Search

A wide range of new modeling languages with a specific focus on cloud computing, also known as cloud modeling languages (CMLs), have been introduced to help developers describe, evaluate, and deploy cloud applications. In general, CMLs define applications as interconnected cloud components within an architectural topology. However, in agile software development, developers describe system-level functionalities using user stories or epics to define end-user scenarios. So far, a CML bridging the gap between formal architectural descriptions and the informal scenario descriptions from agile development is missing. We present Clams (Cloud application modeling solution), a scenario-based CML. Clams uses cloud computing patterns as architectural placeholders in combination with message sequence charts. We introduce standard tooling to handle Clams models and show how one can refine patterns to concrete service offerings. Additionally, we also provide a development framework to support the creation of custom tools to evaluate, analyze, or translate Clams models efficiently.

Networked control systems (NCS) consist of sensors and actuators that are connected to a controller through a packet-switched network in a feedback loop to control physical systems in diverse application areas such as industry, automotive, or power infrastructure. The control of critical real-time systems places strong requirements on the latency and reliability of both the communication network and the controller. In this paper, we consider the problem of increasing the reliability of an NCS subject to crash failures and message loss by replicating the controller component. Previous replication schemes for real-time systems have focused on ensuring that no conflicting values are sent to the actuators by different replicas. Since this property, which we call output consistency, only refers to the values within one time step, it is insufficient for reasoning about the formal conditions under which a group of replicated controllers behaves equivalent to a non-replicated controller. Therefore, we propose the stronger state consistency property, which ensures that the sequence of values produced by the replicated controller exhibits the same dynamical behaviour as a non-replicated controller. Moreover, we present SCRaM, a protocol for replicating generic periodically sampled controllers that satisfies both of these consistency requirements. To demonstrate the effectiveness of our approach, we evaluated it experimentally for the control of a cart-driven inverted pendulum.

Industry 4.0 and the vision of smart factories drive the need for real-time communication. Time-Sensitive Networking (TSN) augments the IEEE Std 802.1Q with a family of mechanisms enabling real-time communication. One of the key mechanisms is the Time-Aware Shaper (TAS) implementing a TDMA scheme on a traffic class basis. With proper synchronization it can even be used to schedule individual frames or streams. With this capability, the network can guarantee communication deadlines, bounded latency, and bounded jitter. However, for these guarantees a system-wide schedule needs to be calculated, which is an NP-hard problem. Current approaches are mainly based on constraint programming and optimization problems, and, therefore do not scale well for larger topologies and number of streams. In this paper, our contribution is twofold: first, we propose a scheduling model for converged networks supporting different traffic types and, secondly, we introduce a novel procedure for schedule planning of isochronous traffic which exploits the hierarchical structure of factory networks. To this end, we split the network into sub-networks and use a two-stage approach based on a heuristic and tracing. Our evaluation shows that the new scheduling approach outperforms the reference scheduler by more than two orders of magnitude with regard to execution time.

Recent years have seen an evolution of software-defined networking (SDN) control plane architectures, starting from simple monolithic controllers, over modular monolithic controllers, to distributed controllers. We observe, however, that today’s distributed controllers still exhibit inflexibility with respect to the distribution of control logic. Therefore, we propose a novel architecture of a distributed SDN controller, providing maximum flexibility with respect to distribution and improved manageability. Our architecture splits control logic into lightweight control modules, called controllets , based on a micro-kernel approach, reducing common controllet functionality to a bare minimum and factoring out all higher-level functionality. Lightweight controllets also allow for pushing control logic onto switches and enable local processing of data plane events to minimize control latency and communication overhead while leveraging SDN’s global view to maximize control decision quality. Controllets are interconnected through a message bus supporting the publish/subscribe communication paradigm with specific extensions for content-based message filtering. Publish/subscribe allows for complete decoupling of controllets to further facilitate control plane distribution. Furthermore, we identify crucial requirements for practical on-switch deployments, where we employ lightweight virtualization techniques to ensure a safe control plane operation. We evaluate both, the scalability and performance properties of our architecture, including its deployment on a white-box networking hardware switch.

Several networking architectures have been developed atop IEEE 802.3 networks to provide real-time communication guarantees for time-sensitive applications in industrial automation systems. The basic principle underlying these technologies is the precise transmission scheduling of time-triggered traffic through the network for providing deterministic and bounded latency and jitter. These transmission schedules are typically synthesized offline (computational time in the order of hours) and remain fixed thereafter, making it difficult to dynamically add or remove network applications. This paper presents algorithms for incrementally adding time-triggered flows in a time-sensitive software-defined network (TSSDN). The TSSDN is a network architecture based on software-defined networking, which provides real-time guarantees for time-triggered flows by scheduling their transmissions on the hosts (network edge) only. These algorithms exploit the global view of the control plane on the data plane to schedule and route time-triggered flows needed for the dynamic applications in the Industrial Internet of Things (Industry 4.0). The evaluations show that these algorithms can compute incremental schedules for time-triggered flows in subseconds with an average relative optimality of 68%.

With the increasing popularity of software-defined networking (SDN), ternary content-addressable memory of switches can be directly accessed by a publish/subscribe middleware to perform filtering operations at low latency. In this way, three important requirements for a publish/subscribe middleware can be fulfilled, namely, bandwidth efficiency, line-rate performance, and low latency in forwarding messages between producers and consumers. Nevertheless, it is challenging to sustain line-rate performance in the presence of dynamically changing interests of producers and consumers. In this paper, we realize a scalable, SDN-based publish/subscribe middleware, called PLEROMA, that performs efficient forwarding at line-rate. Moreover, PLEROMA offers methods to efficiently reconfigure a deployed topology in the presence of dynamic subscriptions and advertisements. We evaluate the performance of both the data plane and the control plane of PLEROMA to support our claim. Furthermore, we evaluate and benchmark the performances of SDN-compliant hardware and software switches in the context of our middleware.

Time-sensitive Networking (TSN) is an evolving group of IEEE standards for deterministic real-time communication making standard Ethernet technology applicable to safety-critical application domains such as manufacturing or automotive systems. TSN includes several mechanisms influencing the timely forwarding of traffic, in particular, a time-triggered scheduling mechanism called time-aware shaper (TAS) and frame preemption to reduce the blocking time of high-priority traffic by low-priority traffic. Although these mechanisms have been standardized and products implementing them begin to enter the market, it is still hard for practitioners to select and apply suitable mechanisms fitting the problem at hand. For instance, TAS schedules can be calculated for individual streams or classes of traffic, and frame preemption with strict priority scheduling (w/o TAS) might seem to be an option in networks with extremely high data rates. In this paper, we make a first step towards assisting practitioners in making an informed decision when choosing between stream-based TAS, class-based TAS, and frame preemption by comparing these mechanisms in selected scenarios using our TSN network simulation tool NeSTiNg. Moreover, to facilitate the application of class-based TAS, we derive a formula for calculating class-based TAS configuration.

Internet of Things (IoT) is expanding at a rapid rate where it allows for virtually endless opportunities and connections to take place. In general, IoT opens the door to a myriad of applications but also to many challenges. One of the major challenges is how to efficiently retrieve the sensory data from "resources-limited" IoT devices. Such devices typically have a restricted energy budget, which broadly hinders their direct connection to the Internet. In this realm, modern mobile devices, e.g. smartphones, tablets, smartwatches, have been harnessed to bridge between the low-power IoT devices and the Internet. However, the current vision which mainly relies on designing siloed gateways, i.e. a separate gateway/App for each IoT device, is certainly impractical, especially with the rapid growth in the number of IoT devices. Furthermore, the energy efficiency of the smart mobile devices hosting the IoT gateways has to be thoroughly considered. To tackle these challenges, we introduce GaaS (Gateway as a Service), a cross-platform gateway architecture for opportunistically retrieving sensory data from the low-power IoT sensors. Through Bluetooth low energy radios, GaaS is capable of simultaneously connecting to several nearby IoT sensors. To this end, we devise two distinct priority-based scheduling algorithms, namely the EP-WSM and FEP-AHP schedulers, which rank the detected IoT sensors, before estimating the connection time for each IoT sensor. The intuition behind ranking the IoT sensors is to improve the data retrieval rate from these sensors together with reducing the energy overhead on the mobile devices. Additionally, GaaS encompasses a self-adaptive engine to automatically balance the trade-off between energy efficiency and data retrieval rate through switching between schedulers according to the runtime dynamics. To demonstrate the effectiveness of GaaS, we implemented an IoT testbed to evaluate the energy consumption, the latency, and the data retrieval rate. The results show that using GaaS, compared to siloed gateways, we can identify up to 18% savings in the consumed energy while requiring much less data retrieval time.

Typically, Function-as-a-Service (FaaS) involves state-less replication with very large numbers of instances. The reliability of such services can be evaluated using Bayesian Networks and k-out-of-n models. However, existing k-out-of-n models do not scale to the larger number of hosts of FaaS services. Therefore, we propose a scalable k-out-of-n model in this paper with the same semantics as the standard k-out-of-n voting gates in fault trees, enabling the reliability analysis of FaaS services.

Recently, mobile crowdsensing has become an appealing paradigm thanks to the ubiquitous presence of powerful mobile devices. Indoor mapping, as an example of crowdsensingdriven applications, is essential to provide many indoor locationbased services, such as emergency response, security, and tracking/navigation in large buildings. In this realm, 3D point clouds stand as an optimal data type which can be crowdsensed—using currently-available mobile devices, e.g. Google Tango, Microsoft Hololens and Apple ARKit—to generate floor plans with different levels of detail, i.e. 2D and 3D mapping. However, collecting such bulky data from "resources-limited" mobile devices can significantly harm their energy efficiency. To overcome this challenge, we introduce GreenMap, an energy-aware architectural framework for automatically mapping the interior spaces using crowdsensed point clouds with the support of structural information encoded in formal grammars. GreenMap reduces the energy overhead through projecting the point clouds to several filtration steps on the mobile devices. In this context, GreenMap leverages the potential of approximate computing to reduce the computational cost of data filtering while maintaining a satisfactory level of modeding accuracy. To this end, we propose two approximation strategies, namely DyPR and SuFFUSION. To demonstrate the effectiveness of GreenMap, we implemented a crowdsensing Android App to collect 3D point clouds from two different buildings. We show that GreenMap achieves significant energy savings of up to 67.8%, compared to the baseline methods, while generating comparable floor plans.

Im Rahmen der Auseinandersetzung mit dem vielschichtigen Begriff des Public Interest Designs (PID) wird in diesem Beitrag die Ausstellung als ein PID-Phänomen verhandelt. Dabei soll die Frage geklärt werden, wie Ausstellungen produziert werden, um Öffentlichkeiten zur Auseinandersetzung mit einer vorgestellten Thematik zu bewegen, um bestehende Meinungen und Einstellungen zu verändern. Um dieses spannende Konstrukt für den Bereich des PID fruchtbar zu bestimmen, wird das Phänomen Ausstellung aus der Perspektive der modernen Rhetoriktheorie untersucht. Die Rhetorik als wissenschaftliche Disziplin liefert unter anderem konkrete Kriterien des Meinungs- und Einstellungswechsels durch diverse Kommunikationsformen und analysiert damit explizit die Überzeugungsinstrumente und Orientierungstechniken, die auf die Persuasion von Einzelpersonen oder Gruppen ausgerichtet sind.

Location information sharing on popular online social networking platforms like Facebook and Foursquare brings mutual benefits for the users of these platforms (e.g., free locationbased services) as well as the platform providers (e.g., locationbased businesses). An obvious problem however that impedes these mutual benefits are privacy concerns related to location data of users, which also curb their active participation. In this paper, we analyze the role of existing location privacypreserving mechanisms in minimizing this mutual loss of benefits. Our analysis reveals that most existing mechanisms either ignore social platform related user-privacy concerns or they disregard location data-quality related demands of the platform providers. Moreover, we also point out concrete research gaps and implementation issues related to existing privacy mechanisms.