https://www.mdu.se/

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.