An EDF-based restricted-migration scheduling algorithm for multiprocessor soft real-time systems

There has been much recent interest in the use of the earliest-deadline-first (EDF) algorithm for scheduling soft real-time sporadic task systems on identical multiprocessors. In hard real-time systems, a significant disparity exists between EDF-based schemes and Pfair scheduling: on M processors, the worst-case schedulable utilization for all known EDF variants is approximately M/2, whereas it is M for optimal Pfair algorithms. This is unfortunate because EDF-based algorithms entail lower scheduling and task-migration overheads. However, such a disparity in schedulability can be alleviated by easing the requirement that all deadlines be met, which may be sufficient for soft real-time systems. In particular, in recent work, we have shown that if task migrations are not restricted, then EDF (i.e., global EDF) can ensure bounded tardiness for a sporadic task system with no restrictions on total utilization. Unrestricted task migrations in global EDF may be unappealing for some systems, but if migrations are forbidden entirely, then bounded tardiness cannot be guaranteed. In this paper, we address the issue of striking a balance between task migrations and system utilization by proposing an algorithm called EDF-fm, which is based upon EDF and treads a middle path, by restricting, but not eliminating, task migrations. Specifically, under EDF-fm, the ability to migrate is required for at most M − 1 tasks, and it is sufficient that every such task migrate between two processors and at job boundaries only. EDF-fm, like global EDF, can ensure bounded tardiness to a sporadic task system as long as the available processing capacity is not exceeded, but, unlike global EDF, may require that per-task utilizations be capped. The required cap is quite liberal, hence, EDF-fm should enable a wide range of soft real-time applications to be scheduled with no constraints on total utilization

Soft real-time scheduling on multiprocessors

The design of real-time systems is being impacted by two trends. First, tightly-coupled multiprocessor platforms are becoming quite common. This is evidenced by the availability of affordable symmetric shared-memory multiprocessors and the emergence of multicore architectures. Second, there is an increase in the number of real-time systems that require only soft real-time guarantees and have workloads that necessitate a multiprocessor. Examples of such systems include some tracking, signal-processing, and multimedia systems. Due to the above trends, cost-effective multiprocessor-based soft real-time system designs are of growing importance. Most prior research on real-time scheduling on multiprocessors has focused only on hard real-time systems. In a hard real-time system, no deadline may ever be missed. To meet such stringent timing requirements, all known theoretically optimal scheduling algorithms tend to preempt process threads and migrate them across processors frequently, and also impose certain other restrictions. Hence, the overheads of such algorithms can significantly reduce the amount of useful work that is accomplished and limit their practical implementation. On the other hand, non-optimal algorithms that are more practical suffer from the drawback that their validation tests require workload restrictions that can approach roughly 50% of the available processing capacity. Thus, for soft real-time systems, which can tolerate occasional or bounded deadline misses, and hence, allow for a tradeoff between timeliness and improved processor utilization, the existing scheduling algorithms or their validation tests can be overkill. The thesis of this dissertation is: Processor utilization can be improved on multiprocessors while providing non-trivial soft real-time guarantees for different soft real-time applications, whose preemption and migration overheads can span different ranges and whose tolerances to tardiness are different, by designing new algorithms, simplifying optimal algorithms, and developing new validation tests. The above thesis is established by developing validation tests that are sufficient to provide soft real-time guarantees under non-optimal (but more practical) algorithms, designing and analyzing a new restricted-migration scheduling algorithm, determining the guarantees on timeliness that can be provided when some limiting restrictions of known optimal algorithms are relaxed, and quantifying the benefits of the proposed mechanisms through simulations. First, we show that both preemptive and non-preemptive global earliest-deadline-first(EDF) scheduling can guarantee bounded tardiness (that is, lateness) to every recurrent real-time task system while requiring no restriction on the workload (except that it not exceed the available processing capacity). The tardiness bounds that we derive can be used to devise validation tests for soft real-time systems that are EDF-scheduled. Though overheads due to migrations and other factors are lower under EDF (than under known optimal algorithms), task migrations are still unrestricted. This may be unappealing for some applications, but if migrations are forbidden entirely, then bounded tardiness cannot always be guaranteed. Hence, we consider providing an acceptable middle path between unrestricted-migration and no-migration algorithms, and as a second result, present a new algorithm that restricts, but does not eliminate, migrations. We also determine bounds on tardiness that can be guaranteed under this algorithm. Finally, we consider a more efficient but non-optimal variant of an optimal class of algorithms called Pfair scheduling algorithms. We show that under this variant, called earliest- pseudo-deadline-first (EPDF) scheduling, significantly more liberal restrictions on workloads than previously known are sufficient for ensuring a specified tardiness bound. We also show that bounded tardiness can be guaranteed if some limiting restrictions of optimal Pfair algorithms are relaxed. The algorithms considered in this dissertation differ in the tardiness bounds guaranteed and overheads imposed. Simulation studies show that these algorithms can guarantee bounded tardiness for a significant percentage of task sets that are not schedulable in a hard real-time sense. Furthermore, for each algorithm, conditions exist in which it may be the preferred choice

Theoretical Fundamentals of Real-time Virtualization from the Resource Management Perspective

A Virtual Machine Monitor (VMM) partitions a host physical machine into a group of Virtual Machines (VMs). Typically, a VM machine only preempts a part of a dedicated physical resource temporally or spatially. This fact greatly impacts the real-time task scheduling in VMs because most traditional real-time scheduling theories are based on dedicated resources. The real-time community has introduced some Hierarchical Real-Time Scheduling Models to address this issue. Among them, the Regularity-based Resource (RRP) Model is able to provide maximal transparency for task scheduling. However, current theoretical results on the RRP Model are still far from the complete theoretical fundamentals required by a real-time VMM. At the resource level, only a naive algorithm has been found for resource partitioning. At the task level, only the Periodic Task Model is investigated, and even for this task model, only one simple case has been considered. This work explores the RRP Model at both the resource and task levels. On the one hand, it is the first to solve the resource partitioning problem with both global and partitioned strategies. On the other hand, it solves the task scheduling problem with a strong result that the classic task scheduling problem in the RRP Model can be easily transformed into an equivalent problem on a dedicated resource. With these theory enhancements, a 2-layer real-time resource model is presented and the theoretical fundamentals of a real-time VMM are fully established from resource management perspective.Computer Science, Department o

Mixed Pfair/ERfair scheduling of asynchronous periodic tasks

PD2 uses a simpler tie-breaking scheme than PD to disambiguate equal deadlines. We present a series of counterexamples that suggest that, in general, the PD2 tie-breaking mechanism cannot be simplified. In contrast to this, we show that no tie-breaking information is needed on two-processor systems.Pfair scheduling was proposed by Baruah, Cohen, Plaxton, and Varvel as a non-work-conserving way of optimally and efficiently scheduling periodic tasks on a multiprocessor. In this paper, we introduce a work-conserving variant of Pfair scheduling called “early-release” fair (ERfair) scheduling. We also present a new scheduling algorithm called PD2 and show that it is optimal for scheduling any mix of early-release and non-early-release asynchronous, periodic tasks. In contrast, almost all prior work on Pfair scheduling has been limited to synchronous systems. PD2 is an optimization of an earlier deadline-based algorithm of Baruah, Gehrke, and Plaxton called P

Optimal virtual cluster-based multiprocessor scheduling

Scheduling of constrained deadline sporadic task systems on multiprocessor platforms is an area which has received much attention in the recent past. It is widely believed that finding an optimal scheduler is hard, and therefore most studies have focused on developing algorithms with good processor utilization bounds. These algorithms can be broadly classified into two categories: partitioned scheduling in which tasks are statically assigned to individual processors, and global scheduling in which each task is allowed to execute on any processor in the platform. In this paper we consider a third, more general, approach called cluster-based scheduling. In this approach each task is statically assigned to a processor cluster, tasks in each cluster are globally scheduled among themselves, and clusters in turn are scheduled on the multiprocessor platform. We develop techniques to support such cluster-based scheduling algorithms, and also consider properties that minimize total processor utilization of individual clusters. In the last part of this paper, we develop new virtual cluster-based scheduling algorithms. For implicit deadline sporadic task systems, we develop an optimal scheduling algorithm that is neither Pfair nor ERfair. We also show that the processor utilization bound of us-edf{m/(2m−1)} can be improved by using virtual clustering. Since neither partitioned nor global strategies dominate over the other, cluster-based scheduling is a natural direction for research towards achieving improved processor utilization bounds

The Generalized Multiprocessor Periodic Resource Interface Model for Hierarchical Multiprocessor Scheduling

Composition is a practice of key importance in software engineering. When real-time applications are composed it is necessary that their timing properties (such as meeting the deadlines) are guaranteed. The composition is performed by establishing an interface between the application and the physical platform. Such an interface does typically contain information about the amount of computing capacity needed by the application. In multiprocessor platforms, the interface should also present information about the degree of parallelism. Recently there have been quite a few interface proposals. However, they are either too complex to be handled or too pessimistic. In this paper we propose the Generalized Multiprocessor Periodic Resource model (GMPR) that is strictly superior to the MPR model without requiring a too detailed description. We describe a method to generate the interface from the application specification. All these methods have been implemented in Matlab routines that are publicly available

Reinforcement learning based multi core scheduling (RLBMCS) for real time systems

Embedded systems with multi core processors are increasingly popular because of the diversity of applications that can be run on it. In this work, a reinforcement learning based scheduling method is proposed to handle the real time tasks in multi core systems with effective CPU usage and lower response time. The priority of the tasks is varied dynamically to ensure fairness with reinforcement learning based priority assignment and Multi Core MultiLevel Feedback queue (MCMLFQ) to manage the task execution in multi core system