Composite asset or systems engineering requires integration and verification of the constituent asset models at the system level. It requires integrating relevant asset data and dynamical models of the individual assets which may be developed using diverse design and simulation tools by multiple vendors. Key integration challenges of lack of interoperability among corresponding data and simulation models, and separated, inflexible application deployments are addressed in this paper via application to a drive-motor-load system. A novel cloud-enabled drive-motor-load simulation platform based on the usage of Asset Administration Shells (AASs) for automatic data exchange and Functional Mockup Units (FMUs) for interoperable simulation is presented, together with an initial application of containerization for securing components. The solution enables higher quality and efficiency of engineering through simulation-based asset dimensioning, what-if scenario simulations, and simulation-based calibration and optimization as well as flexible, user-friendly, and more secure application deployment. An AAS-based model integration for linking various domain models and their parameters across design, configuration, and virtual commissioning, including application-side load FMUs via AAS Simulation Submodels at the composite system level, enables data interoperability while also serving as a building block of the future drive-motor-load system’s digital twin. The solution enables various applications across different product lifecycle phases such as pre-sales (drive) customer engagement, simulation-based calibration and design optimization during engineering, advanced system validations and testing in virtual commissioning, and continuous customer and OEM trainings during commissioning and operations.

Process engineers design industrial process plants using piping and instrumentation diagrams (P&IDs). Today, data analysts who diagnose plant disturbances based on historical signal trends usually analyze these complex diagrams manually on paper, which is time-consuming and error-prone. In the last 15 years, researchers have thus proposed several approaches and tools to turn these diagrams into machine-readable models that can be processed by software tools. Yet, these tools lack sophisticated query interfaces and intuitive visualizations. We propose the method TOPNAV to navigate plant topology models and aid data analytics. The method supports systematic searching for elements and paths in topology models and feeding the results into analytical tools to facilitate statistical analyses. In a user study, an up to 90% time reduction was observed compared to manual P&ID analysis, while reducing errors significantly.

Many Internet-of-Things (IoT) applications for smart homes, connected factories, or car-to-car communication utilize broker-based publish/subscribe communication protocols, such as the MQTT protocol. Commercial IoT applications have high reliability requirements for messaging, as lost messages due to unstable Internet connections or node failures can harm devices or even human beings. MQTT brokers implement numerous architectural availability tactics, but former analyses of MQTT communication have mainly focused on performance measurements under stable conditions. We have created the MAYHEM resilience testing tool for MQTT brokers and applied it in various resilience experiments on different MQTT brokers (VerneMQ, Mosquitto, HiveMQ, EMQ X). We found that MQTT QoS level 0 is already robust against minor packet loss, that selected broker message persistency solutions can lead to lost messages, and that most clustered MQTT brokers favor availability and performance over communication integrity. The results can support IoT practitioners in architectural decisions and researchers as well as broker vendors in optimizing designs and implementations.

Summary Distributed control systems are currently evolving towards industrial Internet of Things (IoT) systems communicating fully using Internet protocols. This creates opportunities for streamlining costly commissioning processes, which today require substantial manual work for installing, configuring, and integrating thousands of actuators and sensors. The vision of “plug‐and‐produce” control systems has been pursued for more than 15 years, but existing approaches fell short regarding configuration tasks and vendor neutrality. This paper introduces the standards‐based IoT reference architecture OpenPnP, which allows largely automating the configuration and integration tasks of industrial commissioning processes. The architecture includes a number of design and technology decisions and the required implementation can be scaled down to resource‐constrained industrial devices. This paper demonstrates how OpenPnP can reduce configuration and integration efforts up to 90% in typical settings, while potentially scaling well up to tens of thousands of communicated signals. Practitioners can orient their implementations towards OpenPnP, therefore potentially enabling “plug‐and‐produce” in many thousands of control systems.

The concepts of decision forces and the decision forces viewpoint were proposed to help software architects to make architectural decisions more transparent and the documentation of their rationales more explicit. However, practical experience reports and guidelines on how to use the viewpoint in typical industrial project setups are not available. Existing works mainly focus on basic tool support for the documentation of the viewpoint or show how forces can be used as part of focused architecture review sessions. With this paper, we share experiences and lessons learned from applying the decision forces viewpoint in a distributed industrial project setup, which involves consultants supporting architects during the re-design process of an existing large software system. Alongside our findings, we describe new forces that can serve as template for similar projects, discuss challenges applying them in a distributed consultancy project, and share ideas for potential extensions.

The OPC UA communication architecture is currently becoming an integral part of industrial automation systems, which control complex production processes, such as electric power generation or paper production. With a recently released extension for pub/sub communication, OPC UA can now also support fast cyclic control applications, but the bottlenecks of OPC UA implementations and their scalability on resource-constrained industrial devices are not yet well understood. Former OPC UA performance evaluations mainly concerned client/server round-trip times or focused on jitter, but did not explore resource bottlenecks or create predictive performance models. We have carried out extensive performance measurements with OPC UA client/server and pub/sub communication and created a CPU utilization prediction model based on linear regression that can be used to size hardware environments. We found that the server CPU is the main bottleneck for OPC UA pub/sub communication, but allows a throughput of up to 40,000 signals per second on a Raspberry Pi Zero. We also found that the client/server session management overhead can severely impact performance, if more than 20 clients access a single server.

BTLive is a novel, unique, mesh/push-based peer-to-peer (P2P) live video streaming mechanism. Previous studies show that it achieves significantly lower streaming delays than other approaches but suffers from a high overhead, where peers receive up to 33% duplicate video chunks. Other P2P streaming mechanisms, most of which are either mesh/pull or tree/push-based, do not experience duplicates at such a severe level. As a result, the problem of duplicate transmission has not explicitly been studied so far. However, to make BTLive's streaming approach more practical, it is critical to drastically reduce its overhead. To this end, this paper presents the design of a novel adaptive mechanism which, applied to BTLive, reduces the number of duplicates to less than 4%, while preserving its superior delivery performance. While the mechanism is applicable without changing the original BTLive protocol, also an extension to the protocol is proposed to further improve the duplicate avoidance. It reduces the amount of duplicates to less than 1%, without significantly increasing the overhead or playback delay. Thereby this work finally shows how a long envisioned low-delay and, at the same time, low-overhead P2P-based live stream delivery can be achieved.

A number of today's over-the-top (OTT) services could greatly benefit from a flexible, efficient, and scalable network-layer multicast support on the Internet. IP multicast showed not to meet these requirements, hindering its adoption in more than individual network islands. As a consequence, content delivery networks (CDNs) emerged as alternative and implemented one-to-many multicasting at the application layer. Yet, due to practical reasons and conflicting stakeholder interests, CDN deployments usually end at the border of Internet service provider networks, making one-to-many deliveries at an Internet-scale challenging up to date. In a previous work, the authors presented software-defined multicast (Sdm) to mitigate this problem by unlocking network-layer multicast support for OTT services inside ISP networks. In this paper, DYNSDM is proposed to complement Sdm in key aspects, allowing for a more holistic support of relevant scenarios aspects, and enabling a fine-granular traffic engineering of multicast traffic inside ISP networks. For this, DYNSDM presents a detailed design of the ISP-internal traffic and service management process and introduces a set of novel SDN-based network-layer mechanisms for traffic load balancing as well as handling of group and network dynamics. DYNSDM was implemented as prototype and extensively evaluated, showing its flexibility, high traffic efficiency, good scalability, and superior ability to balance traffic load inside ISP networks.

Dynamic network service chaining allows network operators to apply network services to customer traffic on demand and in a highly flexible manner. SDN and particularly OpenFlow-based approaches have been presented that exploit the flexibility and feature richness of OpenFlow for service chaining in data center settings. These systems often use network architectures similar to MPLS, where network traffic is processed at the network edges and the network core conducts simple packet forwarding only. This approach is suitable for many use cases due to the complementary characteristics of the hardware devices used in the core and the virtual switches used at the edge on virtualization hosts. Yet, this leads to a concentration of processing flow updates solely at the network edges, which can cause lowered QoS during flow update peaks, e.g. when a virtualization host fails. To tackle this problem, in this paper a concept is presented and evaluated that offloads OpenFlow rule updates from software-based edge switches to exploit hitherto unused resources on hardware switches in the core of the network. Furthermore, an analytical model is presented that allows describing the expected gain of the approach based on characteristics of the used OpenFlow devices.

A number of today's over-the-top (OTT) services could greatly benefit from a scalable and efficient network-layer multicast support on the Internet. IP multicast showed to not meet these requirements and, thus, is not available for this purpose. Content Delivery Networks emerged as global alternative but usually end at the border of ISP networks. Software-Defined Multicast (SDM) is proposed in a previous work by the authors, enabling ISP-internal network-layer multicast delivery of OTT traffic. While it coins fundamental concepts, it does not detail the ISP-internal traffic and service management and leaves important questions unanswered. To this end, DYNSDM is proposed in this paper, detailing the multicast planning and management, proposing a novel network-layer multi-tree mechanism to distribute traffic on links inside the ISP network, and introducing mechanisms to handle group and network dynamics. DYNSDM was prototypically evaluated, showing its high traffic efficiency, good scalability, and superior traffic distribution characteristics.

Live streaming of large-scale events such as the Olympic Games with a huge number of viewers is challenging, as the streaming infrastructure needs to scale fast and big, and often in an unpredictable manner. Peer-to-peer (P2P) live streaming (Peercasting) has proven to be beneficial in these scenarios, as resources are scaling inherently with the number of nodes. However, churn behavior in a node's neighborhood may result in fluctuating downstream bandwidth and thus freezing (stalling) playback. Related work tries to mitigate this effect by using layered video codecs, focusing on single-dimensional scalability in mesh-pull based systems. Yet, the benefits of multidimensional scalability (resolution, frame rate, and quantization) combined with coexisting pull-/push mechanisms introduced by modern hybrid P2P streaming architectures have not been studied in detail. Consequently, this work proposes a new scheduling algorithm taking these aspects into account. The evaluation shows large benefits for end-users by reducing the frequency of stalls by 90% even under extreme conditions.

Online media services are reshaping the way video content is watched. People with similar interests tend to request same content. This provides enormous potential to predict which content users are interested in. Besides, mobile devices are commonly used to watch videos which popularity is largely driven by its social success. In this paper, we design CPSys a Central Predictor System to prefetch relevant videos for each user. To fine tune our prefetching system, we rely on a large dataset collected from a large mobile carrier in Europe. The rationale of our prefetching strategy is first to form a graph and build implicit or explicit ties between similar users. On top of this graph, we propose the Most Popular and Most Recent (MPMR) policy to predict relevant videos for each user. We show that CPSys can achieve high performance with respect to the correct prediction ratio and by significantly reducing the traffic overhead. We further show that CPSys outperforms other prefetching schemes that have been presented and studied in the state of the art. At the end, we provide a proof-of-concept implementation of our prefetching system.

Mobile network operators are expected to face significant traffic increase in the upcoming years. One alternative method is to intelligently move transmissions to times of network underutilization, either on 3G/4G or by offloading to WiFi. Video content, predicted by Cisco to constitute 69% of mobile traffic, offers the greatest potential for offloading. To this end, the demonstrated app strives to relieve the mobile network in a two ways. First, long-term prefetching of promising videos based on posts from the user's Online Social Network feed is performed. The knowledge about which video is likely being requested in the near future offers the opportunity to schedule the transmission according to its probability of being watched. Second, the approach is complemented with short-term prefetching, which is used whenever a content could not be downloaded by long-term prefetching. In this case, resources are optimized so as to maximize the communication efficiency while preserving the quality of service. The demonstrated app considers the smartphone's observed cellular network history to optimize the mobile throughput. A customized video player implements both the long-term and short-term prefetching. It reduces both the load on mobile networks, decreases playback pausing events and hereby achieves a high QoE. Thus, the player addresses both the operators' and the users' needs.

Internet Service Providers (ISPs) need to cope with a growing amount of over-the-top (OTT) traffic, often without a share in the high revenues of the content providers. To achieve an efficient global delivery of content, today content providers usually employ content delivery networks (CDNs) located at the edge of ISP networks from where content is delivered to end users via IP unicast. Many OTT services could benefit from a better support within the ISP's network, e.g. by packet duplication to deliver OTT video streams. While traditional solutions like IP multicast did not prevail, SDN-based alternatives have started to gain attention recently. In contrast to traditional approaches, SDN enables ISPs to support network services in a more manageable and flexible manner. However, the approaches proposed so far are quite rigid and keep state at every network device, independent of the multicast group size. To alleviate this problem, this paper proposes a new approach termed ASDM enabling ISPs to dynamically adjust the tradeoff between bandwidth and state for any multicast service. It is shown that, given ISP-defined bandwidth and state cost functions, the optimal parameter for ASDM can be derived and applied for a transparent multicast-to-unicast conversion achieving the desired characteristics. The proposed approach results in up to 30% bandwidth reduction compared to unicast while using only a seventh of the network state compared to traditional multicast.

Recent studies show that an increasing number of over-the-top live streams is delivered over the Internet. For the delivery of those streams, the dynamically changing and potentially large number of users imposes a major challenge. Flash crowds, where the number of users multiplies or significantly drops in a very small time frame, can cause serious degradations in the streaming performance. Due to the missing support for global network-layer multicast, overlay-based approaches have been broadly studied, showing that, with relaxed time constraints, they can scale well with the number of users. Yet, to support flash crowds, scaling has to happen quickly to keep up also with rapidly changing populations. Only a few approaches exist that focus on this aspect by influencing the streaming topology and, so far, it is not clear if and how these mechanisms can be applied to state-of-the-art hybrid streaming systems. Therefore, in this paper, TOPT is proposed, integrating new as well as existing mechanisms in a common framework. The evaluation shows that the streaming topology, indeed, plays a major role during flash crowds. The lightweight and decentralized tree-forming and topology optimization mechanisms of TOPT, combined with tracker extensions to attach new peers in batches, greatly help improving the streaming performance in terms of reduced playback interruptions by more than 60% and slight reduction in communication overhead at an acceptable increase in average startup delays by 24%.

Peer-to-Peer (P2P) has proven to be a scalable approach for content distribution, while reducing the load for the content provider. As P2P systems depend on each node's participation, reciprocal incentive mechanisms for stimulating contribution are a major building block in those systems. However, reciprocal incentive schemes fall short for resource-poor devices, as they lead to service degradation for those nodes. This paper presents vINCENT, an incentive scheme allowing resource-poor devices to seek support from resource-rich devices. The novel scheme is based on virtual nodes which form a trusted domain around multiple devices. vINCENT has been implemented and evaluated in a P2P live streaming scenario. The solution provides good playback quality to all devices within a virtual node, given a sufficient contribution of the virtual node as a whole. At the same time, free riding nodes are isolated efficiently and the game theoretic properties of reciprocity are preserved.

The transmission of video content accounts for a large share of today's Internet traffic. While Video-on-Demand (VoD) substantially contributes to this, live streaming events such as video broadcasts from the Olympic Games can cause very high traffic volumes in the short term as well. Such peaks along with high fluctuations triggered by sudden changes in the behavior of users make the design of live streaming systems particularly challenging. Peer-to-Peer (P2P) has proven to be a scalable approach for disseminating content to a large number of users. Accordingly, the body of research offers numerous P2P live streaming approaches tailored towards specific scenarios and assumptions. However, no single approach is able to perform well under all possible conditions. Keeping up a high performance when conditions are changing is a challenge, since topology management and scheduling mechanisms cannot be exchanged easily. Therefore, this paper proposes TRANSIT, a new approach going beyond existing works in that it supports transitions between different live streaming mechanisms. TRANSIT makes different configurations of such mechanisms seamlessly exchangeable to enable the optimal choice of configurations for a wide range of live streaming scenarios. The approach is evaluated using measurement- and trace-based workloads. The results show that TRANSIT is able to maintain a high performance at a low overhead in highly fluctuating environments, whereas static configurations show serious performance degradations.

Over the years, the demand for high bandwidth services, such as live and on-demand video streaming, steadily increased. The adequate provisioning of such services is challenging and requires complex network management mechanisms to be implemented by Internet service providers (ISPs). In current broadband network architectures, the traffic of subscribers is tunneled through a single aggregation point, independent of the different service types it belongs to. While having a single aggregation point eases the management of subscribers for the ISP, it implies huge bandwidth requirements for the aggregation point and potentially high end-to-end latency for subscribers. An alternative would be a distributed subscriber management, adding more complexity to the management itself. In this paper, a new traffic management architecture is proposed that uses the concept of Software Defined Networking (SDN) to extend the existing Ethernet-based broadband network architecture, enabling a more efficient traffic management for an ISP. By using SDN-enabled home gateways, the ISP can configure traffic flows more dynamically, optimizing throughput in the network, especially for bandwidth-intensive services. Furthermore, a proof-of-concept implementation of the approach is presented to show the general feasibility and study configuration tradeoffs. Analytic considerations and testbed measurements show that the approach scales well with an increasing number of subscriber sessions.

The peer-to-peer approach can greatly help to cope with highly dynamic live streaming workload by using idle client resources. Yet, P2P streaming typically comes at the cost of increased streaming delays caused by the inevitable multi-hop forwarding of content by peers within the overlay. Various P2P streaming approaches have been proposed aiming at a good tradeoff between flexibility, streaming delay, and costs in terms of traffic overhead for both content providers and clients. Recently, BitTorrent Inc. released a new P2P live streaming system termed BTLive, specifically targeted at low delay and low overhead. For content providers investigating the applicability of BTLive's approach, it is essential to understand its properties as well as its limitations. So far, no publicly available study exists that quantitatively analyzes BTLive's performance. To this end, this paper presents a measurement study of the official beta version of BTLive. The study aims to answer the following key questions: How peer-to-peer is BTLive? How delay optimized is BTLive? What is the overhead of BTLive? To answer these questions, traces of real BTLive traffic between a broadcast server and a number of peers deployed across Europe have been analyzed.

Peer-assisted delivery of video content has shown a great potential to reduce upload bandwidth requirements for content providers by exploiting idle client resources in the video dissemination process. As primary content sources, the servers run by content providers play a critical role in such systems, making their adequate provisioning a key part of the streaming mechanism. While dynamic resource provisioning has been studied before, little is known about resource allocation for streaming of scalable media content. Besides the pure amount of resources, here, the quality level of the delivered video content becomes relevant. The spreading of video blocks with the wrong quality can lead to situations where peers are forced to reduce their video qualities, despite them having enough download capacity. To address this problem, in this paper, a new SVC-based adaptation policy and a request-based extension to it are proposed, enabling content providers to manage their streaming services in a video quality-aware manner. Prototypical evaluations show that the mechanisms outperform existing quality-agnostic approaches in terms of delivered SVC video quality.

Internet video streaming causes the second largest transfer volume and is the second fastest growing application class in Internet traffic analysis [3]. In this context, also the streaming of live content becomes increasingly relevant as more traditional broadcasters start delivering content over the Internet. Today, live video streaming services rely on IP-unicast delivery or closed IP-multicast systems inside single administrative domains. Approaches such as Content Delivery Networks (CDNs) are used to improve the unicast delivery of content. They usually end at the edge of the residential broadband access Internet Service Provider (ISP) networks that connect end users to the Internet. For live content delivery, network layer multicast would be desirable for ISPs as well as content providers to reduce the load due to parallel unicast connections for the same content. Because of the well-known drawbacks and limitations of IP-multicast [2], however, network layer multicast support is usually not available.

The proliferation of wireless broadband technologies and mobile devices has led to an increase in mobile traffic, especially due to a rapid growth of real-time entertainment such as video streaming on smartphones. Mobile peer-to-peer-based content distribution schemes can help to relieve infrastructure based mobile networks, but require participating nodes to provide resources which can drain their battery. Thus, the goal is to exploit mobile peers' resources while minimizing and balancing the energy consumption over all participating devices. Simulation models considering energy consumption lack precision, because they abstract away important parts of the hardware. On the other hand, prototypical energy measurements are more precise, but require a time consuming implementation and assessment. To this end, this demo paper presents a mobile P2P video streaming and benchmarking platform which enables to assess and compare the energy consumption of different approaches in a precise manner through live assessments at runtime. The demonstrated platform includes a simple, yet high-performance tree-based mobile P2P streaming overlay which can be utilized to easily implement and assess further streaming overlay approaches.

The peer-to-peer paradigm shows the potential to provide the same functionality and quality like client/server based systems, but with much lower costs. In order to control the quality of peer-to-peer systems, monitoring and management mechanisms need to be applied. Both tasks are challenging in large-scale networks with autonomous, unreliable nodes. In this paper we present a monitoring and management framework for structured peer-to-peer systems. It captures the live status of a peer-to-peer network in an exhaustive statistical representation. Using principles of autonomic computing, a preset system state is approached through automated system re-configuration in the case that a quality deviation is detected. Evaluation shows that the monitoring is very precise and lightweight and that preset quality goals are reached and kept automatically.