Scheduling Jobs with Multiple Feasible Intervals

Shih, Chih-yu; Liu, Jane W. S.; Cheong, Infan Kuok

doi:10.1007/978-3-540-24686-2_4

Cited by 10 publications

(6 citation statements)

References 8 publications

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“…Simons and Sipser [21] considered unit-length, nonpreemptive jobs that have multiple feasible intervals, and showed that the general problem is NP-complete. More recently, Shih et al [22] showed NP-hardness in case of preemptive jobs, but their assumption does not allow a job execution to continue over multiple feasible intervals. Instead, partial work is lost at the end of each feasible interval if a job is incomplete.…”

Section: Schedulingmentioning

confidence: 99%

Optimal Speed Control of Mobile Node for Data Collection in Sensor Networks

Sugihara

Gupta

2010

IEEE Trans. on Mobile Comput.

140

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Abstract-A data mule represents a mobile device that collects data in a sensor field by physically visiting the nodes in a sensor network. The data mule collects data when it is in the proximity of a sensor node. This can be an alternative to multihop forwarding of data when we can utilize node mobility in a sensor network. To be useful, a data mule approach needs to minimize data delivery latency. In this paper, we first formulate the problem of minimizing the latency in the data mule approach. The data mule scheduling (DMS) problem is a scheduling problem that has both location and time constraints. Then, for the 1D case of the DMS problem, we design an efficient heuristic algorithm that incorporates constraints on the data mule motion dynamics. We provide lower bounds of solutions to evaluate the quality of heuristic solutions. Through numerical experiments, we show that the heuristic algorithm runs fast and yields good solutions that are within 10 percent of the optimal solutions.

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Section: Schedulingmentioning

confidence: 99%

Optimal Speed Control of Mobile Node for Data Collection in Sensor Networks

Sugihara

Gupta

2010

IEEE Trans. on Mobile Comput.

140

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“…Similar to the algorithms proposed by Shih et al (2003), a solution for the IJS problem has two parts: feasible interval selection and job scheduling. The first part selects one feasible interval I i, j for job J i if a feasible schedule exists.…”

Section: Algorithm Least Execution Time First (Lecf) For Preemptible mentioning

confidence: 98%

“…A schedule is said feasible when all the jobs in the job set complete in time as defined in Definition 1. The following theorem stated in Shih et al (2003) shows that unless P = N P, there does not exist any polynomial-time algorithm to determine if there exists a feasible schedule for all the jobs in job set J. (Shih et al, 2003)] Finding a schedule to complete all the jobs in a set of interval jobs in time is N Pcomplete.…”

Section: Multiple Feasible Interval Jobsmentioning

confidence: 99%

“…As long as the job with N feasible intervals completes in time, the error of the periodic task is under the allowed threshold. Shih et al (2003) showed that it is N P-complete to determine if there exists a schedule for a set of jobs such that all the jobs meet their timing constraints. They developed optimal and sub-optimal algorithms for off-line scheduling and a family of heuristics for on-line scheduling.…”

Section: Introductionmentioning

confidence: 99%

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Approximation algorithms for scheduling real-time jobs with multiple feasible intervals

Chen

Shih

2006

Real-Time Syst

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Time-critical jobs in many real-time applications have multiple feasible intervals. Such a job is constrained to execute from start to completion in one of its feasible intervals. A job fails if the job remains incomplete at the end of the last feasible interval. Earlier works developed an optimal off-line algorithm to schedule all the jobs in a given job set and on-line heuristics to schedule the jobs in a best effort manner. This paper is concerned with how to find a schedule in which the number of jobs completed in one of their feasible intervals is maximized. We show that the maximization problem is N P-hard for both non-preemptible and preemptible jobs. This paper develops two approximation algorithms for non-preemptible and preemptible jobs. When jobs are non-preemptible, Algorithm Least Earliest Completion Time First (LECF) is shown to have a 2-approximation factor; when jobs are preemptible, Algorithm Least Execution Time First (LEF) is proved being a 3-approximation algorithm. We show that our analysis for the two algorithms are tight. We also evaluate our algorithms by extensive simulations. Simulation results show that Algorithms LECF and LEF not only guarantee the approximation factors but also outperform other multiple feasible interval scheduling algorithms in average.

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“…On the other hand, our problem is not standard in that each job may have multiple feasible intervals. As far as we know, there are only a few studies of this case: for unitlength, non-preemptible jobs [Simons and Sipser 1984] and for preemptible, noncontinuable (i.e., jobs must be completed within one feasible interval) jobs [Shih et al 2003;Chen et al 2005], both of which are different from our problem of interest.…”

Section: Related Workmentioning

confidence: 99%

Speed control and scheduling of data mules in sensor networks

Sugihara

Gupta

2010

ACM Trans. Sen. Netw.

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Unlike traditional multihop forwarding among stationary sensor nodes, use of mobile devices for data collection in wireless sensor networks has recently been gathering more attention. The use of mobility significantly reduces the energy consumption at sensor nodes, elongating the functional lifetime of the network. However, a drawback is an increased data delivery latency. Reducing the latency through optimizing the motion of data mules is critical for this approach to thrive. In this article, we focus on the problem of motion planning, specifically, determination of the speed of the data mule and the scheduling of the communication tasks with the sensors. We consider three models of mobility capability of the data mule to accommodate different types of vehicles. Under each mobility model, we design optimal and heuristic algorithms for different problems: single data mule case, single data mule with periodic data generation case, and multiple data mules case. We compare the performance of the heuristic algorithm with a naive algorithm and also with the multihop forwarding approach by numerical experiments. We also compare one of the optimal algorithms with a previously proposed method to see how our algorithm improves the performance and is also useful in practice. As far as we know, this study is the first of a kind that provides a systematic understanding of the motion planning problem of data mules.

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Scheduling Jobs with Multiple Feasible Intervals

Abstract: Abstract

Cited by 10 publications

References 8 publications

Optimal Speed Control of Mobile Node for Data Collection in Sensor Networks

Optimal Speed Control of Mobile Node for Data Collection in Sensor Networks

Approximation algorithms for scheduling real-time jobs with multiple feasible intervals

Speed control and scheduling of data mules in sensor networks

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