692 research outputs found

    MORA: an Energy-Aware Slack Reclamation Scheme for Scheduling Sporadic Real-Time Tasks upon Multiprocessor Platforms

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    In this paper, we address the global and preemptive energy-aware scheduling problem of sporadic constrained-deadline tasks on DVFS-identical multiprocessor platforms. We propose an online slack reclamation scheme which profits from the discrepancy between the worst- and actual-case execution time of the tasks by slowing down the speed of the processors in order to save energy. Our algorithm called MORA takes into account the application-specific consumption profile of the tasks. We demonstrate that MORA does not jeopardize the system schedulability and we show by performing simulations that it can save up to 32% of energy (in average) compared to execution without using any energy-aware algorithm.Comment: 11 page

    Optimization Models and Approximate Algorithms for the Aerial Refueling Scheduling and Rescheduling Problems

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    The Aerial Refueling Scheduling Problem (ARSP) can be defined as determining the refueling completion times for fighter aircrafts (jobs) on multiple tankers (machines) to minimize the total weighted tardiness. ARSP can be modeled as a parallel machine scheduling with release times and due date-to-deadline window. ARSP assumes that the jobs have different release times, due dates, and due date-to-deadline windows between the refueling due date and a deadline to return without refueling. The Aerial Refueling Rescheduling Problem (ARRP), on the other hand, can be defined as updating the existing AR schedule after being disrupted by job related events including the arrival of new aircrafts, departure of an existing aircrafts, and changes in aircraft priorities. ARRP is formulated as a multiobjective optimization problem by minimizing the total weighted tardiness (schedule quality) and schedule instability. Both ARSP and ARRP are formulated as mixed integer programming models. The objective function in ARSP is a piecewise tardiness cost that takes into account due date-to-deadline windows and job priorities. Since ARSP is NP-hard, four approximate algorithms are proposed to obtain solutions in reasonable computational times, namely (1) apparent piecewise tardiness cost with release time rule (APTCR), (2) simulated annealing starting from random solution (SArandom ), (3) SA improving the initial solution constructed by APTCR (SAAPTCR), and (4) Metaheuristic for Randomized Priority Search (MetaRaPS). Additionally, five regeneration and partial repair algorithms (MetaRE, BestINSERT, SEPRE, LSHIFT, and SHUFFLE) were developed for ARRP to update instantly the current schedule at the disruption time. The proposed heuristic algorithms are tested in terms of solution quality and CPU time through computational experiments with randomly generated data to represent AR operations and disruptions. Effectiveness of the scheduling and rescheduling algorithms are compared to optimal solutions for problems with up to 12 jobs and to each other for larger problems with up to 60 jobs. The results show that, APTCR is more likely to outperform SArandom especially when the problem size increases, although it has significantly worse performance than SA in terms of deviation from optimal solution for small size problems. Moreover CPU time performance of APTCR is significantly better than SA in both cases. MetaRaPS is more likely to outperform SAAPTCR in terms of average error from optimal solutions for both small and large size problems. Results for small size problems show that MetaRaPS algorithm is more robust compared to SAAPTCR. However, CPU time performance of SA is significantly better than MetaRaPS in both cases. ARRP experiments were conducted with various values of objective weighting factor for extended analysis. In the job arrival case, MetaRE and BestINSERT have significantly performed better than SEPRE in terms of average relative error for small size problems. In the case of job priority disruption, there is no significant difference between MetaRE, BestINSERT, and SHUFFLE algorithms. MetaRE has significantly performed better than LSHIFT to repair job departure disruptions and significantly superior to the BestINSERT algorithm in terms of both relative error and computational time for large size problems

    Performance Evaluation of Scheduling Algorithms for Real Time Cloud Computing Systems

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    Cloud computing shares data and oers services transparently among its users. With the increase in number of users of cloud the tasks to be scheduled increases. The performance of cloud depends on the task scheduling algorithms used in the scheduling components or brokering components. Scheduling of tasks on cloud computing systems is one of the research problem, Where the matching of machines and completion time of the tasks are considered. Tasks matching of machines problem is that, assume number of active hosts are Y, number of VMs in each host are Z. Maximum number of possible Virtual Machines(VMs) to schedule a single task is (y*z). If we need to schedule X tasks, number of possibilities are (y *z)^x. So scheduling of tasks is NP Hard problem. NP Hard means this scheduling of tasks on VMs not having polynomial time complexity, but it may have algorithm for verifying solution. Fault-tolerance becomes an important key to establish dependability in cloud computing system. In task scheduling, if task not completed in it's deadline ,then it is one type of fault in scheduling of tasks. In this thesis this type of faults are taken and try to overcome it. In this thesis we present a non-preemptive scheduling algorithm, By inserting the ideal time for postponing the task by ensuring the other task will completes its execution with in the deadline. In simulation the proposed algorithm maximizes the prot of 25%, throughput of 25% and minimizes the penalty of 20% over EDF

    Practical solutions for a dock assignment problem with trailer transportation.

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    We study a distribution warehouse in which trailers need to be assigned to docks for loading or unloading. A parking lot is used as a buffer zone and transportation between the parking lot and the docks is performed by auxiliary resources called terminal tractors. Each incoming trailer has a known arrival time and each outgoing trailer a desired departure time. The primary objective is to produce a docking schedule such that the weighted sum of the number of late outgoing trailers and the tardiness of these trailers is minimized; the secondary objective is to minimize the weighted completion time of all trailers, both incoming and outgoing. The purpose of this paper is to produce high-quality solutions to large instances that are comparable to a real-life case. We implement several heuristic algorithms: truncated branch and bound, beam search and tabu search. Lagrangian relaxation is embedded in the algorithms for constructing an initial solution and for computing lower bounds. The different solution frameworks are compared via extensive computational experiments.Dock assignment; Multicriteria scheduling; Branch and bound; Beam search; Lagrangian relaxation; Tabu search;

    Performance Evaluation of Scheduling Algorithms for Real Time Cloud Computing Systems

    Get PDF
    Cloud computing shares data and oers services transparently among its users. With the increase in number of users of cloud the tasks to be scheduled increases. The performance of cloud depends on the task scheduling algorithms used in the scheduling components or brokering components. Scheduling of tasks on cloud computing systems is one of the research problem, Where the matching of machines and completion time of the tasks are considered. Tasks matching of machines problem is that, assume number of active hosts are Y, number of VMs in each host are Z. Maximum number of possible Virtual Machines(VMs) to schedule a single task is (y*z). If we need to schedule X tasks, number of possibilities are (y *z)^x. So scheduling of tasks is NP Hard problem. NP Hard means this scheduling of tasks on VMs not having polynomial time complexity, but it may have algorithm for verifying solution. Fault-tolerance becomes an important key to establish dependability in cloud computing system. In task scheduling, if task not completed in it's deadline ,then it is one type of fault in scheduling of tasks. In this thesis this type of faults are taken and try to overcome it. In this thesis we present a non-preemptive scheduling algorithm, By inserting the ideal time for postponing the task by ensuring the other task will completes its execution with in the deadline. In simulation the proposed algorithm maximizes the prot of 25%, throughput of 25% and minimizes the penalty of 20% over EDF
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