Showing papers on "Deadline-monotonic scheduling published in 2018"

A Collaborative Multiobjective Fruit Fly Optimization Algorithm for the Resource Constrained Unrelated Parallel Machine Green Scheduling Problem

Abstract: Due to the development of the green economy, green manufacturing has been a hot topic. This paper proposes a new problem, i.e., the resource constrained unrelated parallel machine green manufacturing scheduling problem (RCUPMGSP) with the criteria of minimizing the makespan and the total carbon emission. To solve the problem, a collaborative multiobjective fruit fly optimization algorithm (CMFOA) is proposed. First, a job-speed pair-based solution representation is presented, and an effective decoding method is designed. Second, a heuristic for initialization of the population is proposed. Third, three collaborative search operators are designed to handle three subproblems in the smell-based search phase, i.e., job-to-machine assignment, job sequence, and processing speed selection. The technique for order preference by similarity to an ideal solution and the fast nondominated sorting methods are both employed for multiobjective evaluation in the vision-based search phase. Moreover, a critical-path-based carbon saving technique is designed according to the problem analysis to further improve the nondominated solutions explored in the fruit fly optimization algorithm-based evolution. In addition, the effect of parameter setting is investigated and the suitable parameter values are recommended. Finally, numerical tests and comparisons are carried out using the randomly generated instances, which show that the CMFOA is able to obtain more and better nondominated solutions than other algorithms. The comparisons also demonstrate the effectiveness of the collaborative scheme and the carbon saving technique as well as the CMFOA in solving the RCUPMGSP.

Single-machine scheduling and common due date assignment with potential machine disruption

Abstract: This paper studies a single-machine due date assignment and scheduling problem in a disruptive environment, where a machine disruption may occur at a particular time that will last for a period of time with a certain probability, and the job due dates are determined by the decision-maker using the popular common due date assignment method. The goal is to determine jointly the optimal job sequence and the common due date so as to minimise the expected value of an integrated cost function that includes the earliness, tardiness and due date assignment costs. We analyse the computational complexity status of various cases of the problem, and develop pseudo-polynomial-time solution algorithms, randomised adaptive search algorithms, and fully polynomial-time approximation schemes for them, if viable. Finally, we conduct extensive numerical testing to assess the performance of the proposed algorithms.

Multi-mode Resource-Constrained Project Scheduling

Abstract: The first part of this chapter presents Multi-mode Resource-Constrained Project Scheduling Problem (MRCPSP) formulations and notations (Sect. 5.1).

An exact schedulability test for fixed-priority preemptive mixed-criticality real-time systems

Abstract: The current literature of fixed-priority scheduling algorithms relies on sufficient tests to determine if a set of mixed-criticality sporadic tasks is schedulable on a single processor. The drawback of these safe tests is their pessimism, a matter that could be solved if an exact schedulability analysis is used. However, because of the non-deterministic behavior of tasks in the mentioned setups, exact quantification of worst-case response times, needed for the test, is a difficult problem; more precisely, such a quantification needs evaluation of enormous sequences of job executions. The core problem is thus to merge such sequences to make the analysis practical. This paper, for the first time, gives an algorithm for exact worst-case response time characterization of mixed-criticality sporadic real-time tasks executing according to a given fixed-priority scheduler. We use a set of techniques which carefully consider the task properties and their relation to the worst scenarios to prune the analysis state space. We also show an interesting result that if an exact schedulability test is used, the Audsley's optimal priority assignment algorithm is not applicable to the mixed-criticality case. Accordingly, we need new priority assignment algorithms to work with the exact test; we give a simple task priority assignment algorithm to this aim. The performance of the proposed exact test (in terms of time complexity) is examined and the effectiveness of some heuristic priority assignment algorithms using the test (in terms of the ratio of task sets which are deemed schedulable) are compared.

Optimal Power Control and Scheduling for Real-Time and Non-Real-Time Data

Abstract: We consider a joint scheduling-and-power-allocation problem of a downlink cellular system. The system consists of two groups of users: real-time (RT) and non-real-time (NRT) users. Given an average power constraint on the base station, the problem is to find an algorithm that satisfies the RT hard deadline constraint and NRT queue stability constraint. We propose two sum-rate-maximizing algorithms that satisfy these constraints as well as achieving the system's capacity region. In both algorithms, the power allocation policy has a closed-form expression for the two groups of users. However, interestingly, the power policy of the RT users, which we call the Lambert-power policy, differs in structure from the water-filling policy for the NRT users. The first algorithm is optimal for the on–off channel model with a polynomial-time scheduling complexity in the number of RT users. The second, on the other hand, works for any channel fading model, which is shown through simulations to have an average complexity that is close to linear. We also show the superiority of the proposed algorithms over existing approaches using extensive simulations.

Single machine scheduling to minimise resource consumption cost with a bound on scheduling plus due date assignment penalties

Abstract: We study a single-machine scheduling problem in a flexible framework, where both job processing times and due dates are decision variables to be determined by the scheduler. We consider the case wh...

Exact Speedup Factors and Sub-Optimality for Non-Preemptive Scheduling

Abstract: Fixed priority scheduling is used in many real-time systems; however, both preemptive and non-preemptive variants (FP-P and FP-NP) are known to be sub-optimal when compared to an optimal uniprocessor scheduling algorithm such as preemptive earliest deadline first (EDF-P). In this paper, we investigate the sub-optimality of fixed priority non-preemptive scheduling. Specifically, we derive the exact processor speed-up factor required to guarantee the feasibility under FP-NP (i.e. schedulability assuming an optimal priority assignment) of any task set that is feasible under EDF-P. As a consequence of this work, we also derive a lower bound on the sub-optimality of non-preemptive EDF (EDF-NP). As this lower bound matches a recently published upper bound for the same quantity, it closes the exact sub-optimality for EDF-NP. It is known that neither preemptive, nor non-preemptive fixed priority scheduling dominates the other, in other words, there are task sets that are feasible on a processor of unit speed under FP-P that are not feasible under FP-NP and vice-versa. Hence comparing these two algorithms, there are non-trivial speedup factors in both directions. We derive the exact speed-up factor required to guarantee the FP-NP feasibility of any FP-P feasible task set. Further, we derive the exact speed-up factor required to guarantee FP-P feasibility of any constrained-deadline FP-NP feasible task set.

Multi-attribute assignment of trains to departures in rolling stock management: A contribution to the EURO/ROADEF 2014 Challenge

Abstract: The article describes our findings on the EURO/ROADEF 2014 Challenge problem. Several heuristic solution techniques have been implemented in a prototypical system for rolling stock management. First, the assignment of trains to departures is supported by a multi-attribute priority rule, for which extensive experiments have been conducted. The subsequent scheduling problem is then solved by a heuristic routing and scheduling concept. The feasibility of solutions is ensured by adopting a transaction model known from database programming to the scheduling problem domain. Besides our contributions to the solution of the optimization problem, a decision support system has been build that visualizes the movements of convoys in the network. Moreover, we make the source code of our optimization approach available with this article: doi: 10.17632/nc642wfw2k.1 .

Adapting MCP and HLFET Algorithms to Multiple Simultaneous Scheduling

Abstract: We deal with the following scheduling problem: an infinite number of tasks must be scheduled for processing on a finite number of heterogeneous machines, such as all tasks are sent to execution with a minimum delay. The tasks have causal dependencies and are generated in the context of biomedical applications, and produce results relevant for the medical domain, such as diagnosis support or drug dose adjust measures. The proposed scheduling model had a starting point in two known bounded number of processors algorithms: Modified Critical Path and Highest Level First With Estimated Times. Several steps were added to the original implementation along with a merge stage in order to combine the results obtained for each of the previously scheduled tasks. Regarding the implementation, a simulator was used to analyze and design the scheduling algorithms.

Evolutionary Computing Approach to Optimize Superframe Scheduling on Industrial Wireless Sensor Networks

Abstract: There has been a paradigm shift in the industrial wireless sensor domain caused by the Internet of Things (IoT). IoT is a thriving technology leading the way in short range and fixed wireless sensing. One of the issues in Industrial Wireless Sensor Network-IWSN is finding the optimal solution for minimizing the defect time in superframe scheduling. This paper proposes a method using the evolutionary algorithms approach namely particle swarm optimization (PSO), Orthogonal Learning PSO, genetic algorithms (GA) and modified GA for optimizing the scheduling of superframe. We have also evaluated a contemporary method, deadline monotonic scheduling on the ISA 100.11a. By using this standard as a case study, the presented simulations are object-oriented based, with numerous variations in the number of timeslots and wireless sensor nodes. The simulation results show that the use of GA and modified GA can provide better performance for idle and missed deadlines. A comprehensive and detailed performance evaluation is given in the paper.

Optimal priority and threshold assignment for fixed-priority preemption threshold scheduling

Abstract: Fixed-priority preemption-threshold scheduling (FPTS) is a generalization of fixed-priority preemptive scheduling (FPPS) and fixed-priority non-preemptive scheduling (FPNS). Since FPPS and FPNS are incomparable in terms of potential schedulability, FPTS has the advantage that it can schedule any task set schedulable by FPPS or FPNS and some that are not schedulable by either. FPTS is based on the idea that each task is assigned a priority and a preemption threshold. While tasks are admitted into the system according to their priorities, they can only be preempted by tasks that have priority higher than the preemption threshold.This paper presents a new optimal priority and preemption threshold assignment (OPTA) algorithm for FPTS which in general outperforms the existing algorithms in terms of the size of the explored state-space and the total number of worst case response time calculations performed. The algorithm is based on back-tracking, i.e. it traverses the space of potential priorities and preemption thresholds, while pruning infeasible paths, and returns the first assignment deemed schedulable.We present the evaluation results where we compare the complexity of the new algorithm with the existing one. We show that the new algorithm significantly reduces the time needed to find a solution. Through a comparative evaluation, we show the improvements that can be achieved in terms of schedulability ratio by our OPTA compared to a deadline monotonic priority assignment.

Resource-Constrained Project Scheduling

Abstract: The first part of this chapter presents Resource-Constrained Project Scheduling Problem (RCPSP) formulations and notations (Sect. 4.1). It also provides an overview of the best methods proposed so far for solving this problem, including a set of relevant bibliographic references in Sect. 4.2.