EDZL scheduling analysis

A schedulability test is derived for the global Earliest Deadline Zero Laxity (EDZL) scheduling algorithm on a platform with multiple identical processors. The test is sufficient, but not necessary, to guarantee that a system of independent sporadic tasks with arbitrary deadlines will be successfully scheduled, with no missed deadlines, by the multiprocessor EDZL algorithm. Global EDZL is known to be at least as effective as global Earliest-Deadline-First (EDF) in scheduling task sets to meet deadlines. It is shown, by testing on large numbers of pseudo-randomly generated task sets, that the combination of EDZL and the new schedulability test is able to guarantee that far more task sets meet deadlines than the combination of EDF and known EDF schedulability tests. In the second part of the paper, an improved version of the EDZL-schedulability test is presented. This new algorithm is able to efficiently exploit information on the slack values of interfering tasks, to iteratively refine the estimation of the interference a task can be subjected to. This iterative algorithm is shown to have better performance than the initial test, in terms of schedulable task sets detected.

An analysis of global edf schedulability for arbitrary-deadline sporadic task systems

Recent results on the global multiprocessor edf scheduling of sporadic task systems are, for the most part, applicable only to task systems in which each task’s relative deadline parameter is constrained to be no larger than its minimum inter-arrival separation. This paper introduces new analysis techniques that allow for similar results to be derived for task systems in which individual tasks are not constrained in this manner. For tasks with deadlines greater than their minimum inter-arrival separation, two models are considered, with and without an implicit intra-task job precedence constraint. The new analyses yield schedulability conditions that strictly dominate some previously proposed tests that are generally accepted to represent the current state of the art in multiprocessor edf schedulability analysis, and permits the derivation of an improved speed-up bound.

The feasibility of general task systems with precedence constraints on multiprocessor platforms

Feasibility analysis determines (prior to system execution-time) whether a specified collection of hard-real-time jobs executed on a processing platform can meet all deadlines. In this paper, we derive near-optimal sufficient tests for determining whether a given collection of jobs can feasibly meet all deadlines upon a specified multiprocessor platform assuming job migration is permitted. The collection of jobs may contain precedence constraints upon the order of execution of these jobs. The derived tests are general enough to be applied even when the collection of jobs is incompletely specified. We discuss the applicability of these tests to the scheduling of collections of jobs that are generated by systems of recurrent real-time tasks. We also show that our feasibility conditions may be used to obtain global-EDF schedulability conditions.

Real-time scheduling for energy harvesting sensor nodes

Energy harvesting has recently emerged as a feasible option to increase the operating time of sensor networks. If each node of the network, however, is powered by a fluctuating energy source, common power management solutions have to be reconceived. This holds in particular if real-time responsiveness of a given application has to be guaranteed. Task scheduling at the single nodes should account for the properties of the energy source, capacity of the energy storage as well as deadlines of the single tasks. We show that conventional scheduling algorithms (like e.g. EDF) are not suitable for this scenario. Based on this motivation, we have constructed optimal scheduling algorithms that jointly handle constraints from both energy and time domain. Further we present an admittance test that decides for arbitrary task sets, whether they can be scheduled without deadline violations. To this end, we introduce the concept of energy variability characterization curves (EVCC) which nicely captures the dynamics of various energy sources. Simulation results show that our algorithms allow significant reductions of the battery size compared to Earliest Deadline First scheduling.

Flexible hard real-time scheduling for deliberative AI systems

Constructing deliberative real-time AI systems is challenging due to the high execution-time variance in AI algorithms and the requirement of worst-case bounds for hard real-time guarantees, often resulting in poor use of system resources. Using a motivating case study, the general problem of resource usage maximization is addressed. We approach the issues by employing a hybrid task model for anytime algorithms, which is supported by recent advances in fixed priority scheduling for imprecise computation. In particular, with a novel scheduling scheme based on Dual Priority Scheduling, hard tasks are guaranteed by schedulability analysis and scheduled in favor of optional and anytime components which are executed whenever possible for enhancing system utility. Simulation studies show satisfactory performance on the case study with the application of the scheduling scheme. We also suggest how aperiodic tasks can be scheduled effectively within the framework and how tasks can be prioritized based on their utilities by an efficient algorithm. These works form a comprehensive package of scheduling model, analysis, and algorithms based on fixed priority scheduling, providing a versatile platform where real-time AI applications can be suitably facilitated.

The space of EDF deadlines: the exact region and a convex approximation

It is well known that the performance of computer controlled systems is heavily affected by delays and jitter occurring in the control loops, which are mainly caused by the interference introduced by other concurrent activities. A common approach adopted to reduce delay and jitter in periodic task systems is to decrease relative deadlines as much as possible, but without jeopardizing the schedulability of the task set. In this paper, we formally characterize the region of admissible deadlines so that the system designer can appropriately select the desired values to maximize a given performance index defined over the task set. Finally we also provide a sufficient region of feasible deadlines which is proved to be convex.

End-to-end deadline control for aperiodic tasks in distributed real-time systems

A category of Distributed Real-Time Systems (DRTS) that has multiprocessor pipeline architecture is increasingly used. The key challenge of such systems is to guarantee the end-to-end deadlines of aperiodic tasks. This paper proposes an end-to-end deadline control model, called Linear Quadratic Stochastic Optimal Control Model (LQ-SOCM), which features a distributed feedback control that dynamically enforces the desired performance. The control system considers the aperiodic task arrivals and execution times’ variation as the two external factors of the system unpredictability. LQ-SOCM uses discrete time state space equation to describe the real-time computing system. Then, in the actuator design, a continuous manner is adopted to deal with discrete QoS (Quality of Service) adaptation. Finally, experiments demonstrate that the system is globally stable and can statistically provide the end-to-end deadline guarantee for aperiodic tasks. At the same time, LQ-SOCM is capable of effectively improving the system throughput.

Delay composition in preemptive and non-preemptive real-time pipelines

Uniprocessor schedulability theory made great strides, in part, due to the simplicity of composing the delay of a job from the execution times of higher-priority jobs that preempt it. In this paper, we bound the end-to-end delay of a job in a multistage pipeline as a function of job execution times on different stages under preemptive as well as non-preemptive scheduling. We show that the end-to-end delay is bounded by that of a single virtual “bottleneck” stage plus a small additive component. This contribution effectively transforms the pipeline into a single stage system. The wealth of schedulability analysis techniques derived for uniprocessors can then be applied to decide the schedulability of the pipeline. The transformation does not require imposing artificial per-stage deadlines, but rather models the pipeline as a whole and uses the end-to-end deadlines directly in the single-stage analysis. It also does not make assumptions on job arrival patterns or periodicity and thus can be applied to periodic and aperiodic tasks alike. We show through simulations that this approach outperforms previous pipeline schedulability tests except for very short pipelines or when deadlines are sufficiently large. The reason lies in the way we account for execution overlap among stages. We discuss how previous approaches account for overlap and point out interesting differences that lead to different performance advantages in different cases. Further, we also show that in certain cases non-preemptive scheduling can result in higher system utilization than preemptive scheduling in pipelined systems. We hope that the pipeline delay composition rule, derived in this paper, may be a step towards a general schedulability analysis foundation for large distributed systems.

Schedulability analysis of global edf

The multiprocessor edf scheduling of sporadic task systems is studied. A new sufficient schedulability test is presented and proved correct. It is shown that this test generalizes the previously-known exact uniprocessor edf-schedulability test, and that it offers non-trivial quantitative guarantees (including a resource augmentation bound) on multiprocessors.

Optimal Control of Two-Stage Discrete Event Systems with Real-Time Constraints

We consider discrete event systems (DES) involving tasks with real-time constraints and seek to control processing times so as to minimize a cost function subject to each task meeting its own constraint. When tasks are processed over a single stage, it has been shown that there are structural properties of the optimal sample path that lead to very efficient solutions of such problems. When tasks are processed over multiple stages and are subject to end-to-end real-time constraints, these properties no longer hold and no obvious extensions are known. We consider a two-stage problem with homogeneous cost functions over all tasks at each stage and derive several new optimality properties. These properties lead to the idea of introducing “virtual” deadlines at the first stage, thus partially decoupling the stages so that the known efficient solutions for single-stage problems can be used. We prove that the solution obtained by an iterative virtual deadline algorithm (VDA) converges to the global optimal solution of the two-stage problem and illustrate the efficiency of the VDA through numerical examples.

Fault-aware grid scheduling using performance prediction by workload modeling

Computational grids hold great promise in utilizing geographically separated heterogeneous resources to solve large-scale complex problems. However, they suffer from a number of major technical hurdles, including distributed resource management and effective job scheduling. The main focus of this work is devoted on online scheduling of real time applications in distributed environments such as grids. Specifically, we are interested in applications with several independent tasks, each task with a prespecified lifecycle called deadline. Here, our goal is to schedule applications within an optimum overall time considering the specified deadlines. To achieve this, the resource performance prediction based on workload modeling and with the help of queuing techniques is employed. Afterward, a mathematical neural model is used to schedule the subtasks of the application. The main contributions of this work is to incorporate the impatiency factor as well as resource fault in performance modeling of nondedicated distributed systems, and also presenting an efficient and fast parallel scheduling algorithm under time constraint and heterogeneous resources. The proposed model is appropriate for implementation on parallel machines and in O(1) time. The new model was implemented on GridSim toolkit and under various conditions and with different parameters to evaluate the performance of scheduling algorithm. Simulation outcomes have shown that approximately in 87.8% of cases, our model schedules the tasks in such a way that all constraints are satisfied.

Robust priority assignment for messages on Controller Area Network (CAN)

This paper addresses the problem of determining the most robust priority assignment for CAN messages that are subject to transmission errors due to Electromagnetic Interference. In the presence of errors on the bus, CAN messages have a non-zero probability of missing their deadlines. An appropriate choice of priority ordering can minimise the overall worst-case deadline failure probability resulting in a more robust system. This paper shows that “deadline minus jitter” monotonic priority assignment, commonly used for priority assignment in commercial CAN systems, does not always result in the most robust priority ordering. A Robust Priority Assignment algorithm is presented that computes the most robust priority ordering for CAN messages subject to bit errors on the bus. This algorithm is optimal in the sense that it can be used to (i) maximise the number of errors tolerated, (ii) maximise the delay tolerated by any message, or (iii) minimise the probability of any message failing to meet its deadline. This algorithm is efficient and appropriate for use in an engineering context.

Static priority scheduling of event-triggered real-time embedded systems

Real-time embedded systems are often specified as a collection of independent tasks, each generating a sequence of event-triggered code blocks. The goal of scheduling tasks in this domain is to find an execution order which satisfies all real-time constraints. Within the context of recurring real-time tasks, all previous work either allowed preemptions, or only considered dynamic scheduling, and generally had exponential complexity. However, for many embedded systems running on limited resources, preemptive scheduling may be very costly due to high context switching and memory overheads, and dynamic scheduling can be less desirable due to high CPU overhead. In this paper, we study static priority scheduling of recurring real-time tasks. We focus on and obtain schedule-theoretic results for the non-preemptive uniprocessor case. To achieve this, we derive a sufficient (albeit not necessary) condition for schedulability under static priority scheduling and show that this condition can be efficiently tested in practice. The latter technique is demonstrated with examples, where in each case, an optimal solution for a given problem specification is obtained within reasonable time, by first detecting good candidates using meta-heuristics, and then by testing them for schedulability.

Optimization scheduling of MPEG-4 FGS video coding stream under the feasible mandatory constraint

MPEG-4 video coding stream with Fine Granularity Scalability (FGS) can be flexibly dropped by very fine granularity so as to adapt to the available network bandwidth. The MPEG-4 FGS model is similar to the imprecise computation model originally proposed in the real-time scheduling field. In both models, it is required that all the mandatory tasks be completely scheduled before their deadlines even in the worst case, which is called the feasible mandatory constraint. The problem is how to maximize the number of the scheduled tasks based on the importance of tasks and to satisfy the feasible mandatory constraint. We adapt the existing unit-time tasks scheduling algorithm to address the problem by using a weighted assignment scheme that adds constant weights to mandatory tasks. Under the feasible mandatory constraint, we prove that the proposed algorithm maximizes the total weights of the scheduled tasks, and all mandatory tasks are guaranteed to be completely scheduled before their deadlines. The experimental results show the performance of the video quality for our scheduling algorithm by the measurements of Peak Signal to Noise Ratio (PSNR).

Rate monotonic schedulability tests using period-dependent conditions

Feasibility and schedulability problems have received considerable attention from the real-time systems research community in recent decades. Since the publication of the Liu and Layland bound, many researchers have tried to improve the schedulability bound of the RM scheduling. The LL bound does not make any assumption on the relationship between any of the task periods. In this paper we consider the relative period ratios in a system. By reducing the difference between the smallest and the second largest virtual period values in a system, we can show that the RM schedulability bound can be improved significantly. This research has also proposed a system design methodology to improve the schedulability of real time system with a fixed system load.

Adaptation and application of multi-objective evolutionary algorithms for rule reduction and parameter tuning of fuzzy rule-based systems

Recently, multi-objective evolutionary algorithms have been applied to improve the difficult tradeoff between interpretability and accuracy of fuzzy rule-based systems. It is known that both requirements are usually contradictory, however, these kinds of algorithms can obtain a set of solutions with different trade-offs. This contribution analyzes different application alternatives in order to attain the desired accuracy/interpr-etability balance by maintaining the improved accuracy that a tuning of membership functions could give but trying to obtain more compact models. In this way, we propose the use of multi-objective evolutionary algorithms as a tool to get almost one improved solution with respect to a classic single objective approach (a solution that could dominate the one obtained by such algorithm in terms of the system error and number of rules). To do that, this work presents and analyzes the application of six different multi-objective evolutionary algorithms to obtain simpler and still accurate linguistic fuzzy models by performing rule selection and a tuning of the membership functions. The results on two different scenarios show that the use of expert knowledge in the algorithm design process significantly improves the search ability of these algorithms and that they are able to improve both objectives together, obtaining more accurate and at the same time simpler models with respect to the single objective based approach.

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ONTRACK: Dynamically adapting music playback to support navigation

Listening to music on personal, digital devices whilst mobile is an enjoyable, everyday activity. We explore a scheme for exploiting this practice to immerse listeners in navigation cues. Our prototype, ONTRACK, continuously adapts audio, modifying the spatial balance and volume to lead listeners to their target destination. First we report on an initial lab-based evaluation that demonstrated the approach’s efficacy: users were able to complete tasks within a reasonable time and their subjective feedback was positive. Encouraged by these results we constructed a handheld prototype. Here, we discuss this implementation and the results of field-trials. These indicate that even with a low-fidelity realisation of the concept, users can quite effectively navigate complicated routes.

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Protecting business intelligence and customer privacy while outsourcing data mining tasks

Nowadays data mining plays an important role in decision making. Since many organizations do not possess the in-house expertise of data mining, it is beneficial to outsource data mining tasks to external service providers. However, most organizations hesitate to do so due to the concern of loss of business intelligence and customer privacy. In this paper, we present a Bloom filter based solution to enable organizations to outsource their tasks of mining association rules, at the same time, protect their business intelligence and customer privacy. Our approach can achieve high precision in data mining by trading-off the storage requirement. This research was supported by the USA National Science Foundation Grants CCR-0310974 and IIS-0546027.

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The 2007 IEEE CEC simulated car racing competition

This paper describes the simulated car racing competition that was arranged as part of the 2007 IEEE Congress on Evolutionary Computation. Both the game that was used as the domain for the competition, the controllers submitted as entries to the competition and its results are presented. With this paper, we hope to provide some insight into the efficacy of various computational intelligence methods on a well-defined game task, as well as an example of one way of running a competition. In the process, we provide a set of reference results for those who wish to use the simplerace game to benchmark their own algorithms. The paper is co-authored by the organizers and participants of the competition.

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Search and track coordination in multi-ship multi-radar systems using schedulability envelope

This paper addresses the search and track coordination problems of multiple shipboard radars. The proposed approach first exploits the physical characteristics of a single phased array radar to improve its effective capacity. Its effective capacity is abstracted by a closed-form equation called a schedulability envelope. Using the schedulability envelope for each radar, we deal with search and track coordination as a relative-load-balancing problem in a multi-resource environment. The simulation results show that the proposed approach significantly improves the overall capacity of a multi-ship multi-radar system.