Simulation-based Queueing Models for Performance Analysis of IoT Applications

International audienceTo facilitate the development of Internet of Things (IoT) applications, numerous middleware protocols and APIs have been introduced. Such applications built atop reliable or unreliable protocols and they expose different characteristics. Additionally, with regard to the application context (e.g., emergency response operations), several Quality of Service (QoS) requirements must be satisfied. To study QoS in IoT applications, the provision of a generic performance analysis methodology is required. Queueing network models offer a simple modeling environment, which can be used to represent IoT interactions by combining multiple queueing model types for building queueing networks. The resulting networks can be used for performance analysis through analytical or simulation models. In this paper, we present several types of queueing models that represent different QoS settings of IoT interactions, such as intermittent mobile connectivity, message drop probabilities, message availability/validity and resource constrained devices. Using MobileJINQS, we simulate our models demonstrating the significant effect on response times and message success rates when varying QoS settings

Loupe: Verifying Publish-Subscribe Architectures with a Magnifying Lens

CONE

Analysis of Timing Constraints in Heterogeneous Middleware Interactions

International audienceWith the emergence of Future Internet applications that connect web services, sensor-actuator networks and service feeds, scalability and heterogeneity support of interaction paradigms are of critical importance. Heterogeneous interactions can be abstractly represented by client-service, publish-subscribe and tuple space middleware connectors that are interconnected via bridging mechanisms providing interoperability among the services. In this paper, we make use of the eXtensible Service Bus (XSB), proposed in the CHOReOS project as the connector enabling interoperability among heterogeneous choreography participants. XSB models transactions among peers through generic post and get operations that represent peer behavior with varying time/space coupling. Nevertheless, the heterogeneous lease and timeout constraints of these operations severely affect latency and success rates of transactions. By precisely studying the related timing thresholds using timed automata models, we verify conditions for successful transactions with XSB connectors. Furthermore, we statistically analyze through simulations, the effect of varying lease and timeout periods to ensure higher probabilities of successful transactions. Simulation experiments are compared with experiments run on the XSB implementation testbed to evaluate the accuracy of results. This work can provide application developers with precise design time information when setting these timing thresholds in order to ensure accurate runtime behavior

Formal analysis of Publish-Subscribe systems by probabilistic timed automata

The publish-subscribe architectural style has recently emerged as a promising approach to tackle the dynamism of modern distributed applications. The correctness of these applications does not only depend on the behavior of each component in isolation, but the interactions among components and the delivery infrastructure play key roles. This paper presents the first results on considering the validation of these applications in a probabilistic setting. We use probabilistic model checking techniques on stochastic models to tackle the uncertainty that is embedded in these systems. The communication infrastructure (i.e., the transmission channels and the publish-subscribe middleware) are modeled directly by means of probabilistic timed automata. Application components are modeled by using statechart diagrams and then translated into probabilistic timed automata. The main elements of the approach are described through an example

Formal Analysis of Publish-Subscribe Systems by Probabilistic Timed Automata

Abstract. The publish-subscribe architectural style has recently emer-ged as a promising approach to tackle the dynamism of modern dis-tributed applications. The correctness of these applications does not only depend on the behavior of each component in isolation, but the interac-tions among components and the delivery infrastructure play key roles. This paper presents the first results on considering the validation of these applications in a probabilistic setting. We use probabilistic model check-ing techniques on stochastic models to tackle the uncertainty that is embedded in these systems. The communication infrastructure (i.e., the transmission channels and the publish-subscribe middleware) are mod-eled directly by means of probabilistic timed automata. Application com-ponents are modeled by using statechart diagrams and then translated into probabilistic timed automata. The main elements of the approach are described through an example.

PrioDeX: a Data Exchange middleware for efficient event prioritization in SDN-based IoT systems

International audienceReal-time event detection and targeted decision making for emerging mission-critical applications require systems that extract and process relevant data from IoT sources in smart spaces. Oftentimes, this data is heterogeneous in size, relevance, and urgency, which creates a challenge when considering that different groups of stakeholders (e.g., first responders, medical staff, government officials, etc) require such data to be delivered in a reliable and timely manner. Furthermore, in mission-critical settings, networks can become constrained due to lossy channels and failed components, which ultimately add to the complexity of the problem. In this paper, we propose PrioDeX, a cross-layer middleware system that enables timely and reliable delivery of mission-critical data from IoT sources to relevant consumers through the prioritization of messages. It integrates parameters at the application, network, and middleware layers into a data exchange service that accurately estimates end-to-end performance metrics through a queueing analytical model. PrioDeX proposes novel algorithms that utilize the results of this analysis to tune data exchange configurations (event priorities and dropping policies), which is necessary for satisfying situational awareness requirements and resource constraints. PrioDeX leverages Software-Defined Networking (SDN) methodologies to enforce these configurations in the IoT network infrastructure. We evaluate our approach using both simulated and prototype-based experiments in a smart building fire response scenario. Our application-aware prioritization algorithm improves the value of exchanged information by 36% when compared with no prioritization; the addition of our network-aware drop rate policies improves this performance by 42% over priorities only and by 94% over no prioritization

Modelling Event-Based Interactions in Component-Based Architectures for Quantitative System Evaluation

This dissertation thesis presents an approach enabling the modelling and quality-of-service prediction of event-based systems at the architecture-level. Applying a two-step model refinement transformation, the approach integrates platform-specific performance influences of the underlying middleware while enabling the use of different existing analytical and simulation-based prediction techniques

Modelling Event-Based Interactions in Component-Based Architectures for Quantitative System Evaluation

This dissertation thesis presents an approach enabling the modelling and quality-of-service prediction of event-based systems at the architecture-level. Applying a two-step model refinement transformation, the approach integrates platform-specific performance influences of the underlying middleware while enabling the use of different existing analytical and simulation-based prediction techniques