Network function virtualization requires scaling and placement, deciding the number and the location of function instances. Current approaches are limited in flexibility and practical applicability. Specifically, we study dynamic, single-step, joint scaling and placement of network services with bidirectional flows traversing Physical or Virtual Network Functions (VNFs) and returning to their sources. We develop models to support stateful components and legacy network functions with fixed locations in these network services as well as the possibility of reusing VNFs across network services. We formalize the problem of jointly scaling and placing such network services as a mixed- integer linear program (MILP). We show that this problem is NP-complete and also present a heuristic algorithm to find good solutions in short time. In an extensive evaluation with realistic scenarios, we investigate the capabilities of the two approaches.

Dynamically steering flows through virtualized net- work function instances is a key enabler for elastic, on-demand deployments of virtualized network functions. This becomes par- ticular challenging when stateful functions are involved, necessi- tating state management. The problem with existing solutions is that they typically embrace state migration and flow rerouting jointly, imposing a huge set of requirements on the on-boarded VNFs, e.g., solution-specific state management interfaces. In this paper, we introduce the seamless handover proto- col (SHarP). It provides an easy-to-use, loss-less, and order- preserving flow rerouting mechanism that is not fixed to a single state management approach. This allows VNF vendors to implement or use the state management solution of their choice. SHarP supports these solutions with additional information when flows are migrated. Further, we show how SHarP significantly reduces the buffer usage at a central (SDN) controller, which is a typical bottleneck in existing solutions. Our experiments show that SHarP uses a constant amount of controller buffer, irrespective of the time taken to migrate the VNF state.

Developing a virtualized network service does not only involve the implementation and configuration of the network functions it is composed of but also its integration and test with management solutions that will control the service in its production environment. These integration tasks require testbeds that offer the needed network function virtualization infrastructure~(NFVI), like OpenStack, introducing a lot of management and maintenance overheads. Such testbed setups become even more complicated when the multi point-of-presence~(PoP) case, with multiple infrastructure installations, is considered. In this demo, we showcase an emulation platform that executes containerized network services in user-defined multi-PoP topologies. The platform does not only allow network service developers to locally test their services but also to connect real-world management and orchestration solutions to the emulated PoPs. During our interactive demonstration we focus on the integration between the emulated infrastructure and state-of-the-art orchestration solutions like SONATA or OSM.

Management and orchestration~(MANO) systems are the key components of future large-scale NFV environments. They will manage resources of hundreds or even thousands of NFV infrastructure installations, so called points of presence~(PoP). Such scenarios need to be automatically tested during the development phase of a MANO system. This task becomes very challenging because large-scale NFV testbeds are hard to maintain, too expensive, or simply not available. In this paper, we present a multi-PoP NFV infrastructure emulation platform that enables automated, large-scale testing of MANO stacks. We show that our platform can easily emulate hundreds of PoPs on a single physical machine and reduces the setup time of a test PoP by a factor of 232x compared to a DevStack-based test PoP installation. Further, we present a case study in which we test ETSI's Open Source MANO~(OSM) against our proposed system to gain insights about OSM's behaviour in large-scale NFV deployments.

Programmability, control and flexibility can be considered as some of the indirect enablers for the success of 5G technologies. A key driver towards this are mechanisms or methodologies to drive shorter time to market for suppliers and operators of virtual network functions (VNFs) and network services. 5GTANGO includes a DevOps approach that can be utilized for the validation and verification (V&V) of VNFs and network services. In this paper, we elaborate further on the approaches made in the areas of testing, catalogues and package management as a means to enable that full DevOps V&V workflow. Finally, we explore the deployment requirement of the V&V via one of our pilot use cases.

Dynamic allocation of resources is a key feature in network function virtualization (NFV), enabling flexible adjustment of slices and contained network services to ever-changing service demands. Considering resource allocation across the entire network, many authors have proposed approaches to optimize the placement and chaining of virtual network function (VNF) instances and the allocation of resources to these VNF instances. In doing so, various optimization objectives are conceivable, e.g., minimizing certain required resources or the end-to-end delay of the placed services. In this paper, we investigate the relationship between four typical optimization objectives when coordinating the placement and resource allocation of chained VNF instances. We observe an interesting trade-off between minimizing the overhead of starting/stopping VNF instances and all other objectives when adapting to changed service demands.

In recent years, a variety of different approaches have been proposed to tackle the problem of scaling and placing network services, consisting of interconnected virtual network functions (VNFs). This paper presents a placement abstraction layer (PAL) that provides a clear and simple northbound interface for using such algorithms while hiding their internal functionality and implementation. Through its southbound interface, PAL can connect to different back ends that evaluate the calculated placements, e.g., using simulations, emulations, or testbed approaches. As an example for such evaluation back ends, we introduce a novel placement emulation framework (PEF) that allows executing calculated placements using real, containerbased VNFs on real-world network topologies. In a case study, we show how PAL and PEF facilitate reusing and evaluating placement algorithms as well as validating their underlying models and performance claims.

A key challenge of network function virtualization (NFV) is the complexity of developing and deploying new network services. Currently, development requires many manual steps that are time-consuming and error-prone (e.g., for creating service descriptors). Furthermore, existing management and orchestration (MANO) platforms only offer limited support of standardized descriptor models or package formats, limiting the re-usability of network services. To this end, we introduce a fully integrated, open-source NFV service development kit (SDK) with multi-MANO platform support. Our SDK simplifies many NFV service development steps by offering initial generation of descriptors, advanced project management, as well as fully automated packaging and submission for on-boarding. To achieve multi-platform support, we present a package format that extends ETSI’s VNF package format. In this demonstration, we present the end-to-end workflow to develop an NFV service that is then packaged for multiple platforms, i.e., 5GTANGO and OSM.

For optimal placement and orchestration of network services, it is crucial that their structure and semantics are specified clearly and comprehensively and are available to an orchestrator. Existing specification approaches are either ambiguous or miss important aspects regarding the behavior of virtual network functions (VNFs) forming a service. We propose to formally and unambiguously specify the behavior of these functions and services using Queuing Petri Nets (QPNs). QPNs are an established method that allows to express queuing, synchronization, stochastically distributed processing delays, and changing traffic volume and characteristics at each VNF. With QPNs, multiple VNFs can be connected to complete network services in any structure, even specifying bidirectional network services containing loops. We discuss how management and orchestration systems can benefit from our clear and comprehensive specification approach, leading to better placement of VNFs and improved Quality of Service. Another benefit of formally specifying network services with QPNs are diverse analysis options, which allow valuable insights such as the distribution of end-to-end delay. We propose a tool-based workflow that supports the specification of network services and the automatic generation of corresponding simulation code to enable an in-depth analysis of their behavior and performance.

As 5G and network function virtualization (NFV) are maturing, it becomes crucial to demonstrate their feasibility and benefits by means of vertical scenarios. While 5GPPP has identified smart manufacturing as one of the most important vertical industries, there is still a lack of specific, practical use cases. Using the experience from a large-scale manufacturing company, Weidm{\"u}ller Group, we present a detailed use case that reflects the needs of real-world manufacturers. We also propose an architecture with specific network services and virtual network functions (VNFs) that realize the use case in practice. As a proof of concept, we implement the required services and deploy them on an emulation-based prototyping platform. Our experimental results indicate that a fully virtualized smart manufacturing use case is not only feasible but also reduces machine interconnection and configuration time and thus improves productivity by orders of magnitude.

5G together with software defined networking (SDN) and network function virtualisation (NFV) will enable a wide variety of vertical use cases. One of them is the smart man- ufacturing case which utilises 5G networks to interconnect production machines, machine parks, and factory sites to enable new possibilities in terms of flexibility, automation, and novel applications (industry 4.0). However, the availability of realistic and practical proof-of-concepts for those smart manufacturing scenarios is still limited. This demo fills this gap by not only showing a real-world smart manufacturing application entirely implemented using NFV concepts, but also a lightweight prototyping framework that simplifies the realisation of vertical NFV proof-of-concepts. Dur- ing the demo, we show how an NFV-based smart manufacturing scenario can be specified, on-boarded, and instantiated before we demonstrate how the presented NFV services simplify machine data collection, aggregation, and analysis.

Service Level Agreements are essential tools enabling clients and telco operators to specify required quality of service. The 5GTANGO NFV platform enables SLAs through policies and custom service lifecycle management components. This allows the operator to trigger certain lifecycle management events for a service, and the network service developer to define how to execute such events (e.g., how to scale). In this demo we will demonstrate this unique 5GTANGO concept using an elastic proxy service supported by a high availability SLA enforced through a range of traffic regimes.

More and more management and orchestration approaches for (software) networks are based on machine learning paradigms and solutions. These approaches depend not only on their program code to operate properly, but also require enough input data to train their internal models. However, such training data is barely available for the software networking domain and most presented solutions rely on their own, sometimes not even published, data sets. This makes it hard, or even infeasible, to reproduce and compare many of the existing solutions. As a result, it ultimately slows down the adoption of machine learning approaches in softwarised networks. To this end, we introduce the "softwarised network data zoo" (SNDZoo), an open collection of software networking data sets aiming to streamline and ease machine learning research in the software networking domain. We present a general methodology to collect, archive, and publish those data sets for use by other researches and, as an example, eight initial data sets, focusing on the performance of virtualised network functions.

Offloading packet processing tasks to programmable switches and/or to programmable network interfaces, so called “SmartNICs”, is one of the key concepts to prepare softwarized networks for the high traffic demands of the future. However, implementing network functions that make use of those offload- ing technologies is still challenging and usually requires the availability of specialized hardware. It becomes even harder if heterogeneous services, making use of different offloading and network virtualization technologies, should be developed. In this paper, we introduce FOP4 (Function Offloading Pro- totyping with P4), a novel prototyping platform that allows to prototype heterogeneous software network scenarios, including container-based, P4-switch-based, and SmartNIC-based network functions. The presented work substantially extends our existing Containernet platform with the means to prototype offloading scenarios. Besides presenting the platform’s system design, we evaluate its scalability and show that it can run scenarios with more than 64 P4 switch or SmartNIC nodes on a single laptop. Finally, we presented a case study in which we use the presented platform to prototype an extended in-band network telemetry use case.

Building on 5G and network function virtualization (NFV), smart manufacturing has the potential to drastically increase productivity, reduce cost, and introduce novel, flexible manufacturing services. Current work mostly focuses on high-level scenarios or emulation-based prototype deployments. Extending our previous work, we showcase one of the first cloud-native 5G verticals focusing on the deployment of smart manufacturing use cases on production infrastructure. In particular, we use the 5GTANGO service platform to deploy our developed network services on Kubernetes. For this demo, we implemented a series of cloud-native virtualized network functions (VNFs) and created suitable service descriptors. Their light-weight, stateless deployment on Kubernetes enables quick instantiation, scalability, and robustness.

Network function virtualization (NFV) proposes to replace physical middleboxes with more flexible virtual network functions (VNFs). To dynamically adjust to everchanging traffic demands, VNFs have to be instantiated and their allocated resources have to be adjusted on demand. Deciding the amount of allocated resources is non-trivial. Existing optimization approaches often assume fixed resource requirements for each VNF instance. However, this can easily lead to either waste of resources or bad service quality if too many or too few resources are allocated. To solve this problem, we train machine learning models on real VNF data, containing measurements of performance and resource requirements. For each VNF, the trained models can then accurately predict the required resources to handle a certain traffic load. We integrate these machine learning models into an algorithm for joint VNF scaling and placement and evaluate their impact on resulting VNF placements. Our evaluation based on real-world data shows that using suitable machine learning models effectively avoids over- and underallocation of resources, leading to up to 12 times lower resource consumption and better service quality with up to 4.5 times lower total delay than using standard fixed resource allocation.

Softwarization facilitates the introduction of smart manufacturing applications in the industry. Manifold devices such as machine computers, Industrial IoT devices, tablets, smartphones and smart glasses are integrated into factory networks to enable shop floor digitalization and big data analysis. To handle the increasing number of devices and the resulting traffic, a flexible and scalable factory network is necessary which can be realized using softwarization technologies like Network Function Virtualization (NFV). However, the security risks increase with the increasing number of new devices, so that cyber security must also be considered in NFV-based networks. Therefore, extending our previous work, we showcase threat detection using a cloud-native NFV-driven intrusion detection system (IDS) that is integrated in our industrial-specific network services. As a result of the threat detection, the affected network service is put into quarantine via automatic network reconfiguration. We use the 5GTANGO service platform to deploy our developed network services on Kubernetes and to initiate the network reconfiguration.