Memory-Aware Scheduling for Fixed Priority Hard Real-Time Computing Systems

As a major component of a computing system, memory has been a key performance and power consumption bottleneck in computer system design. While processor speeds have been kept rising dramatically, the overall computing performance improvement of the entire system is limited by how fast the memory can feed instructions/data to processing units (i.e. so-called memory wall problem). The increasing transistor density and surging access demands from a rapidly growing number of processing cores also significantly elevated the power consumption of the memory system. In addition, the interference of memory access from different applications and processing cores significantly degrade the computation predictability, which is essential to ensure timing specifications in real-time system design. The recent IC technologies (such as 3D-IC technology) and emerging data-intensive real-time applications (such as Virtual Reality/Augmented Reality, Artificial Intelligence, Internet of Things) further amplify these challenges. We believe that it is not simply desirable but necessary to adopt a joint CPU/Memory resource management framework to deal with these grave challenges. In this dissertation, we focus on studying how to schedule fixed-priority hard real-time tasks with memory impacts taken into considerations. We target on the fixed-priority real-time scheduling scheme since this is one of the most commonly used strategies for practical real-time applications. Specifically, we first develop an approach that takes into consideration not only the execution time variations with cache allocations but also the task period relationship, showing a significant improvement in the feasibility of the system. We further study the problem of how to guarantee timing constraints for hard real-time systems under CPU and memory thermal constraints. We first study the problem under an architecture model with a single core and its main memory individually packaged. We develop a thermal model that can capture the thermal interaction between the processor and memory, and incorporate the periodic resource sever model into our scheduling framework to guarantee both the timing and thermal constraints. We further extend our research to the multi-core architectures with processing cores and memory devices integrated into a single 3D platform. To our best knowledge, this is the first research that can guarantee hard deadline constraints for real-time tasks under temperature constraints for both processing cores and memory devices. Extensive simulation results demonstrate that our proposed scheduling can improve significantly the feasibility of hard real-time systems under thermal constraints

Towards Efficient Explainability of Schedulability Properties in Real-Time Systems

The notion of efficient explainability was recently introduced in the context of hard-real-time scheduling: a claim that a real-time system is schedulable (i.e., that it will always meet all deadlines during run-time) is defined to be efficiently explainable if there is a proof of such schedulability that can be verified by a polynomial-time algorithm. We further explore this notion by (i) classifying a variety of common schedulability analysis problems according to whether they are efficiently explainable or not; and (ii) developing strategies for dealing with those determined to not be efficiently schedulable, primarily by identifying practically meaningful sub-problems that are efficiently explainable

On the Design of Real-Time Systems on Multi-Core Platforms under Uncertainty

Real-time systems are computing systems that demand the assurance of not only the logical correctness of computational results but also the timing of these results. To ensure timing constraints, traditional real-time system designs usually adopt a worst-case based deterministic approach. However, such an approach is becoming out of sync with the continuous evolution of IC technology and increased complexity of real-time applications. As IC technology continues to evolve into the deep sub-micron domain, process variation causes processor performance to vary from die to die, chip to chip, and even core to core. The extensive resource sharing on multi-core platforms also significantly increases the uncertainty when executing real-time tasks. The traditional approach can only lead to extremely pessimistic, and thus, unpractical design of real-time systems. Our research seeks to address the uncertainty problem when designing real-time systems on multi-core platforms. We first attacked the uncertainty problem caused by process variation. We proposed a virtualization framework and developed techniques to optimize the system\u27s performance under process variation. We further studied the problem on peak temperature minimization for real-time applications on multi-core platforms. Three heuristics were developed to reduce the peak temperature for real-time systems. Next, we sought to address the uncertainty problem in real-time task execution times by developing statistical real-time scheduling techniques. We studied the problem of fixed-priority real-time scheduling of implicit periodic tasks with probabilistic execution times on multi-core platforms. We further extended our research for tasks with explicit deadlines. We introduced the concept of harmonic to a more general task set, i.e. tasks with explicit deadlines, and developed new task partitioning techniques. Throughout our research, we have conducted extensive simulations to study the effectiveness and efficiency of our developed techniques. The increasing process variation and the ever-increasing scale and complexity of real-time systems both demand a paradigm shift in the design of real-time applications. Effectively dealing with the uncertainty in design of real-time applications is a challenging but also critical problem. Our research is such an effort in this endeavor, and we conclude this dissertation with discussions of potential future work

Energy-Efficient Multi-Core Scheduling for Real-Time DAG Tasks

In this work, we study energy-aware real-time scheduling of a set of sporadic Directed Acyclic Graph (DAG) tasks with implicit deadlines. While meeting all real-time constraints, we try to identify the best task allocation and execution pattern such that the average power consumption of the whole platform is minimized. To the best of our knowledge, this is the first work that addresses the power consumption issue in scheduling multiple DAG tasks on multi-cores and allows intra-task processor sharing. We first adapt the decomposition-based framework for federated scheduling and propose an energy-sub-optimal scheduler. Then we derive an approximation algorithm to identify processors to be merged together for further improvements in energy-efficiency and to prove the bound of the approximation ratio. We perform a simulation study to demonstrate the effectiveness and efficiency of the proposed scheduling. The simulation results show that our algorithms achieve an energy saving of 27% to 41% compared to existing DAG task schedulers

Real-Time Wireless Sensor-Actuator Networks for Cyber-Physical Systems

A cyber-physical system (CPS) employs tight integration of, and coordination between computational, networking, and physical elements. Wireless sensor-actuator networks provide a new communication technology for a broad range of CPS applications such as process control, smart manufacturing, and data center management. Sensing and control in these systems need to meet stringent real-time performance requirements on communication latency in challenging environments. There have been limited results on real-time scheduling theory for wireless sensor-actuator networks. Real-time transmission scheduling and analysis for wireless sensor-actuator networks requires new methodologies to deal with unique characteristics of wireless communication. Furthermore, the performance of a wireless control involves intricate interactions between real-time communication and control. This thesis research tackles these challenges and make a series of contributions to the theory and system for wireless CPS. (1) We establish a new real-time scheduling theory for wireless sensor-actuator networks. (2) We develop a scheduling-control co-design approach for holistic optimization of control performance in a wireless control system. (3) We design and implement a wireless sensor-actuator network for CPS in data center power management. (4) We expand our research to develop scheduling algorithms and analyses for real-time parallel computing to support computation-intensive CPS

An Energy-Efficient Semi-Partitioned Approach for Hard Real-Time Systems with Voltage and Frequency Islands

The shift from uniprocessor to multi-core architectures has made it difficult to design predictable hard real-time systems (HRTS) since guaranteeing deadlines while achieving high processor utilization remains a major challenge. In addition, due to increasing demands, energy efficiency has become an important design metric in HRTS. To obtain energy savings, most multi-core systems use dynamic voltage and frequency scaling (DVFS) to reduce dynamic power consumption when the system is underloaded. However, in many multi-core systems, DVFS is implemented using voltage and frequency islands (VFI), implying that individual cores cannot independently select their voltage and frequency (v/f) pairs, thus resulting in less energy savings when existing energy-aware task assignment and scheduling techniques are used. In this thesis, we present an analysis of the increase in energy consumption in the presence of VFI. Further, we propose a semi-partitioned approach called EDF-hv to reduce the energy consumption of HRTS on multi-core systems with VFI. Simulation results revealed that when workload imbalance among the cores is sufficiently high, EDF-hv can reduce system energy consumption by 15.9% on average

CPU Energy-Aware Parallel Real-Time Scheduling

Both energy-efficiency and real-time performance are critical requirements in many embedded systems applications such as self-driving car, robotic system, disaster response, and security/safety control. These systems entail a myriad of real-time tasks, where each task itself is a parallel task that can utilize multiple computing units at the same time. Driven by the increasing demand for parallel tasks, multi-core embedded processors are inevitably evolving to many-core. Existing work on real-time parallel tasks mostly focused on real-time scheduling without addressing energy consumption. In this paper, we address hard real-time scheduling of parallel tasks while minimizing their CPU energy consumption on multicore embedded systems. Each task is represented as a directed acyclic graph (DAG) with nodes indicating different threads of execution and edges indicating their dependencies. Our technique is to determine the execution speeds of the nodes of the DAGs to minimize the overall energy consumption while meeting all task deadlines. It incorporates a frequency optimization engine and the dynamic voltage and frequency scaling (DVFS) scheme into the classical real-time scheduling policies (both federated and global) and makes them energy-aware. The contributions of this paper thus include the first energy-aware online federated scheduling and also the first energy-aware global scheduling of DAGs. Evaluation using synthetic workload through simulation shows that our energy-aware real-time scheduling policies can achieve up to 68% energy-saving compared to classical (energy-unaware) policies. We have also performed a proof of concept system evaluation using physical hardware demonstrating the energy efficiency through our proposed approach

A Survey of Research into Mixed Criticality Systems

This survey covers research into mixed criticality systems that has been published since Vestal’s seminal paper in 2007, up until the end of 2016. The survey is organised along the lines of the major research areas within this topic. These include single processor analysis (including fixed priority and EDF scheduling, shared resources and static and synchronous scheduling), multiprocessor analysis, realistic models, and systems issues. The survey also explores the relationship between research into mixed criticality systems and other topics such as hard and soft time constraints, fault tolerant scheduling, hierarchical scheduling, cyber physical systems, probabilistic real-time systems, and industrial safety standards