Reservation-Based Federated Scheduling for Parallel Real-Time Tasks

This paper considers the scheduling of parallel real-time tasks with arbitrary-deadlines. Each job of a parallel task is described as a directed acyclic graph (DAG). In contrast to prior work in this area, where decomposition-based scheduling algorithms are proposed based on the DAG-structure and inter-task interference is analyzed as self-suspending behavior, this paper generalizes the federated scheduling approach. We propose a reservation-based algorithm, called reservation-based federated scheduling, that dominates federated scheduling. We provide general constraints for the design of such systems and prove that reservation-based federated scheduling has a constant speedup factor with respect to any optimal DAG task scheduler. Furthermore, the presented algorithm can be used in conjunction with any scheduler and scheduling analysis suitable for ordinary arbitrary-deadline sporadic task sets, i.e., without parallelism

Packing sporadic real-time tasks on identical multiprocessor systems

In real-time systems, in addition to the functional correctness recurrent tasks must fulfill timing constraints to ensure the correct behavior of the system. Partitioned scheduling is widely used in real-time systems, i.e., the tasks are statically assigned onto processors while ensuring that all timing constraints are met. The decision version of the problem, which is to check whether the deadline constraints of tasks can be satisfied on a given number of identical processors, has been known NP-complet

Packing Sporadic Real-Time Tasks on Identical Multiprocessor Systems

In real-time systems, in addition to the functional correctness recurrent tasks must fulfill timing constraints to ensure the correct behavior of the system. Partitioned scheduling is widely used in real-time systems, i.e., the tasks are statically assigned onto processors while ensuring that all timing constraints are met. The decision version of the problem, which is to check whether the deadline constraints of tasks can be satisfied on a given number of identical processors, has been known ${\cal NP}$-complete in the strong sense. Several studies on this problem are based on approximations involving resource augmentation, i.e., speeding up individual processors. This paper studies another type of resource augmentation by allocating additional processors, a topic that has not been explored until recently. We provide polynomial-time algorithms and analysis, in which the approximation factors are dependent upon the input instances. Specifically, the factors are related to the maximum ratio of the period to the relative deadline of a task in the given task set. We also show that these algorithms unfortunately cannot achieve a constant approximation factor for general cases. Furthermore, we prove that the problem does not admit any asymptotic polynomial-time approximation scheme (APTAS) unless ${\cal P}={\cal NP}$ when the task set has constrained deadlines, i.e., the relative deadline of a task is no more than the period of the task.Comment: Accepted and to appear in ISAAC 2018, Yi-Lan, Taiwa

k2U: A General Framework from k-Point Effective Schedulability Analysis to Utilization-Based Tests

To deal with a large variety of workloads in different application domains in real-time embedded systems, a number of expressive task models have been developed. For each individual task model, researchers tend to develop different types of techniques for deriving schedulability tests with different computation complexity and performance. In this paper, we present a general schedulability analysis framework, namely the k2U framework, that can be potentially applied to analyze a large set of real-time task models under any fixed-priority scheduling algorithm, on both uniprocessor and multiprocessor scheduling. The key to k2U is a k-point effective schedulability test, which can be viewed as a "blackbox" interface. For any task model, if a corresponding k-point effective schedulability test can be constructed, then a sufficient utilization-based test can be automatically derived. We show the generality of k2U by applying it to different task models, which results in new and improved tests compared to the state-of-the-art. Analogously, a similar concept by testing only k points with a different formulation has been studied by us in another framework, called k2Q, which provides quadratic bounds or utilization bounds based on a different formulation of schedulability test. With the quadratic and hyperbolic forms, k2Q and k2U frameworks can be used to provide many quantitive features to be measured, like the total utilization bounds, speed-up factors, etc., not only for uniprocessor scheduling but also for multiprocessor scheduling. These frameworks can be viewed as a "blackbox" interface for schedulability tests and response-time analysis

Energy-Efficient Transaction Scheduling in Data Systems

Natural short term fluctuations in the load of transactional data systems present an opportunity for power savings. For example, a system handling 1000 requests per second on average can expect more than 1000 requests in some seconds, fewer in others. By quickly adjusting processing capacity to match such fluctuations, power consumption can be reduced. Many systems do this already, using dynamic voltage and frequency scaling (DVFS) to reduce processor performance and power consumption when the load is low. DVFS is typically controlled by frequency governors in the operating system or by the processor itself. The work presented in this dissertation shows that transactional data systems can manage DVFS more effectively than the underlying operating system. This is because data systems have more information about the workload, and more control over that workload, than is available to the operating system. Our goal is to minimize power consumption while ensuring that transaction requests meet specified latency targets. We present energy-efficient scheduling algorithms and systems that manage CPU power consumption and performance within data systems. These algorithms are workload-aware and can accommodate concurrent workloads with different characteristics and latency budgets. The first technique we present is called POLARIS. It directly manages processor DVFS and controls database transaction scheduling. We show that POLARIS can simultaneously reduce power consumption and reduce missed latency targets, relative to operating-system-based DVFS governors. Second, we present PLASM, an energy-efficient scheduler that generalizes POLARIS to support multi-core, multi-processor systems. PLASM controls the distribution of requests to the processors, and it employs POLARIS to manage power consumption locally at each core. We show that PLASM can save power and reduce missed latency targets compared to generic routing techniques such as round-robin

Integrating security into real-time cyber-physical systems

Cyber-physical systems (CPS) such as automobiles, power plants, avionics systems, unmanned vehicles, medical devices, manufacturing and home automation systems have distinct cyber and physical components that must work cohesively with each other to ensure correct operation. Many cyber-physical applications have “real-time” constraints, i.e., they must function correctly within predetermined time scales. A failure to protect these systems could result in significant harm to humans, the system or even the environment. While traditionally such systems were isolated from external accesses and used proprietary components and protocols, modern CPS use off-the-shelf components and are increasingly interconnected, often via networks such as the Internet. As a result, they are exposed to additional attack surfaces and have become increasingly vulnerable to cyber attacks. Enhancing security for real-time CPS, however, is not an easy task due to limited resource availability (e.g., processing power, memory, storage, energy) and stringent timing/safety requirements. Security monitoring techniques for cyber-physical platforms (a) must execute with existing real-time tasks, (b) operate without impacting the timing and safety constraints of the control logic and (c) have to be designed and executed in a way that an adversary cannot easily evade it. The objective of my research is to increase security posture of embedded real-time CPS by integrating monitoring/detection techniques that defeat cyber attacks without violating timing/safety constraints of existing tasks. My dissertation work explores the real-time security domain and shows that by employing a combination of multiple scheduling/analysis techniques and interactions between hardware/software-based security extensions, it becomes feasible to integrate security monitoring mechanisms in real-time CPS without compromising timing/safety requirements of existing tasks. In this research, I (a) develop techniques to raise the responsiveness of security monitoring tasks by increasing their frequency of execution, (b) design a hardware-supported framework to prevent falsification of actuation commands — i.e., commands that control the state of the physical system and (c) propose metrics to trade-off security with real-time guarantees. The solutions presented in this dissertation require minimal changes to system components/parameters and thus compatible for legacy systems. My proposed frameworks and results are evaluated through both, simulations and experiments on real off-the-shelf cyber-physical platforms. The development of analysis techniques and design frameworks proposed in this dissertation will inherently make such systems more secure and hence, safer. I believe my dissertation work will bring researchers and system engineers one step closer to understand how to integrate two seemingly diverse yet important fields — real-time CPS and cyber-security — while gaining a better understanding of both areas