https://www.mdu.se/

Real-time systems such as industrial robots and autonomous navigation vehicles integrate a wide range of algorithms to achieve their functional behavior. In certain systems, these algorithms are deployed on dedicated single-core hardware platforms that exchange information over a real-time network. With the availability of current multi-core platforms, there is growing interest in an integrated architecture where these algorithms can run on a shared hardware platform. In addition, the benefits of virtualization-based cloud and fog architectures for non-real-time applications have prompted discussions about the possibility of achieving similar benefits for real-time systems. Although many useful solutions such as resource reservations and hierarchical scheduling have been proposed to facilitate hardware virtualization for real-time applications, the current state of the art is mainly concerned with applications whose timing requirements can be modelled according to the periodic or the sporadic task model. Since the computational demand of many real-time algorithms can be flexibly adjusted at runtime, e.g., by changing the periods, they can be better abstracted with the elastic task model in the context of virtualized hardware platforms.

Therefore, in this thesis, we first propose a scheduling framework with reservations based on periodic resource supply for real-time elastic applications with single-core workloads, and then extend this solution for applications with multi-core workloads  where reservations are based on the minimum-parallelism model. Since many existing applications run on dedicated single-core platforms, we simultaneously provide a systematic methodology for migrating an existing real-time software application from a single-core to a multi-core platform. In doing so, we focus on recovering the architecture of the existing software and transforming it for implementation on a multi-core platform. Next, we explore the advantages of a fog-based architecture over an existing robot control architecture and identify the key research challenges that must be addressed for the adoption of the fog computing architecture.

Real-time systems such as industrial robots and automated guided vehicles integrate a wide range of algorithms with varying levels of timing requirements to achieve their functional behavior. Historically, in certain systems, these algorithms were deployed on dedicated single-core hardware platforms that exchanged information over a real-time network, while more recent designs have adapted an integrated architecture where these algorithms are executed on an embedded multi-core hardware platform. The advantages provided by cloud and fog architectures for non-real-time applications have prompted discussions around the possibility of achieving similar advantages for systems such as industrial robot controllers by moving from an embedded architecture to a cloud and fog native architecture. This thesis addresses a subset of challenges related to scheduling to facilitate this transition and presents three main contributions aimed at improving online scheduling methodologies in multi-server systems for applications with real-time requirements. First, an approach based on minimum parallelism reservations is proposed for scheduling sequential tasks in hierarchical multi-server systems with clairvoyant inputs, ensuring adherence to hard real-time requirements. Second, a framework is introduced that utilizes estimated processing times to enhance average throughput in distributed multi-queue multi-server systems while managing tasks with stochastic inputs and firm real-time requirements, thereby improving resource utilization. Finally, competitive algorithms are proposed that leverage estimated processing times to minimize average (modified) tardiness in centralized single-queue multi-server systems, addressing the scheduling of sequential tasks with arbitrary arrivals and soft real-time requirements. Collectively, these contributions establish a robust foundation for improving the performance of real-time systems operating in increasingly complex environments characterized by dynamic workloads and varying resource availability.