Virtualization and multi-tenancy are attractive paradigms to improve the utilization of computing infrastructures and hence to reduce costs. In order to provide a high degree of resource sharing without sacrificing predictable cloud application performance, strict performance isolation needs to be ensured. This is non-trivial and requires models which account for all components where applications may interfere: similarly to security, the predictability of cloud application performance can only be as good as the least predictable component in the model. This paper identifies a new source of potential performance interference that has been overlooked so far: the network hypervisor - a critical component in any multi-tenant network. We present a first measurement study of the performance implications of the network hypervisor in Software-Defined Networks (SDNs). For the purpose of our study, we developed a new open-source benchmarking tool for OpenFlow control and data planes. We show that cloud application performance may appear unpredictable if the network hypervisor is not accounted for: the performance does not only depend on the specific hypervisor implementation and workload (e.g., OpenFlow message types), but also on the number of tenants and the size of the network. Hence, our results suggest that hypervisors should be included in our performance models, and their performance benchmarked and compared similarly to other crucial software components such as the SDN controller.

Communication networks are the key enabling technology for our digital society. In order to sustain their critical services in the future, communication networks need to flexibly accommodate new requirements and changing contexts due to emerging diverse applications. In contrast to traditional networking technologies, software-oriented networking concepts, such as software-defined networking (SDN) and network function virtualization (NFV), provide ample opportunities for highly flexible network operations, enabling fast and simple adaptation of network resources and flows. This paper identifies the opportunities and challenges of adaptable softwarized networks and introduces a conceptual framework for adaptations in softwarized networks. We first explain how softwarized networks contribute to network adaptability through the functional primitives observation, composition, and control. We review the wide range of options for fine-granular observations as well as fine-granular composition and control provided by SDN and NFV. The multitude of fine-granular “tuning knobs” in adaptable softwarized networks complicates the decision making, which is the main focus of this paper. We propose to enhance the functional primitives observation, composition, and control with data-driven decision making, e.g., machine learning modules, resulting in deep observation, composition, and control. The data-driven decision making modules can learn and react to changes in the environment, e.g., new flow demands, so as to support meaningful decision making for adaptation in softwarized networks. Finally, we make the case for employing the concept of empowerment to realize truly “self-driving” networks.

The design of the 5G next-generation radio access network (NG-RAN) proposes the division of the next- generation eNodeB (gNB) into centralized and distributed units. Centralization should facilitate coordination of RAN functions between gNBs, but the actual benefits of it are still unclear. In this paper, we provide a study of the feasibility and benefits of coordinated downlink scheduling. We first analyze the time constraints that a coordinated scheduler has to face from a theoretical perspective, and we back them with an experimental proof-of-concept. Then, we present a lightweight scheme for coordinated link adaptation that matches the previous constraints. We show that coordination is indeed feasible with state- of-the-art technology, although very limited by time constraints. Finally, we show the results of our experimental testbed, which successfully implemented the described coordination scheme under the predicted constraints.

Software-Defined Networking (SDN) controllers are network entities that act as strategic control points in an SDN network. Controller placement studies mostly aim at optimizing network performance in terms of control latency, reliability and resilience, given network characteristics that are static. Yet dynamic traffic conditions, if not adapted by the controller placement properly, may cause high end-to-end flow setup time. For reactive controllers, the end-to-end flow setup time of a flow implies the difference between sending time at the source and receiving time at the sink of the first packet in that flow. Therefore, end-to-end flow setup time indicates the amount of time needed to set up forwarding rules in all involved switches and acts as a primary concern in terms of service establishment of network operators. In this paper, we analyze the controller placement for dynamic traffic flows based on a combined controller placement model: controller locations and switch-to-controller assignments are simultaneously optimized for minimum average flow setup time with respect to different traffic conditions inside the network. Linearization method is applied to transform the problem into a Mixed Integer Programming (MIP) problem which can be solved optimally. Two derivatives are also presented for comparison, one optimizing only controller locations and the other optimizing only switch-to-controller assignments. Our simulations cover two real network topologies and we explain the effects of the models have on the flow setup time with respect to dynamic flows. For low flow densities, the controller placement that adapts to flows could reduce the average flow setup time by about 50% compared to the static placement. However, when densities are high, the need of changing controller placement to guarantee flow setup performance is marginal.

With the rapid growth of user traffic, service innovation, and the persistent necessity to reduce costs, today's mobile operators are faced with several challenges. In networking, two concepts have emerged aiming at cost reduction, increase of network scalability and deployment flexibility, namely Network Functions Virtualization (NFV) and Software Defined Networking (SDN). NFV mitigates the dependency on hardware, where mobile network functions are deployed as software virtual network functions on commodity servers at cloud infrastructure, i.e., data centers. SDN provides a programmable and flexible network control by decoupling the mobile network functions into control plane and data plane functions. The design of the next generation mobile network (5G) requires new planning and dimensioning models to achieve a cost optimal design that supports a wide range of traffic demands. We propose three optimization models that aim at minimizing the network load cost as well as data center resources cost by finding the optimal placement of the data centers as well the SDN and NFV mobile network functions. The optimization solutions demonstrate the trade-offs between the different data center deployments, i.e., centralized or distributed, and the different cost factors, i.e., optimal network load cost or data center resources cost. We propose a Pareto optimal multi-objective model that achieves a balance between network and data center cost. Additionally, we use prior inference, based on the solutions of the single objectives, to pre-select data center locations for the multi-objective model that results in reducing the optimization complexity and achieves savings in run time while keeping a minimal optimality gap.

Software defined networking (SDN) network hypervisors provide the functionalities needed for virtualizing software-defined networks. Hypervisors sit logically between the multiple virtual SDN networks (vSDNs), which reside on the underlying physical SDN network infrastructure, and the corresponding tenant (vSDN) controllers. Different SDN network hypervisor architectures have mainly been explored through proof-of-concept implementations. We fundamentally advance SDN network hypervisor research by conducting a model-based analysis of SDN hypervisor architectures. Specifically, we introduce mixed integer programming formulations for four different SDN network hypervisor architectures. Our model formulations can also optimize the placement of multi-controller switches in virtualized OpenFlow-enabled SDN networks. We employ our models to quantitatively examine the optimal placement of the hypervisor instances. We compare the control plane latencies of the different SDN hypervisor architectures and quantify the cost of virtualization, i.e., the latency overhead due to virtualizing SDN networks via hypervisors. For generalization, we quantify how the hypervisor architectures behave for different network topologies. Our model formulations and the insights drawn from our evaluations inform network operators about the trade-offs of the different hypervisor architectures and help choosing an architecture according to operator demands.

Virtualization of software defined networks enables tenants to bring their own controller and manage their virtual resources with the full programmability provided by Software Defined Networking (SDN). Distributed SDN hypervisors are proposed to provide an efficient platform for the virtualization of physical SDN networks. They address the issue of scalability that a centralized hypervisor could suffer from. As virtualization provides the possibility to change virtual SDN networks on run time, a hypervisor layer needs efficient mechanisms to dynamically adapt to the changing requirements. Existing proposals provide only a static configuration setup for their distribution of the hypervisor instances. However, in order to satisfy the dynamics of virtual SDN networks, management protocols are needed to support dynamic changes. In this paper, we propose a control path migration protocol for distributed hypervisors. Such protocol is needed to support the dynamic adaptation of the virtualization layer. Our protocol is providing the missing procedure that allows a dynamic change of control connections between virtual SDN networks and the tenants' controllers, respectively. We provide a proof of concept implementation for our proposal. Through measurements in a real testbed setup, we show that our protocol is efficient in terms of control latency overhead and provides transparency to the controllers of the virtual SDN networks.

We present a demonstration of HyperFlex [1], a Software-Defined Networking (SDN) virtualization architecture with flexible hypervisor function allocation guaranteeing control-plane virtualization. Network Virtualization (NV) is expected to overcome the ossification of today's communication networks. SDN is seen as an enabler for programmable network control. In order to fully virtualize software-defined networks, not only the virtualization of the data-plane, but also the virtualization of the control-plane has to be considered. HyperFlex is a virtualization hypervisor that ensures isolated control-plane slices for virtual SDN networks. Control-plane isolation also protects the hypervisor resources from exhaustion. Furthermore, virtualization hypervisors have to be scalable and flexible in order to provide the best possible performance for the virtual software-defined networks. They should be able to adapt to the current state of the network and the divergent demands of virtual SDN networks. HyperFlex distributes the hypervisor functions flexibly and dynamically in order to adapt to the current network state.

Network Virtualization (NV) and Software-Defined Networking (SDN) are both expected to increase the flexibility and programmability of today's communication networks. Combining both approaches may even be a further step towards increasing the efficiency of network resource utilization. Multiple solutions for virtualizing SDN networks have already been proposed, however, they are either implemented in software or they require special network hardware. We propose HyperFlex, an SDN hypervisor architecture that relies on the decomposition of the hypervisor into functions that are essential for virtualizing SDN networks. The hypervisor functions can be flexibly executed in software or hosted on SDN network elements. Furthermore, existing hypervisor solutions focus on data-plane virtualization mechanisms and neglect the virtualization of the control-plane of SDN networks. HyperFlex provides control-plane virtualization by adding a control-plane isolation function, either in software or on network elements. The isolation function ensures that the resources of the control-plane are shared correctly between each virtual SDN network while it also protects the hypervisor resources from resource exhaustion.

Network virtualization is seen as a possible solution to the ossification problem of the Internet by providing flexible and scalable virtual networks running over heterogeneous physical infrastructure. As for today's networks, it is very crucial to evaluate the virtual networks' adaptability to fulfill the demanding quality requirements of the diverse service offers. In addition, such an environment should promote protection strategies to face the unpredictable network failures, which cause significant service interruptions and consequently lead to definite revenue losses. Therefore, we introduce Quality of Service (QoS) modeling in virtual networks and provide three novel QoS-aware, resilient and cost-optimal virtual network design solutions. Through comprehensive simulations we show the necessity for such QoS models in virtual networks, where a no-QoS model fails to meet the requirements of 76% of the services for some service distributions. Finally, we compare the performance of the three proposed solutions having QoS-guarantees and resilience at different virtualization layers and show that there is a trade-off between virtual network cost and network utilization.