Scheduling Mixed-Criticality Real-Time Systems

This dissertation addresses the following question to the design of scheduling policies and resource allocation mechanisms in contemporary embedded systems that are implemented on integrated computing platforms: in a multitasking system where it is hard to estimate a task's worst-case execution time, how do we assign task priorities so that 1) the safety-critical tasks are asserted to be completed within a specified length of time, and 2) the non-critical tasks are also guaranteed to be completed within a predictable length of time if no task is actually consuming time at the worst case? This dissertation tries to answer this question based on the mixed-criticality real-time system model, which defines multiple worst-case execution scenarios, and demands a scheduling policy to provide provable timing guarantees to each level of critical tasks with respect to each type of scenario. Two scheduling algorithms are proposed to serve this model. The OCBP algorithm is aimed at discrete one-shot tasks with an arbitrary number of criticality levels. The EDF-VD algorithm is aimed at recurrent tasks with two criticality levels (safety-critical and non-critical). Both algorithms are proved to optimally minimize the percentage of computational resource waste within two criticality levels. More in-depth investigations to the relationship among the computational resource requirement of different criticality levels are also provided for both algorithms.Doctor of Philosoph

Load Profiling in Distributed Real-Time Systems

Load balancing is often used to ensure that nodes in a distributed systems are equally loaded. In this paper, we show that for real-time systems, load balancing is not desirable. In particular, we propose a new load-profiling strategy that allows the nodes of a distributed system to be unequally loaded. Using load profiling, the system attempts to distribute the load amongst its nodes so as to maximize the chances of finding a node that would satisfy the computational needs of incoming real-time tasks. To that end, we describe and evaluate a distributed load-profiling protocol for dynamically scheduling time-constrained tasks in a loosely-coupled distributed environment. When a task is submitted to a node, the scheduling software tries to schedule the task locally so as to meet its deadline. If that is not feasible, it tries to locate another node where this could be done with a high probability of success, while attempting to maintain an overall load profile for the system. Nodes in the system inform each other about their state using a combination of multicasting and gossiping. The performance of the proposed protocol is evaluated via simulation, and is contrasted to other dynamic scheduling protocols for real-time distributed systems. Based on our findings, we argue that keeping a diverse availability profile and using passive bidding (through gossiping) are both advantageous to distributed scheduling for real-time systems.National Science Foundation (CCR-9308344

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

A NOVEL MESSAGE ROUTING LAYER FOR THE COMMUNICATION MANAGEMENT OF DISTRIBUTED EMBEDDED SYSTEMS

Fault tolerant and distributed embedded systems are research areas that have the interest of such entities as NASA, the Department of Defense, and various other government agencies, corporations, and universities. Taking a system and designing it to work in the presence of faults is appealing to these entities as it inherently increases the reliability of the deployed system. There are a few different fault tolerant techniques that can be implemented in a system design to handle faults as they occur. One such technique is the reconfiguration of a portion of the system to a redundant resource. This is a difficult task to manage within a distributed embedded system because of the distributed, directly addressed data producer and consumer dependencies that exist in common network infrastructures. It is the goal of this thesis work to develop a novel message routing layer for the communication management of distributed embedded systems that reduces the complexity of this problem. The resulting product of this thesis provides a robust approach to the design, implementation, integration, and deployment of a distributed embedded system

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