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Task offloading is a prominent problem in edge−cloud computing, as it aims to utilize the limited capacityof fog servers and cloud resources to satisfy the QoS requirements of tasks, such as meeting their deadlines.This paper formulates the task offloading problem as a nonlinear mathematical programming model to maximizethe number of independent IoT tasks that meet their deadlines and to minimize the deadline violationtime of tasks that cannot meet their deadlines. This paper proposes two Q-learning algorithms to solve theformulated problem. The performance of the proposed algorithms is experimentally evaluated with respect toseveral algorithms. The evaluation results demonstrate that the proposed Q-learning algorithms perform wellin meeting task deadlines and reducing the total deadline violation time.

Controller Area Network (CAN) is an ISO-standardized protocol that is extensively used as the onboard real-time network in vehicles. Evaluating and mitigating responsetime jitter experienced by CAN messages is paramount, particularly in real-time applications where timing predictability is crucial. This paper presents a jitter analysis framework for CAN messages. Within this framework, the worst-case responsetime jitter of CAN messages is calculated through pre-runtime analysis. Additionally, a post-runtime analysis of message traces helps ascertain the maximum response-time jitter these messages encounter at runtime. The framework performs a comparative evaluation of the pre-runtime and runtime response-time jitter and then back-propagates insights and guidelines to the systems' designers to mitigate the jitter. To validate this approach, we implement the framework using two prominent industrial tools: Rubus-ICE and CANalyzer. The effectiveness of the framework is further substantiated by modeling and analyzing an industrial use case provided by Volvo Construction Equipment., Conference paper; Export Date: 17 April 2024; Cited By: 0; Correspondence Address: S. Mubeen; Mälardalen University, Västerås, Sweden; email: saad.mubeen@mdu.se; Conference name: 2024 International Conference on Artificial Intelligence, Computer, Data Sciences, and Applications, ACDSA 2024; Conference date: 1 February 2024 through 2 February 2024; Conference code: 198277

To simplify the formal verification of ROS 2-based applications, in this paper, we propose a novel approach to the automation of their model-based verification using model-driven engineering techniques. We propose a methodology starting with ROS 2 execution traces, generated by ROS2_tracing and using models and model transformations in Eclipse to automatically initialize pre-defined formal model templates in UPPAAL, with system parameters. While the methodology targets the simplification of formal verification for robotics developers as users, the implementation is at an early stage and the toolchain is not fully implemented and evaluated. Hence, this paper targets tool developers and researchers to give a first overview of the underlying idea of automating ROS 2 verification. Hence, we propose a toolchain that supports verification of implemented and conceptual ROS 2 systems, as well as iterative verification of timing and scheduling parameters. We propose using four different model representations, based on the ROS2_tracing output and self-designed Eclipse Ecore metamodels to model the system from a structural and verification perspective. The different model representations allow traceability throughout the modeling and verification process.Last, an initial proof of concept is implemented containing the core elements of the proposed toolchain and validated given a small ROS 2 system.

In this paper, we present a bandwidth reservation analysis for Audio-Video Bridging (AVB) traffic in the Time-Sensitive Networking (TSN) standards. The proposed analysis is based on the existing worst-case response-time analysis and can be used to calculate the minimum required bandwidth for guaranteeing the schedulability of messages in AVB classes. The proposed analysis allocates per-link bandwidth to AVB traffic that is sufficient to ensure its schedulability when a combination of the Credit-Based Shaper and Time-Aware Shaper mechanisms are used. We evaluate the proposed analysis using an automotive industrial use case. We evaluate the schedulability of AVB traffic by comparing the proposed analysis with the utilization-based bandwidth reservation as recommended by the TSN standards.

Over the last two decades, vehicles have undergone a significant shift, transforming into highly software-intensive systems. Projections indicate that even entry-level vehicles will soon integrate hundreds of millions of lines of code and incorporate numerous electronic control units. To navigate the complexity of these software-intensive systems, there has been a notable shift towards adopting model-driven engineering and specialised modelling languages. Among these languages, the Rubus Component Model has played a crucial role for over 25 years, supporting the development and timing analysis of distributed resource-constrained embedded systems. The enduring success of the Rubus Component Model lies in its responsiveness to end-users' demands and its ability to adapt to technological advancements. Notably, the proliferation of network interface controllers, including controller area network controllers, supporting diverse message-receiving policies like polling and interrupt, represents a significant advancement. However, the implications of these policies on end-to-end delays in distributed systems necessitate explicit modelling and dedicated timing analysis tools.This paper introduces an evolved Rubus Component Model, tailored for model-driven development and timing analysis in distributed embedded systems that utilise network interface controllers with diverse message-receiving policies. Drawing inspiration from a real-world example, the paper introduces new elements and properties in the Rubus Component Model designed to support these policies and facilitate timing analysis. The practical application of these enhancements is demonstrated, and insights are extended to other contemporary modelling languages in the vehicular domain. Beyond bolstering expressiveness, this evolution ensures the timing predictability of distributed embedded systems, aligning seamlessly with the Rubus Component Model's core focus., Conference paper; Export Date: 17 April 2024; Cited By: 0; Correspondence Address: A. Bucaioni; Mälardalen University, Västerås, Sweden; email: alessio.bucaioni@mdu.se; Conference name: 2024 International Conference on Artificial Intelligence, Computer, Data Sciences, and Applications, ACDSA 2024; Conference date: 1 February 2024 through 2 February 2024; Conference code: 198277

To utilize edge and cloud in real-time industrial applications, communication with the edge and cloud servers should be predictable in timing. However, the predictability of offloading from device to servers cannot be guaranteed in an environment where multiple devices compete for the same edge and cloud resources due to potential server-side scheduling conflicts. To the best of our knowledge, the state-of-the-art lacks a technique for offloading real-time applications from multiple devices to a set of heterogeneous edge/cloud servers. To this end, this paper proposes a centralized resource reservation technique that enables the offloading of real-time applications to the edge and cloud in a predictable time-schedule. The proposed technique enables end-devices to request the server's time for offloadable real-time applications in advance, allowing a designated offloading server that guarantees the tasks' timely execution. Furthermore, the proposed technique is capable of optimizing the reservation scheduling strategy with the goal of minimizing the energy consumption of edge servers while meeting the stringent timing requirements of real-time applications. The results showed that the number of deadline satisfied jobs improved by 65%, and total energy consumption by 3%, compared to the second best algorithm among the ones that have been compared with the proposed algorithm when the number of jobs is changed.

This paper performs a comparative evaluation of various generations of Controller Area Network (CAN), including the classical CAN, CAN Flexible Data-Rate (FD), and CAN Extra Long (XL). We utilize response-time analysis for the evaluation. In this regard, we identify that the state of the art lacks the response-time analysis for CAN XL. Hence, we discuss the worst-case transmission times calculations for CAN XL frames and incorporate them to the existing analysis for CAN to support response-time analysis of CAN XL frames. Using the extended analysis, we perform a comparative evaluation of the three generations of CAN by analyzing an automotive industrial use case. In crux, we show that using CAN FD is more advantageous than the classical CAN and CAN XL when using frames with payloads of up to 8 bytes, despite the fact that CAN XL supports higher bit rates. For frames with 12-64 bytes payloads, CAN FD performs better than CAN XL when running at the same bit rate, but CAN XL performs better when running at a higher bit rate. Additionally, we discovered that CAN XL performs better than the classical CAN and CAN FD when the frame payload is over 64 bytes, even if it runs at the same or higher bit rates than CAN FD.

We consider the problem of on-arrival dispatching and scheduling jobs with stochastic execution times, inter-arrival times, and deadlines in multi-server fog and edge computing platforms. In terms of mean response times, it has been shown that size-based scheduling policies, when combined with dispatching policies such as join-shortest-queue, provide better performance over policies such as first-in-first-out. Since job sizes may not always be known apriori, prediction-based policies have been shown to perform reasonably well. However, little is known about the performance of prediction-based policies for jobs with firm deadlines. In this paper, we address this issue by considering the number of jobs that complete within their deadlines as a performance metric and investigate, using simulations, the performance of a prediction-based shortest-job-first scheduling policy for the considered metric and compare it against scheduling policies that prioritize based on deadlines (EDF) and arrival times (FIFO). The evaluation indicates that in under-loaded conditions, the prediction-based policy is outperformed by both FIFO and EDF policies. However, in overloaded scenarios, the prediction-based policy offers slightly better performance.

Novel production paradigms aim at increasing the efficiency and flexibility of production systems. Nonetheless, traditional industrial infrastructures lack the mechanisms needed to support these new paradigms. One of the main limiting factors is the architecture, which follows the automation pyramid in which subsystems are divided in layers depending on their functionalities. This allowed to meet the timing and dependability requirements of the production subsystems, however at the cost of limiting the exchange of information required to provide increased flexibility to the system. For this reason, in this paper we propose a new industrial architecture with a single network infrastructure to connect all the devices that conform to the industrial systems. On top of that, we design an Automatic Network Configurator to support the automatic configuration of the system. To assess the feasibility of our design and evaluate its performance, we implement the first instance of the architecture capable of supporting changes in the traffic requirements during run-time, i.e., without stopping or disrupting the system's operation. Furthermore, we use the implemented instance to measure the time required for reconfigurations.

The edge computing paradigm extends the architectural space of real-time systems by bringing the capabilities of the cloud to the edge. Unlike cloud-native systems designed for mean response times, real-time industrial embedded systems are designed to control a single physical system, such as a manipulator arm or a mobile robot, that requires temporal predictability. We consider the problem of dispatching and scheduling of jobs with deadlines that can be offloaded to the edge and propose DAL, a deadline-aware load balancing and scheduling framework that leverages the availability of on-demand computing resources along with an on-arrival dispatching scheme to manage temporal requirements of such offloaded applications. The evaluation indicates that DAL can achieve reasonably good performance even when execution times, arrival times, and deadlines vary.

Wireless Time-Sensitive Networks (TSN) are needed to fulfill the requirements of real-time applications in areas where wired connections are not feasible. Wireless TSN combines the real-time capabilities of TSN with the flexibility of wireless connectivity opening a path for new use cases while providing determinism to time-critical scenarios such as autonomous vehicles. Industrial automation is integrating TSN with various wireless technologies such as WiFi, 4G, and 5G. This paper presents an ongoing work which aim is to experimentally analyze the performance of TSN when integrated with 4G, 5G, and WiFi in real-world scenarios. This will help both the researchers and industries to have a clear view of the network performance regarding the end-to-end latency requirements when designing their applications and use cases.