Piotr Rygielski

• Nowadays, data centers are becoming increasingly dynamic due to the common adoption of virtualization technologies. Systems can scale their capacity on demand by growing and shrinking their resources dynamically based on the current load. However, the complexity and performance of modern data centers is influenced not only by the software architecture, middleware, and computing resources, but also by network virtualization, network protocols, network services, and configuration. The field of network virtualization is not as mature as serverNowadays, data centers are becoming increasingly dynamic due to the common adoption of virtualization technologies. Systems can scale their capacity on demand by growing and shrinking their resources dynamically based on the current load. However, the complexity and performance of modern data centers is influenced not only by the software architecture, middleware, and computing resources, but also by network virtualization, network protocols, network services, and configuration. The field of network virtualization is not as mature as server virtualization and there are multiple competing approaches and technologies. Performance modeling and prediction techniques provide a powerful tool to analyze the performance of modern data centers. However, given the wide variety of network virtualization approaches, no common approach exists for modeling and evaluating the performance of virtualized networks. The performance community has proposed multiple formalisms and models for evaluating the performance of infrastructures based on different network virtualization technologies. The existing performance models can be divided into two main categories: coarse-grained analytical models and highly-detailed simulation models. Analytical performance models are normally defined at a high level of abstraction and thus they abstract many details of the real network and therefore have limited predictive power. On the other hand, simulation models are normally focused on a selected networking technology and take into account many specific performance influencing factors, resulting in detailed models that are tightly bound to a given technology, infrastructure setup, or to a given protocol stack. Existing models are inflexible, that means, they provide a single solution method without providing means for the user to influence the solution accuracy and solution overhead. To allow for flexibility in the performance prediction, the user is required to build multiple different performance models obtaining multiple performance predictions. Each performance prediction may then have different focus, different performance metrics, prediction accuracy, and solving time. The goal of this thesis is to develop a modeling approach that does not require the user to have experience in any of the applied performance modeling formalisms. The approach offers the flexibility in the modeling and analysis by balancing between: (a) generic character and low overhead of coarse-grained analytical models, and (b) the more detailed simulation models with higher prediction accuracy. The contributions of this thesis intersect with technologies and research areas, such as: software engineering, model-driven software development, domain-specific modeling, performance modeling and prediction, networking and data center networks, network virtualization, Software-Defined Networking (SDN), Network Function Virtualization (NFV). The main contributions of this thesis compose the Descartes Network Infrastructure (DNI) approach and include: • Novel modeling abstractions for virtualized network infrastructures. This includes two meta-models that define modeling languages for modeling data center network performance. The DNI and miniDNI meta-models provide means for representing network infrastructures at two different abstraction levels. Regardless of which variant of the DNI meta-model is used, the modeling language provides generic modeling elements allowing to describe the majority of existing and future network technologies, while at the same time abstracting factors that have low influence on the overall performance. I focus on SDN and NFV as examples of modern virtualization technologies. • Network deployment meta-model—an interface between DNI and other meta- models that allows to define mapping between DNI and other descriptive models. The integration with other domain-specific models allows capturing behaviors that are not reflected in the DNI model, for example, software bottlenecks, server virtualization, and middleware overheads. • Flexible model solving with model transformations. The transformations enable solving a DNI model by transforming it into a predictive model. The model transformations vary in size and complexity depending on the amount of data abstracted in the transformation process and provided to the solver. In this thesis, I contribute six transformations that transform DNI models into various predictive models based on the following modeling formalisms: (a) OMNeT++ simulation, (b) Queueing Petri Nets (QPNs), (c) Layered Queueing Networks (LQNs). For each of these formalisms, multiple predictive models are generated (e.g., models with different level of detail): (a) two for OMNeT++, (b) two for QPNs, (c) two for LQNs. Some predictive models can be solved using multiple alternative solvers resulting in up to ten different automated solving methods for a single DNI model. • A model extraction method that supports the modeler in the modeling process by automatically prefilling the DNI model with the network traffic data. The contributed traffic profile abstraction and optimization method provides a trade-off by balancing between the size and the level of detail of the extracted profiles. • A method for selecting feasible solving methods for a DNI model. The method proposes a set of solvers based on trade-off analysis characterizing each transformation with respect to various parameters such as its specific limitations, expected prediction accuracy, expected run-time, required resources in terms of CPU and memory consumption, and scalability. • An evaluation of the approach in the context of two realistic systems. I evaluate the approach with focus on such factors like: prediction of network capacity and interface throughput, applicability, flexibility in trading-off between prediction accuracy and solving time. Despite not focusing on the maximization of the prediction accuracy, I demonstrate that in the majority of cases, the prediction error is low—up to 20% for uncalibrated models and up to 10% for calibrated models depending on the solving technique. In summary, this thesis presents the first approach to flexible run-time performance prediction in data center networks, including network based on SDN. It provides ability to flexibly balance between performance prediction accuracy and solving overhead. The approach provides the following key benefits: • It is possible to predict the impact of changes in the data center network on the performance. The changes include: changes in network topology, hardware configuration, traffic load, and applications deployment. • DNI can successfully model and predict the performance of multiple different of network infrastructures including proactive SDN scenarios. • The prediction process is flexible, that is, it provides balance between the granularity of the predictive models and the solving time. The decreased prediction accuracy is usually rewarded with savings of the solving time and consumption of resources required for solving. • The users are enabled to conduct performance analysis using multiple different prediction methods without requiring the expertise and experience in each of the modeling formalisms. The components of the DNI approach can be also applied to scenarios that are not considered in this thesis. The approach is generalizable and applicable for the following examples: (a) networks outside of data centers may be analyzed with DNI as long as the background traffic profile is known; (b) uncalibrated DNI models may serve as a basis for design-time performance analysis; (c) the method for extracting and compacting of traffic profiles may be used for other, non-network workloads as well.…
• Durch Virtualisierung werden moderne Rechenzentren immer dynamischer. Systeme sind in der Lage ihre Kapazität hoch und runter zu skalieren , um die ankommende Last zu bedienen. Die Komplexität der modernen Systeme in Rechenzentren wird nicht nur von der Softwarearchitektur, Middleware und Rechenressourcen sondern auch von der Netzwerkvirtualisierung beeinflusst. Netzwerkvirtualisierung ist noch nicht so ausgereift wie die Virtualisierung von Rechenressourcen und es existieren derzeit unterschiedliche Netzwerkvirtualisierungstechnologien.Durch Virtualisierung werden moderne Rechenzentren immer dynamischer. Systeme sind in der Lage ihre Kapazität hoch und runter zu skalieren , um die ankommende Last zu bedienen. Die Komplexität der modernen Systeme in Rechenzentren wird nicht nur von der Softwarearchitektur, Middleware und Rechenressourcen sondern auch von der Netzwerkvirtualisierung beeinflusst. Netzwerkvirtualisierung ist noch nicht so ausgereift wie die Virtualisierung von Rechenressourcen und es existieren derzeit unterschiedliche Netzwerkvirtualisierungstechnologien. Man kann aber keine der Technologien als Standardvirtualisierung für Netzwerke bezeichnen. Die Auswahl von Ansätzen durch Performanzanalyse von Netzwerken stellt eine Herausforderung dar, weil existierende Ansätze sich mehrheitlich auf einzelne Virtualisierungstechniken fokussieren und es keinen universellen Ansatz für Performanzanalyse gibt, der alle Techniken in Betracht nimmt. Die Forschungsgemeinschaft bietet verschiedene Performanzmodelle und Formalismen für Evaluierung der Performanz von virtualisierten Netzwerken an. Die bekannten Ansätze können in zwei Gruppen aufgegliedert werden: Grobetaillierte analytische Modelle und feindetaillierte Simulationsmodelle. Die analytischen Performanzmodelle abstrahieren viele Details und liefern daher nur beschränkt nutzbare Performanzvorhersagen. Auf der anderen Seite fokussiert sich die Gruppe der simulationsbasierenden Modelle auf bestimmte Teile des Systems (z.B. Protokoll, Typ von Switches) und ignoriert dadurch das große Bild der Systemlandschaft. ...…