Scheduling of Overload-Tolerant Computation and Multi-Mode Communication in Real-Time Systems

Real-time tasks require sufficient resources to meet deadline constraints. A component should provision sufficient resources for its workloads consisting of tasks to meet their deadlines. Supply and demand bound functions can be used to analyze the schedulability of workloads. The demand-bound function determines the maximum required computational units for a given workload and the supply-bound function determines the minimum possible resources supplied to the workload. A component will experience an overload if it receives fewer resources than required. An overload will be transient if it occurs for a bounded amount of time. Most work concentrates on designing components that avoid overloads by over-provisioning resources even though some computational units such as control system components can tolerate transient overloads. Overload-tolerant components can utilize resources more efficiently if over-provisioning of resources can be avoided. First, this dissertation presents the design of an efficient periodic resource model for scheduling computation of components that can tolerate transient overloads under the Earliest Deadline First (EDF) scheduling policy. We propose a periodic resource model for overload-tolerant components to address three problems: (1) characterize overloads and determine metrics of interest (i.e., delay), (2) derive a model to compute a periodic resource supply for a given workload and a worst-case tolerable delay, and (3) find a periodic resource supply for given control system specifications with a worst-case delay. The derived periodic resource supply can be used to derive an overload-tolerant component interface. Overload-tolerant real-time components can connect with each other in a distributed manner and thus require communication scheduling for reliable and guaranteed transmissions. Moreover, applications may require multi-mode communication for efficient data transmission. Second, this dissertation discusses communication schedules for multi-mode distributed components. Since distributed multi-mode applications are prone to suffer from delays incurred during mode changes, good communication schedules have low average mode-change delays. A key problem in designing multi-mode communication in real-time systems is the generation of schedules to move away the complexity of schedule design from the developer. We propose a mechanism to generate multi-mode communication schedules using optimization constraints associated with timing requirements. We illustrate a workflow from specifications to the generation of communication schedules through a real-time video monitoring case-study. Experimental analysis for the case-study demonstrates that schedules generated using the proposed method reduce the average mode-change delay compared to a randomized algorithm and the well-known EDF scheduling policy. Finally, this thesis discusses the synthesis of schedules for computation and communication to achieve not only performance but also separation of concerns for reducing complexity and increasing safety. To integrate overload-tolerant components using real-time communication, we derive specifications of component interfaces using the characterization of overloads and the proposed periodic resource model. The generation of communication schedules uses the specifications of interfaces which include timing requirements of possible transient overloads. A walk-through case-study explains the steps necessary to generate communication schedules using component interfaces. The interfaces provide safety through isolation of transient overload-tolerant components and the generated communication schedules provide high performance as a result of their low average mode-change delay

Mining Event Traces from Real-time Systems for Anomaly Detection

Real-time systems are a significant class of applications, poised to grow even further as autonomous vehicles and the Internet of Things (IoT) become a reality. The computation and communication tasks of the underlying embedded systems must comply with strict timing and safety requirements as undetected defects in these systems may lead to catastrophic failures. The runtime behavior of these systems is prone to uncertainties arising from dynamic workloads and extra-functional conditions that affect both the software and hardware over the course of their deployment, e.g., unscheduled firmware updates, communication channel saturation, power-saving mode switches, or external malicious attacks. The operation in such unpredictable environments prevents the detection of anomalous behavior using traditional formal modeling and analysis techniques as they generally consider worst-case analysis and tend to be overly conservative. To overcome these limitations, and primarily motivated by the increasing availability of generated traces from real-time embedded systems, this thesis presents TRACMIN - Trace Mining using Arrival Curves - which is an anomaly detection approach that empirically constructs arrival curves from event traces to capture the recurrent behavior and intrinsic features of a given real-time system. The thesis uses TRACMIN to fill the gap between formal analysis techniques of real-time systems and trace mining approaches that lack expressive, human-readable, and scalable methods. The thesis presents definitions, metrics, and tools to employ statistical learning techniques to cluster and classify traces generated from different modes of normal operation versus anomalous traces. Experimenting with multiple datasets from deployed real-time embedded systems facing performance degradation and hardware misconfiguration anomalies demonstrates the feasibility and viability of our approaches on timestamped event traces generated from an industrial real-time operating system. Acknowledging the high computation expense for constructing empirical arrival curves, the thesis provides a rapid algorithm to achieve desirable scalability on lengthy traces paving the way for adoption in research and industry. Finally, the thesis presents a robustness analysis for the arrival curves models by employing theories of demand-bound functions from the scheduling domain. The analysis provides bounds on how much disruption a real-time system modeled using our approach can tolerate before being declared anomalous, which is crucial for specification and certification purposes. In conclusion, TRACMIN combines empirical and theoretical methods to provide a concrete anomaly detection framework that uses robust models of arrival curves scalably constructed from event traces to detect anomalies that affect the recurrent behavior of a real-time system