needed to explore pathological cases of a protocol during its design. This paper has two goals: (1) to describe the method, and (2) to serve as a case study of robustness analysis of multicast routing protocols. We aim to offer design tools similar to those used in CAD and VLSI design, and demonstrate how effective systematic simulation can be in studying protocol robustness. We present a variety of new architectural features and training procedures that we apply to the generative adversarial networks (GANs) framework. We focus on two applications of GANs: semi-supervised learning, and the generation of images that humans find visually realistic. Unlike most work on generative models, our primary goal is not to train a model that assigns high likelihood to test data, nor do we require the model to be able to learn well without using any labels. Using our new techniques, we achieve state-of-the-art results in semi-supervised classification on MNIST, CIFAR-10 and SVHN. The generated images are of high quality as confirmed by a visual Turing test: our model generates MNIST samples that humans cannot distinguish from real data, and CIFAR-10 samples that yield a human error rate of 21.3 . We also present ImageNet samples with unprecedented resolution and show that our methods enable the model to learn recognizable features of ImageNet classes. The simulation of product behavior is a vital part in virtual product development, but currently there is no tool or method available that can examine the quality of FE simulations and decide automatically on whether a simulation is plausible or non-plausible. In the paper a method is presented that enables automatic plausibility checks on basis of empirical simulation datasets. Nodal simulation data is transformed to numerical arrays, of fixed size, using virtual spherical detector surfaces. Afterwards the arrays are used to train a Deep Convolutional Neural Network (AlexNet). The Neural Network can then be used for plausibility checks of FE simulations (structural mechanics). In a first application a Deep Convolutional Neural Network is trained with simulation data of a demonstrator part, the rail of speed inline skates. After the GPU training of the Neural Network, further simulations are evaluated with the net. These simulations were not part of the training data and are used to calculate the prediction quality of the Neural Network. This approach is to support development engineers during design accompanying FEA in virtual product development.