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Scheduling of real-time applications modelled according to the periodic and the sporadic task model under hierarchical and compositional real-time systems has been widely studied to provide temporal isolation among independent applications running on shared resources. However, for some real-time applications which are amenable to variation in their timing behaviour, usage of these tasks models can result in pessimistic solutions. The elastic task model addresses this pessimism by allowing the timing requirements of an application's tasks to be specified as a range of values instead of a single value. Although the scheduling of elastic applications on dedicated resources has received considerable attention, there is limited work on scheduling of such applications in hierarchical and compositional settings. In this paper, we evaluate different earliest deadline first scheduling algorithms to schedule elastic applications in a minimum parallelism supply form reservation on a multiprocessor system. Our evaluation indicates that the proposed approach provides performance comparable to the current state-of-art algorithms for scheduling elastic applications on dedicated processors in terms of schedulability. 

Many real-time applications have functional behaviour that requires variability in timing properties at runtime. The elastic task model provides a convenient mechanism to specify and encapsulate such variability and enables the modification of an application's periods during run-time to keep the application schedulable. Additionally, reservation-based scheduling techniques were proposed for the same purpose of taming unpredictability of timing variations, but with a different solution, i.e., by providing the spatial and temporal isolation for executing independent applications on the same hardware. In this paper, we combine the two approaches by proposing a two-level adaptive scheduling framework which is based on the elastic task model and the compositional framework based on the periodic resource model. The proposed framework minimises the number of requests for bandwidth adaption at the reservation (system) level and primarily enables schedulability by accounting for the application's elasticity by adjusting the periods. The motivation for this design choice is to rather localise the effect of the modifications within the application, without necessarily affecting all the applications at the system level compared to the changes made at the application level. The evaluation results show that the local application changes may often be enough to solve the problem of variability, significantly reducing the number of bandwidth adjustments, and therefore reducing the potential negative impact on all the applications of a system.

To meet the demands of future automation systems, the architecture of traditional control systems such as the industrial robotic systems needs to evolve and new architectural paradigms need to be investigated. While cloud-based platforms provide services such as computational resources on demand, they do not address the requirements of real-time performance expected by control applications. Fog computing is a promising new architectural paradigm that complements the cloud-based platform by addressing its limitations. In this paper, we analyse the existing robot system architecture and propose a fog-based solution for industrial robotic systems that addresses the needs of future automation systems. We also propose the use of Time-Sensitive Networking (TSN) services for real-time communication and OPC-UA for information modelling within this architecture. Additionally, we discuss the main research challenges associated with the proposed architecture.

This paper proposes a systematic three-stage methodology for migrating complex real-time industrial software systems from single-core to multi-core computing platforms. Single-core platforms have limited computational capabilities that prevent integration of computationally demanding applications such as image processing within the existing system. Modern multi-core processors offer a promising solution to address these limitations by providing increased computational power and allowing parallel execution of different applications within the system. However, the transition from traditional single-core to contemporary multi-core computing platforms is non-trivial and requires a systematic and well-defined migration process. This paper reviews some of the existing migration methods and provides a systematic multi-phase migration process with emphasis on software architecture recovery and transformation to explicitly address the timing and dependability attributes expected of industrial software systems. The methodology was evaluated using a survey-based approach and the results indicate that the presented methodology is feasible, useable and useful for real-time industrial software systems.