Showing papers on "Deadline-monotonic scheduling published in 2011"

Multi-core Real-Time Scheduling for Generalized Parallel Task Models

Abstract: Multi-core processors offer a significant performance increase over single core processors. Therefore, they have the potential to enable computation-intensive real-time applications with stringent timing constraints that cannot be met on traditional single-core processors. However, most results in traditional multiprocessor real-time scheduling are limited to sequential programming models and ignore intra-task parallelism. In this paper, we address the problem of scheduling periodic parallel tasks with implicit deadlines on multi-core processors. We first consider a synchronous task model where each task consists of segments, each segment having an arbitrary number of parallel threads that synchronize at the end of the segment. We propose a new task decomposition method that decomposes each parallel task into a set of sequential tasks. We prove that our task decomposition achieves a resource augmentation bound of 2.62 and 3.42 when the decomposed tasks are scheduled using global EDF and partitioned deadline monotonic scheduling, respectively. Finally, we extend our analysis to directed a cyclic graph tasks. We show how these tasks can be converted into synchronous tasks such that the same transformation can be applied and the same augmentation bounds hold.

Improved priority assignment for global fixed priority pre-emptive scheduling in multiprocessor real-time systems

Abstract: This paper is an extended version of a paper that appeared in the proceedings of the IEEE Real-Time Systems Symposium 2009 This paper has been updated with respect to advances made in schedulability analysis, and contains a number of significant additional results The paper addresses the problem of priority assignment in multiprocessor real-time systems using global fixed task-priority pre-emptive scheduling We prove that Audsley's Optimal Priority Assignment (OPA) algorithm, originally devised for uniprocessor scheduling, is applicable to the multiprocessor case, provided that three conditions hold with respect to the schedulability tests used Our empirical investigations show that the combination of optimal priority assignment policy and a simple compatible schedulability test is highly effective in terms of the number of tasksets deemed to be schedulable We also examine the performance of heuristic priority assignment policies such as Deadline Monotonic, and an extension of the TkC priority assignment policy called DkC that can be used with any schedulability test Here we find that Deadline Monotonic priority assignment has relatively poor performance in the multiprocessor case, while DkC priority assignment is highly effective

Temperature-Aware Scheduling and Assignment for Hard Real-Time Applications on MPSoCs

Abstract: Increasing integrated circuit (IC) power densities and temperatures may hamper multiprocessor system-on-chip (MPSoC) use in hard real-time systems. This paper formalizes the temperature-aware real-time MPSoC assignment and scheduling problem and presents an optimal phased steady-state mixed integer linear programming-based solution that considers the impact of scheduling and assignment decisions on MPSoC thermal profiles to directly minimize the chip peak temperature. We also introduce a flexible heuristic framework for task assignment and scheduling that permits system designers to trade off accuracy for running time when solving large problem instances. Finally, for task sets with sufficient slack, we show that inserting idle times between task executions can further reduce the peak temperature of the MPSoC quite significantly.

Location‐Based Management for Construction: Planning, Scheduling and Control

Abstract: Russell Kenley and Olli Seppanen, Spon Press, London, 2009, 336 pp., ISBN 978 0 415 37050 9, £90 I found this book to be a very well‐researched and written summary of prevalent construction industr...

End-to-End Delay Analysis for Fixed Priority Scheduling in WirelessHART Networks

Abstract: The WirelessHART standard has been specifically designed for real-time communication between sensor and actuator devices for industrial process monitoring and control. End-to-end communication delay analysis for WirelessHART networks is required for acceptance test of real-time data flows from sensors to actuators and for workload adjustment in response to network dynamics. In this paper, we map the scheduling of real-time periodic data flows in a WirelessHART network to real-time multiprocessor scheduling. We, then, exploit the response time analysis for multiprocessor scheduling and propose a novel method for the end-to-end delay analysis of the real-time flows that are scheduled using a fixed priority scheduling policy in a WirelessHART network. Simulations based on both random topologies and real network topologies of a physical testbed demonstrate the efficacy of our end-to-end delay analysis in terms of acceptance ratio under various fixed priority scheduling policies.

On Partitioned Scheduling of Fixed-Priority Mixed-Criticality Task Sets

Abstract: Mixed-criticality real-time systems, where tasks may be associated with different criticality and assurance levels, have attracted much attention in the recent past. In this paper, we consider partitioning-based multiprocessor scheduling of mixed-criticality real-time task sets. Guaranteeing feasibility in this setting is shown to be NP-Hard. With a focus on fixed-priority preemptive scheduling on each processor, we identify the two main aspects of the problem, namely the task allocation and priority assignment dimensions. For the task allocation dimension, we propose and compare bin-packing-inspired heuristics, based on offline task ordering according to utilization and criticality. For the priority assignment dimension, we compare the well-known Rate Monotonic priority assignment policy with Audsley's priority assignment algorithm. Through simulations, we also assess and discuss the relative importance of these two primary dimensions on the overall mixed-criticality feasibility problem for multiprocessor platforms.

Priority Assignment for Real-Time Flows in WirelessHART Networks

Abstract: WirelessHART is a new wireless sensor-actuator network standard specifically developed for process industries. A key challenge faced by WirelessHART networks is to meet the stringent real-time communication requirements imposed by process monitoring and control applications. Fixed-priority scheduling, a popular scheduling policy for real-time networks, has recently been shown to be an effective real-time transmission scheduling policy in WirelessHART networks. Priority assignment has a major impact on the schedulability of real-time flows in these networks. This paper investigates the open problem of priority assignment for periodic real-time flows in a WirelessHART network. We first propose an optimal priority assignment algorithm based on local search for any given worst case delay analysis. We then propose an efficient heuristic search algorithm for priority assignment. We also identify special cases where the heuristic search is optimal. Simulations based on random networks and the real topology of a physical sensor network test bed showed that the heuristic search algorithm achieved near optimal performance in terms of schedulability, while significantly outperforming traditional priority assignment policies for real-time systems.

A GRASP-based approach for technicians and interventions scheduling for telecommunications

Abstract: The Technicians and Interventions Scheduling Problem for Telecommunications embeds the scheduling of interventions, the assignment of teams to interventions and the assignment of technicians to teams. Every intervention is characterized, among other attributes, by a priority. The objective of this problem is to schedule interventions such that the interventions with the highest priority are scheduled at the earliest time possible while satisfying a set of constraints like the precedence between some interventions and the minimum number of technicians needed with the required skill levels for the intervention. We present a Greedy Randomized Adaptive Search Procedure (GRASP) for solving this problem. In the proposed implementation, we integrate learning to the GRASP framework in order to generate good-quality solutions using information brought by previous ones. We also compute lower bounds and present experimental results that validate the effectiveness of this approach.

A dynamic priority scheduling algorithm on service request scheduling in cloud computing

Abstract: With the popularity of cloud computing, more and more business are operated in the cloud computing. In this paper, we address the problem of service request scheduling in cloud computing systems. We consider the three-tier cloud structure, which consists of resource providers, service providers and consumers. The service request scheduling strategies in this scenario should satisfy the objectives of the service providers and consumers. We propose a new strategy dynamic priority scheduling algorithm (DPSA) to solve this problem. The algorithm is more efficient and optimal than the FCFS and SPSA.

Minimizing buffer requirements for throughput constrained parallel execution of synchronous dataflow graph

Abstract: This paper concerns throughput-constrained parallel execution of synchronous data flow graphs. This paper assumes static mapping and dynamic scheduling of nodes, which has several benefits over static scheduling approaches. We determine the buffer size of all arcs to minimize the total buffer size while satisfying a throughput constraint. Dynamic scheduling is able to achieve the similar throughput performance as the static scheduling does by unfolding the given SDF graph. A key issue of dynamic scheduling is how to assign the priority to each node invocation, which is also discussed in this paper. Since the problem is NP-hard, we present a heuristic based on a genetic algorithm. The experimental results confirm the viability of the proposed technique.

Improving Feasibility of Fixed Priority Tasks Using Non-Preemptive Regions

Abstract: Preemptive schedulers have been widely adopted in single processor real-time systems to avoid the blocking associated with the non-preemptive execution of lower priority tasks and achieve a high processor utilization. However, under fixed priority assignments, there are cases in which limiting preemptions can improve schedulability with respect to a fully preemptive solution. This is true even neglecting preemption overhead, as it will be shown in the paper. In previous works, limited-preemption schedulers have been mainly considered to reduce the preemption overhead, and make the estimation of worst-case execution times more predictable. In this work, we instead show how to improve the feasibility of fixed-priority task systems by executing the last portion of each task in a non-preemptive fashion. A proper dimensioning of such a region of code allows increasing the number of task sets that are schedulable with a fixed priority algorithm. Simulation experiments are also presented to validate the effectiveness of the proposed approach.

An Optimal Model for Priority based Service Scheduling Policy for Cloud Computing Environment

Abstract: computing refers to the model, which is the pool of resources. Cloud makes on-demand delivery of these computational resources (data, software and infrastructure) among multiple services via a computer network with different load conditions of the cloud network. User will be charged for the resources used based upon time. Hence efficient utilization of cloud resources has become a major challenge in satisfying the user's requirement (QoS) and in gaining benefit for both the user and the service provider. In this paper, we propose a priority and admission control based service scheduling policy that aims at serving the user requests satisfying the QoS, optimizing the time the service-request spends in the queue and achieving the high throughput of the cloud by making an efficient provision of cloud resources. Keywords-category, service scheduling policy, Priority, admission control and deadline.

Project Scheduling Heuristics-Based Standard PSO for Task-Resource Assignment in Heterogeneous Grid

Abstract: The task scheduling problem has been widely studied for assigning resources to tasks in heterogeneous grid environment. Effective task scheduling is an important issue for the performance of grid computing. Meanwhile, the task scheduling problem is an NP-complete problem. Hence, this investigation introduces a named “standard“ particle swarm optimization (PSO) metaheuristic approach to efficiently solve the task scheduling problems in grid. Meanwhile, two promising heuristics based on multimode project scheduling are proposed to help in solving interesting scheduling problems. They are the best performance resource heuristic and the latest finish time heuristic. These two heuristics applied to the PSO scheme are for speeding up the search of the particle and improving the capability of finding a sound schedule. Moreover, both global communication topology and local ring communication topology are also investigated for efficient study of proposed scheme. Simulation results demonstrate that the proposed approach in this investigation can successfully solve the task-resource assignment problems in grid computing and similar scheduling problems.

Schedulability Analysis and Optimization of Heterogeneous EDF and FP Distributed Real-Time Systems

Abstract: The increasing acceptance of the Earliest Deadline First (EDF) scheduling algorithm in industrial environments, together with the continued usage of Fixed Priority (FP) scheduling is leading to heterogeneous systems with different scheduling policies in the same distributed system. Schedulability analysis techniques usually consider the entire system as a whole (holistic approach), with only one preestablished scheduling policy in all the resources. In this work, composition mechanisms will be presented that enable us to combine different FP and EDF response-time analysis techniques for checking the schedulability of heterogeneous systems. Additionally, priority and scheduling deadline assignment techniques will be combined into a new algorithm called HOSPA (Heuristic Optimized Scheduling Parameters Assignment), for optimizing the assignment of priorities and scheduling deadlines to tasks and messages in heterogeneous distributed hard real-time systems.

Dynamic scheduling of a batch of parallel task jobs on heterogeneous clusters

Abstract: This paper addresses the problem of minimizing the scheduling length (make-span) of a batch of jobs with different arrival times. A job is described by a direct acyclic graph (DAG) of parallel tasks. The paper proposes a dynamic scheduling method that adapts the schedule when new jobs are submitted and that may change the processors assigned to a job during its execution. The scheduling method is divided into a scheduling strategy and a scheduling algorithm. We also propose an adaptation of the Heterogeneous Earliest-Finish-Time (HEFT) algorithm, called here P-HEFT, to handle parallel tasks in heterogeneous clusters with good efficiency without compromising the makespan. The results of a comparison of this algorithm with another DAG scheduler using a simulation of several machine configurations and job types shows that P-HEFT gives a shorter makespan for a single DAG but scores worse for multiple DAGs. Finally, the results of the dynamic scheduling of a batch of jobs using the proposed scheduler method showed significant improvements for more heavily loaded machines when compared to the alternative resource reservation approach.

Ant colony optimization technique for the sequence-dependent flowshop scheduling problem

Abstract: In the real world, production scheduling systems, usually optimal job scheduling, requires an explicit consideration of sequence-dependent setup times. One of the most important scheduling criteria in practical systems is makespan. In this paper, the author presents an ant colony optimization (ACO) algorithm for the sequence-dependent permutation flowshop scheduling problem. The proposed ACO algorithm benefits from a new approach for computing the initial pheromone values and a local search. The proposed algorithm is tested on randomly generated problem instances and results indicate that it is very competitive with the existing best metaheuristics.

Feasibility analysis under fixed priority scheduling with limited preemptions

Abstract: Preemptive scheduling often generates a significant runtime overhead that may increase task worst-case execution times up to 40%, with respect to a fully non-preemptive execution. In small embedded systems, such an extra cost results in longer and more variable response times that can significantly affect the overall energy consumption, as well as the system predictability. Limiting preemptions is often possible without jeopardizing schedulability. Although several authors addressed schedulability analysis under different forms of limited preemptive scheduling, current results exhibit two major deficiencies: (i) The maximum lengths of the non-preemptive regions for each task are still unknown under fixed priorities; (i) The exact response time analysis for tasks with fixed preemption points is too complex. This paper presents the schedulability analysis of real-time tasks with non-preemptive regions, under fixed priority assignments. In particular, two different preemption models are considered: the floating and the fixed preemption point model. Under each model, the feasibility analysis is addressed by deriving simple and effective schedulability tests, as well as an algorithm for computing the maximum length of the non-preemptive regions for each task. Finally, simulation experiments are presented to compare the two models in terms of schedulability.

Reliability-Driven Energy-Efficient Task Scheduling for Multiprocessor Real-Time Systems

Abstract: This paper proposes a reliability-driven task scheduling scheme for multiprocessor real-time embedded systems that optimizes system energy consumption under stochastic fault occurrences. The task scheduling problem is formulated as an integer linear program where a novel fault adaptation variable is introduced to model the uncertainties of fault occurrences. The proposed scheme, which considers both the dynamic power and the leakage power, is able to handle the scheduling of independent tasks and tasks with precedence constraints, and is capable of scheduling tasks with varying deadlines. Experimental results have demonstrated that the proposed reliability-driven parallel scheduling scheme achieves energy savings of more than 15% when compared to the approach of designing for the corner case of fault occurrences.

Learning IF-THEN priority rules for dynamic job shops using genetic algorithms

Abstract: Performing complex, informed priority rules might pose a challenge for traditional operator-driven systems. However, computer-integrated manufacturing systems may significantly benefit from the complex, informed rules such as state-dependent priority rules. A state-dependent priority rule can be defined as a list of IF-THEN priority rules that will be performed if certain system conditions are satisfied. Here, we propose a genetic algorithm based learning system for constructing interval-based, state-dependent priority rules for each interval of queue lengths in dynamic job shops. Our approach builds interval based state-dependent priority rules pairing the priority rules with the intervals of queue lengths, and determines priority rules and their corresponding length of intervals for a given objective. A genetic algorithm is developed for matching queue length intervals with appropriate priority rules during simulation. A system simulation evaluates the efficiencies of interval based state dependent priority rules. The experiments show that interval-based state dependent priority rules obtained by the proposed approach considerably outperform the priority rules including shortest processing time (SPT), minimum slack time (MST), earlier due date (EDD), modified due date (MDD), cost over time (COVERT), and critical ratio (CR) for total tardiness for most of the problems.

On the Analysis of Packet Scheduling in Downlink 3GPP LTE System

Abstract: This paper investigates the performance of packet scheduling in downlink LTE (Long Term Evolution) systems using Round Robin strategy in time domain and time and frequency domain. Two types of non-real time services are considered in the analysis performed, with and without priority set, as well as the limitation given by the physical downlink control channels (PDCCH) on the number of simultaneously scheduled users. Cell throughput, achievable user throughput and system capacity are evaluated in different scenarios with two mixed services, two packet scheduling approaches and priority impact on the generated traffic.

A SOMO-based approach to the operating room scheduling problem

Abstract: In most hospitals, operating rooms are the most costly facilities and consume a large percentage of the hospital recourses. Therefore, an efficient and effective operating room scheduling system is highly demanded for hospitals. In this paper, a SOMO-based approach to solving the operating room scheduling problem is proposed. Computational experiments performed on 100 randomly generated simulations are conducted to test whether the proposed scheduling algorithm can provide appealing arrangements in a reasonable computation time.