Analyses and optimizations of timing-constrained embedded systems considering resource synchronization and machine learning approaches

Nowadays, embedded systems have become ubiquitous, powering a vast array of applications from consumer electronics to industrial automation. Concurrently, statistical and machine learning algorithms are being increasingly adopted across various application domains, such as medical diagnosis, autonomous driving, and environmental analysis, offering sophisticated data analysis and decision-making capabilities. As the demand for intelligent and time-sensitive applications continues to surge, accompanied by growing concerns regarding data privacy, the deployment of machine learning models on embedded devices has emerged as an indispensable requirement. However, this integration introduces both significant opportunities for performance enhancement and complex challenges in deployment optimization. On the one hand, deploying machine learning models on embedded systems with limited computational capacity, power budgets, and stringent timing requirements necessitates additional adjustments to ensure optimal performance and meet the imposed timing constraints. On the other hand, the inherent capabilities of machine learning, such as self-adaptation during runtime, prove invaluable in addressing challenges encountered in embedded systems, aiding in optimization and decision-making processes. This dissertation introduces two primary modifications for the analyses and optimizations of timing-constrained embedded systems. For one thing, it addresses the relatively long access times required for shared resources of machine learning tasks. For another, it considers the limited communication resources and data privacy concerns in distributed embedded systems when deploying machine learning models. Additionally, this work provides a use case that employs a machine learning method to tackle challenges specific to embedded systems. By addressing these key aspects, this dissertation contributes to the analysis and optimization of timing-constrained embedded systems, considering resource synchronization and machine learning models to enable improved performance and efficiency in real-time applications with stringent constraints

Computer-aided programming for multiprocessing systems

As both the number of processors and the complexity of problems to be solved increase, programming multiprocessing systems becomes more difficult and error-prone. This report discusses parallel models of computation and tools for computer-aided programming (CAP). Program development tools are necessary since programmers are not able to develop complex parallel programs efficiently. In particular, a CAP tool, named Hypertool, is described here. It performs scheduling and handles the communication primitive insertion automatically so that many errors are eliminated. It also generates the performance estimates and other program quality measures to help programmers in improving their algorithms and programs. Experiments have shown that up to a 300% performance improvement can be achieved by computer-aided programming

Semi-Partitioned Scheduling of Dynamic Real-Time Workload: A Practical Approach Based on Analysis-Driven Load Balancing

Recent work showed that semi-partitioned scheduling can achieve near-optimal schedulability performance, is simpler to implement compared to global scheduling, and less heavier in terms of runtime overhead, thus resulting in an excellent choice for implementing real-world systems. However, semi-partitioned scheduling typically leverages an off-line design to allocate tasks across the available processors, which requires a-priori knowledge of the workload. Conversely, several simple global schedulers, as global earliest-deadline first (G-EDF), can transparently support dynamic workload without requiring a task-allocation phase. Nonetheless, such schedulers exhibit poor worst-case performance. This work proposes a semi-partitioned approach to efficiently schedule dynamic real-time workload on a multiprocessor system. A linear-time approximation for the C=D splitting scheme under partitioned EDF scheduling is first presented to reduce the complexity of online scheduling decisions. Then, a load-balancing algorithm is proposed for admitting new real-time workload in the system with limited workload re-allocation. A large-scale experimental study shows that the linear-time approximation has a very limited utilization loss compared to the exact technique and the proposed approach achieves very high schedulability performance, with a consistent improvement on G-EDF and pure partitioned EDF scheduling

From Dataflow Specification to Multiprocessor Partitioned Time-triggered Real-time Implementation *

International audienceOur objective is to facilitate the development of complex time-triggered systems by automating the allocation and scheduling steps. We show that full automation is possible while taking into account the elements of complexity needed by a complex embedded control system. More precisely, we consider deterministic functional specifications provided (as often in an industrial setting) by means of synchronous data-flow models with multiple modes and multiple relative periods. We first extend this functional model with an original real-time characterization that takes advantage of our time-triggered framework to provide a simpler representation of complex end-to-end flow requirements. We also extend our specifications with additional non-functional properties specifying partitioning, allocation , and preemptability constraints. Then, weprovide novel algorithms for the off-line scheduling of these extended specifications onto partitioned time-triggered architectures à la ARINC 653. The main originality of our work is that it takes into account at the same time multiple complexity elements: various types of non-functional properties (real-time, partitioning, allocation, preemptability) and functional specifications with conditional execution and multiple modes. Allocation of time slots/windows to partitions can be fullyor partially provided, or synthesized by our tool. Our algorithms allow the automatic allocation and scheduling onto multi-processor (distributed) sys-tems with a global time base, taking into account communication costs. We demonstrate our technique on a model of space flight software systemwith strong real-time determinism requirements

Semi-Partitioned Scheduling of Dynamic Real-Time Workload: A Practical Approach Based on Analysis-Driven Load Balancing

Recent work showed that semi-partitioned scheduling can achieve near-optimal schedulability performance, is simpler to implement compared to global scheduling, and less heavier in terms of runtime overhead, thus resulting in an excellent choice for implementing real-world systems. However, semi-partitioned scheduling typically leverages an off-line design to allocate tasks across the available processors, which requires a-priori knowledge of the workload. Conversely, several simple global schedulers, as global earliest-deadline first (G-EDF), can transparently support dynamic workload without requiring a task-allocation phase. Nonetheless, such schedulers exhibit poor worst-case performance. This work proposes a semi-partitioned approach to efficiently schedule dynamic real-time workload on a multiprocessor system. A linear-time approximation for the C=D splitting scheme under partitioned EDF scheduling is first presented to reduce the complexity of online scheduling decisions. Then, a load-balancing algorithm is proposed for admitting new real-time workload in the system with limited workload re-allocation. A large-scale experimental study shows that the linear-time approximation has a very limited utilization loss compared to the exact technique and the proposed approach achieves very high schedulability performance, with a consistent improvement on G-EDF and pure partitioned EDF scheduling

Multi-resource management in embedded real-time systems

This thesis addresses the problem of online multi-resource management in embedded real-time systems. It focuses on three research questions. The first question concentrates on how to design an efficient hierarchical scheduling framework for supporting independent development and analysis of component based systems, to provide temporal isolation between components. The second question investigates how to change the mapping of resources to tasks and components during run-time efficiently and predictably, and how to analyze the latency of such a system mode change in systems comprised of several scalable components. The third question deals with the scheduling and analysis of a set of parallel-tasks with real-time constraints which require simultaneous access to several different resources. For providing temporal isolation we chose a reservation-based approach. We first focused on processor reservations, where timed events play an important role. Common examples are task deadlines, periodic release of tasks, budget replenishment and budget depletion. Efficient timer management is therefore essential. We investigated the overheads in traditional timer management techniques and presented a mechanism called Relative Timed Event Queues (RELTEQ), which provides an expressive set of primitives at a low processor and memory overhead. We then leveraged RELTEQ to create an efficient, modular and extensible design for enhancing a real-time operating system with periodic tasks, polling, idling periodic and deferrable servers, and a two-level fixed-priority Hierarchical Scheduling Framework (HSF). The HSF design provides temporal isolation and supports independent development of components by separating the global and local scheduling, and allowing each server to define a dedicated scheduler. Furthermore, the design addresses the system overheads inherent to an HSF and prevents undesirable interference between components. It limits the interference of inactive servers on the system level by means of wakeup events and a combination of inactive server queues with a stopwatch queue. Our implementation is modular and requires only a few modifications of the underlying operating system. We then investigated scalable components operating in a memory-constrained system. We first showed how to reduce the memory requirements in a streaming multimedia application, based on a particular priority assignment of the different components along the processing chain. Then we investigated adapting the resource provisions to tasks during runtime, referred to as mode changes. We presented a novel mode change protocol called Swift Mode Changes, which relies on Fixed Priority with Deferred preemption Scheduling to reduce the mode change latency bound compared to existing protocols based on Fixed Priority Preemptive Scheduling. We then presented a new partitioned parallel-task scheduling algorithm called Parallel-SRP (PSRP), which generalizes MSRP for multiprocessors, and the corresponding schedulability analysis for the problem of multi-resource scheduling of parallel tasks with real-time constraints. We showed that the algorithm is deadlock-free, derived a maximum bound on blocking, and used this bound as a basis for a schedulability test. We then demonstrated how PSRP can exploit the inherent parallelism of a platform comprised of multiple heterogeneous resources. Finally, we presented Grasp, which is a visualization toolset aiming to provide insight into the behavior of complex real-time systems. Its flexible plugin infrastructure allows for easy extension with custom visualization and analysis techniques for automatic trace verification. Its capabilities include the visualization of hierarchical multiprocessor systems, including partitioned and global multiprocessor scheduling with migrating tasks and jobs, communication between jobs via shared memory and message passing, and hierarchical scheduling in combination with multiprocessor scheduling. For tracing distributed systems with asynchronous local clocks Grasp also supports the synchronization of traces from different processors during the visualization and analysis