Towards Simulation and Emulation of Large-Scale Computer Networks

Developing analytical models that can accurately describe behaviors of Internet-scale networks is difficult. This is due, in part, to the heterogeneous structure, immense size and rapidly changing properties of today\u27s networks. The lack of analytical models makes large-scale network simulation an indispensable tool for studying immense networks. However, large-scale network simulation has not been commonly used to study networks of Internet-scale. This can be attributed to three factors: 1) current large-scale network simulators are geared towards simulation research and not network research, 2) the memory required to execute an Internet-scale model is exorbitant, and 3) large-scale network models are difficult to validate. This dissertation tackles each of these problems. First, this work presents a method for automatically enabling real-time interaction, monitoring, and control of large-scale network models. Network researchers need tools that allow them to focus on creating realistic models and conducting experiments. However, this should not increase the complexity of developing a large-scale network simulator. This work presents a systematic approach to separating the concerns of running large-scale network models on parallel computers and the user facing concerns of configuring and interacting with large-scale network models. Second, this work deals with reducing memory consumption of network models. As network models become larger, so does the amount of memory needed to simulate them. This work presents a comprehensive approach to exploiting structural duplications in network models to dramatically reduce the memory required to execute large-scale network experiments. Lastly, this work addresses the issue of validating large-scale simulations by integrating real protocols and applications into the simulation. With an emulation extension, a network simulator operating in real-time can run together with real-world distributed applications and services. As such, real-time network simulation not only alleviates the burden of developing separate models for applications in simulation, but as real systems are included in the network model, it also increases the confidence level of network simulation. This work presents a scalable and flexible framework to integrate real-world applications with real-time simulation

Toward Distributed At-scale Hybrid Network Test with Emulation and Simulation Symbiosis

In the past decade or so, significant advances were made in the field of Future Internet Architecture (FIA) design. Undoubtedly, the size of Future Internet will increase tremendously, and so will the complexity of its users’ behaviors. This advancement means most of future Internet applications and services can only achieve and demonstrate full potential on a large-scale basis. The development of network testbeds that can validate key design decisions and expose operational issues at scale is essential to FIA research. In conjunction with the development and advancement of FIA, cyber-infrastructure testbeds have also achieved remarkable progress. For meaningful network studies, it is indispensable to utilize cyber-infrastructure testbeds appropriately in order to obtain accurate experiment results. That said, existing current network experimentation is intrinsically deficient. The existing testbeds do not offer scalability, flexibility, and realism at the same time. This dissertation aims to construct a hybrid system of conducting at-scale network studies and experiments by exploiting the distributed computing ability of current testbeds. First, this work presents a synchronization of parallel discrete event simulation that offers the simulation with transparent scalability and performance on various high-end computing platforms. The parallel simulator that we implement is configured so that it can self-adapt for the performance while running on supercomputers with disparate architectures. The simulator could be used to handle models of different sizes, varying modeling details, and different complexity levels. Second, this works addresses the issue of researching network design and implementation realistically at scale, through the use of distributed cyber-infrastructure testbeds. An existing symbiotic approach is applied to integrate emulation with simulation so that they can overcome the limitations of physical setup. The symbiotic method is used to improve the capabilities of a specific emulator, Mininet. In this case, Mininet can be used to run applications directly on the virtual machines and software switches, with network connectivity represented by detailed simulation at scale. We also propose a method for using the symbiotic approach to coordinate separate Mininet instances, each representing a different set of the overlapping network flows. This approach provides a significant improvement to the scalability of the network experiments

Traffic matrix estimation with enhanced origin destination generator algorithm using simulation of real network

The rapid growth of the Internet has made the issue of ensuring reliability and redundancy a big challenge. Studies of these issues using Traffic Engineering and simulation have been extensively done. In Traffic Matrix Estimation (TME), the Origin–Destination Generator algorithm (ODGen) is limited to the number of hops, where the Expectation Maximization (EM) accuracy is 92%. Most studies have not taken into account real traffic parameters and integration of TME models with routing protocols in their simulation models. Also, there is no a comprehensive model consisting of TME, Border Gateway Protocol (BGP) and Hot Potato (HP) routing in the NS-2 network simulator based on real networks. In this research, Integrated Simulated Model (ISM) is introduced consisting of ODGen-HP algorithm and BGP integrated into the NS-2 network simulator. ISM is then used to simulate the infrastructure of a real production network using actual captured traffic data parameters. Validation is then done against the changes in network topology based on packet loss, delay and throughput. Results gave the average error for packet sent by simulated and production networks of 0% and the average error for packet received by simulation and production networks of 3.61%. The network is modelled with a baseline topology where 5 main nodes were connected together, with redundant links for some nodes. The simulations were repeated for link failures, node addition, and node removal. TME used in ISM is based on ODGen, that is optimized with unlimited number of hops, the accuracy of EM increases to 97% and Central Processing Unit complexity is reduced. HP helps in improving the node which experiences a link failure to select shorter distance route to egress router. In the case of a link failure, HP switching time between the links is 0.05 seconds. ISM performance was evaluated by comparing trace file before and after link failure or by adding nodes (up to 32) or removing nodes. The parameters used for comparison are the packets loss, delay and throughput. The ISM error percentage obtained for packets loss is 0.025%, delay 0.013% and throughput 0.003%

Une approche générique pour l'automatisation des expériences sur les réseaux informatiques

This thesis proposes a generic approach to automate network experiments for scenarios involving any networking technology on any type of network evaluation platform. The proposed approach is based on abstracting the experiment life cycle of the evaluation platforms into generic steps from which a generic experiment model and experimentation primitives are derived. A generic experimentation architecture is proposed, composed of an experiment model, a programmable experiment interface and an orchestration algorithm that can be adapted to network simulators, emulators and testbeds alike. The feasibility of the approach is demonstrated through the implementation of a framework capable of automating experiments using any combination of these platforms. Three main aspects of the framework are evaluated: its extensibility to support any type of platform, its efficiency to orchestrate experiments and its flexibility to support diverse use cases including education, platform management and experimentation with multiple platforms. The results show that the proposed approach can be used to efficiently automate experimentation on diverse platforms for a wide range of scenarios.Cette thèse propose une approche générique pour automatiser des expériences sur des réseaux quelle que soit la technologie utilisée ou le type de plate-forme d'évaluation. L'approche proposée est basée sur l'abstraction du cycle de vie de l'expérience en étapes génériques à partir desquelles un modèle d'expérience et des primitives d'expérimentation sont dérivés. Une architecture générique d'expérimentation est proposée, composée d'un modèle d'expérience générique, d'une interface pour programmer des expériences et d'un algorithme d'orchestration qui peux être adapté aux simulateurs, émulateurs et bancs d'essai de réseaux. La faisabilité de cette approche est démontrée par la mise en œuvre d'un framework capable d'automatiser des expériences sur toute combinaison de ces plateformes. Trois aspects principaux du framework sont évalués : son extensibilité pour s'adapter à tout type de plate-forme, son efficacité pour orchestrer des expériences et sa flexibilité pour permettre des cas d'utilisation divers, y compris l'enseignement, la gestion des plate-formes et l'expérimentation avec des plates-formes multiples. Les résultats montrent que l'approche proposée peut être utilisée pour automatiser efficacement l'expérimentation sur les plates-formes d'évaluation hétérogènes et pour un éventail de scénarios variés