Tight Tardiness Bounds for Pseudo-Harmonic Tasks Under Global-EDF-Like Schedulers

The global earliest-deadline-first (GEDF) scheduler and its variants are soft-real-time (SRT) optimal for periodic/sporadic tasks, meaning they provide bounded tardiness so long as the underlying platform is not over-utilized. Although their SRT-optimality has long been known, tight tardiness bounds for these schedulers have remained elusive. In this paper, a tardiness bound, that does not depend on the processor or task count, is derived for pseudo-harmonic periodic tasks, which are commonly used in practice, under global-EDF-like (GEL) schedulers. This class of schedulers includes both GEDF and first-in-first-out (FIFO). This bound is shown to be generally tight via an example. Furthermore, it is shown that exact tardiness bounds for GEL-scheduled pseudo-harmonic periodic tasks can be computed in pseudo-polynomial time

Least space-time first scheduling algorithm : scheduling complex tasks with hard deadline on parallel machines

Both time constraints and logical correctness are essential to real-time systems and failure to specify and observe a time constraint may result in disaster. Two orthogonal issues arise in the design and analysis of real-time systems: one is the specification of the system, and the semantic model describing the properties of real-time programs; the other is the scheduling and allocation of resources that may be shared by real-time program modules. The problem of scheduling tasks with precedence and timing constraints onto a set of processors in a way that minimizes maximum tardiness is here considered. A new scheduling heuristic, Least Space Time First (LSTF), is proposed for this NP-Complete problem. Basic properties of LSTF are explored; for example, it is shown that (1) LSTF dominates Earliest-Deadline-First (EDF) for scheduling a set of tasks on a single processor (i.e., if a set of tasks are schedulable under EDF, they are also schedulable under LSTF); and (2) LSTF is more effective than EDF for scheduling a set of independent simple tasks on multiple processors. Within an idealized framework, theoretical bounds on maximum tardiness for scheduling algorithms in general, and tighter bounds for LSTF in particular, are proven for worst case behavior. Furthermore, simulation benchmarks are developed, comparing the performance of LSTF with other scheduling disciplines for average case behavior. Several techniques are introduced to integrate overhead (for example, scheduler and context switch) and more realistic assumptions (such as inter-processor communication cost) in various execution models. A workload generator and symbolic simulator have been implemented for comparing the performance of LSTF (and a variant -- LSTF+) with that of several standard scheduling algorithms. LSTF\u27s execution model, basic theories, and overhead considerations have been defined and developed. Based upon the evidence, it is proposed that LSTF is a good and practical scheduling algorithm for building predictable, analyzable, and reliable complex real-time systems. There remain some open issues to be explored, such as relaxing some current restrictions, discovering more properties and theorems of LSTF under different models, etc. We strongly believe that LSTF can be a practical scheduling algorithm in the near future

REAL-TIME SCHEDULING ON ASYMMETRIC MULTIPROCESSOR PLATFORMS

Real-time scheduling analysis is crucial for time-critical systems, in which provable timing guarantees are more important than observed raw performance. Techniques for real-time scheduling analysis initially targeted uniprocessor platforms but have since evolved to encompass multiprocessor platforms. However, work directed at multiprocessors has largely focused on symmetric platforms, in which every processor is identical. Today, it is common for a multiprocessor to include heterogeneous processing elements, as this offers advantages with respect to size, weight, and power (SWaP) limitations. As a result, realizing modern real-time systems on asymmetric multiprocessor platforms is an inevitable trend. Unfortunately, principles and mechanisms regarding real-time scheduling on such platforms are relatively lacking. The goal of this dissertation is to enrich such principles and mechanisms, by bridging existing analysis for symmetric multiprocessor platforms to asymmetric ones and by developing new techniques that are unique for asymmetric multiprocessor platforms. The specific contributions are threefold. First, for a platform consisting of processors that differ with respect to processing speeds only, this dissertation shows that the preemptive global earliest-deadline-first (G-EDF) scheduler is optimal for scheduling soft real-time (SRT) task systems. Furthermore, it shows that semi-partitioned scheduling, which is a hybrid of conventional global and partitioned scheduling approaches, can be applied to optimally schedule both hard real-time (HRT) and SRT task systems. Second, on platforms that consist of processors with different functionalities, tasks that belong to different functionalities may process the same source data consecutively and therefore have producer/consumer relationships among them, which are represented by directed acyclic graphs (DAGs). End-to-end response-time bounds for such DAGs are derived in this dissertation under a G-EDF-based scheduling approach, and it is shown that such bounds can be improved by a linear-programming-based deadline-setting technique. Third, processor virtualization can lead a symmetric physical platform to be asymmetric. In fact, for a designated virtual-platform capacity, there exist an infinite number of allocation schemes for virtual processors and a choice must be made. In this dissertation, a particular asymmetric virtual-processor allocation scheme, called minimum-parallelism (MP) form, is shown to dominate all other schemes including symmetric ones.Doctor of Philosoph

Compositional Analysis Techniques For Multiprocessor Soft Real-Time Scheduling

The design of systems in which timing constraints must be met (real-time systems) is being affected by three trends in hardware and software development. First, in the past few years, multiprocessor and multicore platforms have become standard in desktop and server systems and continue to expand in the domain of embedded systems. Second, real-time concepts are being applied in the design of general-purpose operating systems (like Linux) and attempts are being made to tailor these systems to support tasks with timing constraints. Third, in many embedded systems, it is now more economical to use a single multiprocessor instead of several uniprocessor elements; this motivates the need to share the increasing processing capacity of multiprocessor platforms among several applications supplied by different vendors and each having different timing constraints in a manner that ensures that these constraints were met. These trends suggest the need for mechanisms that enable real-time tasks to be bundled into multiple components and integrated in larger settings. There is a substantial body of prior work on the multiprocessor schedulability analysis of real-time systems modeled as periodic and sporadic task systems. Unfortunately, these standard task models can be pessimistic if long chains of dependent tasks are being analyzed. In work that introduces less pessimistic and more sophisticated workload models, only partitioned scheduling is assumed so that each task is statically assigned to some processor. This results in pessimism in the amount of needed processing resources. In this dissertation, we extend prior work on multiprocessor soft real-time scheduling and construct new analysis tools that can be used to design component-based soft real-time systems. These tools allow multiprocessor real-time systems to be designed and analyzed for which standard workload and platform models are inapplicable and for which state-of-the-art uniprocessor and multiprocessor analysis techniques give results that are too pessimistic

Real-Time Scheduling for GPUs with Applications in Advanced Automotive Systems

Self-driving cars, once constrained to closed test tracks, are beginning to drive alongside human drivers on public roads. Loss of life or property may result if the computing systems of automated vehicles fail to respond to events at the right moment. We call such systems that must satisfy precise timing constraints “real-time systems.” Since the 1960s, researchers have developed algorithms and analytical techniques used in the development of real-time systems; however, this body of knowledge primarily applies to traditional CPU-based platforms. Unfortunately, traditional platforms cannot meet the computational requirements of self-driving cars without exceeding the power and cost constraints of commercially viable vehicles. We argue that modern graphics processing units, or GPUs, represent a feasible alternative, but new algorithms and analytical techniques must be developed in order to integrate these uniquely constrained processors into a real-time system. The goal of the research presented in this dissertation is to discover and remedy the issues that prevent the use of GPUs in real-time systems. To overcome these issues, we design and implement a real-time multi-GPU scheduler, called GPUSync. GPUSync tightly controls access to a GPU’s computational and DMA processors, enabling simultaneous use despite potential limitations in GPU hardware. GPUSync enables tasks to migrate among GPUs, allowing new classes of real-time multi-GPU computing platforms. GPUSync employs heuristics to guide scheduling decisions to improve system efficiency without risking violations in real-time constraints. GPUSync may be paired with a wide variety of common real-time CPU schedulers. GPUSync supports closed-source GPU runtimes and drivers without loss in functionality. We evaluate GPUSync with both analytical and runtime experiments. In our analytical experiments, we model and evaluate over fifty configurations of GPUSync. We determine which configurations support the greatest computational capacity while maintaining real-time constraints. In our runtime experiments, we execute computer vision programs similar to those found in automated vehicles, with and without GPUSync. Our results demonstrate that GPUSync greatly reduces jitter in video processing. Research into real-time systems with GPUs is a new area of study. Although there is prior work on such systems, no other GPU scheduling framework is as comprehensive and flexible as GPUSync.Doctor of Philosoph

Scheduling and locking in multiprocessor real-time operating systems

With the widespread adoption of multicore architectures, multiprocessors are now a standard deployment platform for (soft) real-time applications. This dissertation addresses two questions fundamental to the design of multicore-ready real-time operating systems: (1) Which scheduling policies offer the greatest flexibility in satisfying temporal constraints; and (2) which locking algorithms should be used to avoid unpredictable delays? With regard to Question 1, LITMUSRT, a real-time extension of the Linux kernel, is presented and its design is discussed in detail. Notably, LITMUSRT implements link-based scheduling, a novel approach to controlling blocking due to non-preemptive sections. Each implemented scheduler (22 configurations in total) is evaluated under consideration of overheads on a 24-core Intel Xeon platform. The experiments show that partitioned earliest-deadline first (EDF) scheduling is generally preferable in a hard real-time setting, whereas global and clustered EDF scheduling are effective in a soft real-time setting. With regard to Question 2, real-time locking protocols are required to ensure that the maximum delay due to priority inversion can be bounded a priori. Several spinlock- and semaphore-based multiprocessor real-time locking protocols for mutual exclusion (mutex), reader-writer (RW) exclusion, and k-exclusion are proposed and analyzed. A new category of RW locks suited to worst-case analysis, termed phase-fair locks, is proposed and three efficient phase-fair spinlock implementations are provided (one with few atomic operations, one with low space requirements, and one with constant RMR complexity). Maximum priority-inversion blocking is proposed as a natural complexity measure for semaphore protocols. It is shown that there are two classes of schedulability analysis, namely suspension-oblivious and suspension-aware analysis, that yield two different lower bounds on blocking. Five asymptotically optimal locking protocols are designed and analyzed: a family of mutex, RW, and k-exclusion protocols for global, partitioned, and clustered scheduling that are asymptotically optimal in the suspension-oblivious case, and a mutex protocol for partitioned scheduling that is asymptotically optimal in the suspension-aware case. A LITMUSRT-based empirical evaluation is presented that shows these protocols to be practical

Cluster Based Real Time Scheduling for Distributed System

Real time tasks scheduling on a distributed system is a complex problem. The existing real time tasks scheduling techniques are primarily based on partitioned and global scheduling. In partitioned based scheduling the tasks are assigned on a dedicated processor. The advantages of partitioned based approach is existing uni-processor scheduling techniques can be used; no migration overheads but task assignment is NP hard problem and optimal utilization of processing nodes is not possible. In global scheduling all tasks are maintained in a single tasks queue and allocated to multiple processing nodes. The advantage of global scheduling is optimal utilization of processing nodes but suffer from high migration and preemption overheads. This paper proposed cluster based real time tasks scheduling on a distributed system which is a hybrid scheduling approach where processing nodes group into cluster and scheduling using global scheduling. The simulation result shows that the proposed scheduling increases the tasks acceptance ratio, resource utilization as compared to partitioned and global scheduling and reduces migration as well as preemption overheads

Sharing Non-Processor Resources in Multiprocessor Real-Time Systems

Computing devices are increasingly being leveraged in cyber-physical systems, in which computing devices sense, control, and interact with the physical world. Associated with many such real-world interactions are strict timing constraints, which if unsatisfied, can lead to catastrophic consequences. Modern examples of such timing constraints are prevalent in automotive systems, such as airbag controllers, anti-lock brakes, and new autonomous features. In all of these examples, a failure to correctly respond to an event in a timely fashion could lead to a crash, damage, injury and even loss of life. Systems with imperative timing constraints are called real-time systems, and are broadly the subject of this dissertation. Much previous work on real-time systems and scheduling theory assumes that computing tasks are independent, i.e., the only resource they share is the platform upon which they are executed. In practice, however, tasks share many resources, ranging from more overt resources such as shared memory objects, to less overt ones, including data buses and other hardware and I/O devices. Accesses to some such resources must be synchronized to ensure safety, i.e., logical correctness, while other resources may exhibit better run-time performance if accesses are explicitly synchronized. The goal of this dissertation was to develop new synchronization algorithms and associated analysis techniques that can be used to synchronize access to many classes of resources, while improving the overall resource utilization, specifically as measured by real-time schedulability. Towards that goal, the Real-Time Nested Locking Protocol (RNLP), the first multiprocessor real-time locking protocol that supports lock nesting or fine-grained locking is proposed and analyzed. Furthermore, the RNLP is extended to support reader/writer locking, as well as k-exclusion locking. All presented RNLP variants are proven optimal. Furthermore, experimental results demonstrate the schedulability-related benefits of the RNLP. Additionally, three new synchronization algorithms are presented, which are specifically motivated by the need to manage shared hardware resources to improve real-time predictability. Furthermore, two new classes of shared resources are defined, and the first synchronization algorithms for them are proposed. To analyze these new algorithms, a novel analysis technique called idleness analysis is presented, which can be used to incorporate the effects of blocking into schedulability analysis.Doctor of Philosoph

A Reconfigurable Processor for Heterogeneous Multi-Core Architectures

A reconfigurable processor is a general-purpose processor coupled with an FPGA-like reconfigurable fabric. By deploying application-specific accelerators, performance for a wide range of applications can be improved with such a system. In this work concepts are designed for the use of reconfigurable processors in multi-tasking scenarios and as part of multi-core systems

Embedded System Design

A unique feature of this open access textbook is to provide a comprehensive introduction to the fundamental knowledge in embedded systems, with applications in cyber-physical systems and the Internet of things. It starts with an introduction to the field and a survey of specification models and languages for embedded and cyber-physical systems. It provides a brief overview of hardware devices used for such systems and presents the essentials of system software for embedded systems, including real-time operating systems. The author also discusses evaluation and validation techniques for embedded systems and provides an overview of techniques for mapping applications to execution platforms, including multi-core platforms. Embedded systems have to operate under tight constraints and, hence, the book also contains a selected set of optimization techniques, including software optimization techniques. The book closes with a brief survey on testing. This fourth edition has been updated and revised to reflect new trends and technologies, such as the importance of cyber-physical systems (CPS) and the Internet of things (IoT), the evolution of single-core processors to multi-core processors, and the increased importance of energy efficiency and thermal issues