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Статті в журналах з теми "Scheduling of Unreliable Jobs"

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Автор: Grafiati

Опубліковано: 14 березня 2023

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1

Naseera, Shaik. "Dynamic Job Scheduling Strategy for Unreliable Nodes in a Volunteer Desktop Grid." International Journal of Grid and High Performance Computing 8, no. 4 (October 2016): 21–33. http://dx.doi.org/10.4018/ijghpc.2016100102.

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Desktop grid aims to harvest a number of idle desktop computers owned by individuals on the edge of internet. Now days, Desktop grids are gaining increasing popularity because of the advances in the technology and availability of high computing power from the desktops. Volunteer nodes in a Desktop Grid encounter two types of failures: volatility failure and interference failure. Volatile failures are due to periodic maintenance, machine breakdown, system crash or shutdown etc that make node inaccessible to the Desktop Grid user. Interference failures are due to volunteer autonomic nature that the node owner can withdraw participation from public execution due to the need to execute the private jobs. This makes the node inaccessible to the Desktop Grid user and may cause partial or entire loss of the public job execution. Volunteer interferences cause slowdown in the execution of the jobs. In this paper the author present a job scheduling algorithm that analyze the nature of volunteer interference failures for effective scheduling of jobs.

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2

Agnetis, Alessandro, Paolo Detti, and Marco Pranzo. "The list scheduling algorithm for scheduling unreliable jobs on two parallel machines." Discrete Applied Mathematics 165 (March 2014): 2–11. http://dx.doi.org/10.1016/j.dam.2012.09.014.

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3

Raza, Zahid, and Deo P. Vidyarthi. "Reliability Based Scheduling Model (RSM) for Computational Grids." International Journal of Distributed Systems and Technologies 2, no. 2 (April 2011): 20–37. http://dx.doi.org/10.4018/jdst.2011040102.

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Computational Grid attributed with distributed load sharing has evolved as a platform to large scale problem solving. Grid is a collection of heterogeneous resources, offering services of varying natures, in which jobs are submitted to any of the participating nodes. Scheduling these jobs in such a complex and dynamic environment has many challenges. Reliability analysis of the grid gains paramount importance because grid involves a large number of resources which may fail anytime, making it unreliable. These failures result in wastage of both computational power and money on the scarce grid resources. It is normally desired that the job should be scheduled in an environment that ensures maximum reliability to the job execution. This work presents a reliability based scheduling model for the jobs on the computational grid. The model considers the failure rate of both the software and hardware grid constituents like application demanding execution, nodes executing the job, and the network links supporting data exchange between the nodes. Job allocation using the proposed scheme becomes trusted as it schedules the job based on a priori reliability computation.

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4

Kaid, Husam, Abdulrahman Al-Ahmari, Adel Al-Shayea, Emad Abouel Nasr, Ali K. Kamrani, and Haitham A. Mahmoud. "Metaheuristics for optimizing unrelated parallel machines scheduling with unreliable resources to minimize makespan." Advances in Mechanical Engineering 14, no. 5 (May 2022): 168781322210970. http://dx.doi.org/10.1177/16878132221097023.

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Parallel machines scheduling problems with continuous availability of machines are NP-hardness (non-deterministic polynomial-time hardness) and have become very popular for the last decade; there is still very limited literature on this problem. The purpose of this paper is to focus on the problem of scheduling n independent jobs to be processed on m unrelated identical parallel machines with availability constraints to minimize the maximum completion time of jobs (makespan). For this NP-hard problem, a mixed-integer linear programming (MILP) model is proposed to find an optimal solution for this problem. Two metaheuristics, tabu search (TS) and simulated annealing (SA) are proposed to solve large scale problem. Moreover, the performance of the solution obtained by the proposed metaheuristics is evaluated based on a lower bound, which decreases the time required to find the optimal solution. Extensive experiments are carried out to assess the performance of all proposed metaheuristics. The computational results highlight the ability of the proposed metaheuristics to obtain optimal solutions for most of the instances compared with the solutions of the proposed MILP model and lower bounds. Moreover, SA and TS can provide good efficiency for the problem in any jobs size and any machine size, but TS provides worse CPU time as the size of jobs become large.

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5

NANDAGOPAL, MALARVIZHI, S. GAJALAKSHMI, and V. RHYMEND UTHARIARAJ. "SCHEDULING WITH JOB CHECKPOINT IN COMPUTATIONAL GRID ENVIRONMENT." International Journal of Modeling, Simulation, and Scientific Computing 02, no. 03 (September 2011): 299–316. http://dx.doi.org/10.1142/s1793962311000517.

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Computational grids have the potential for solving large-scale scientific applications using heterogeneous and geographically distributed resources. In addition to the challenges of managing and scheduling these applications, reliability challenges arise because of the unreliable nature of grid infrastructure. Two major problems that are critical to the effective utilization of computational resources are efficient scheduling of jobs and providing fault tolerance in a reliable manner. This paper addresses these problems by combining the checkpoint replication based fault tolerance mechanism with minimum total time to release (MTTR) job scheduling algorithm. TTR includes the service time of the job, waiting time in the queue, transfer of input and output data to and from the resource. The MTTR algorithm minimizes the response time by selecting a computational resource based on job requirements, job characteristics, and hardware features of the resources. The fault tolerance mechanism used here sets the job checkpoints based on the resource failure rate. If resource failure occurs, the job is restarted from its last successful state using a checkpoint file from another grid resource. Globus ToolKit is used as the grid middleware to set up a grid environment and evaluate the performance of the proposed approach. The monitoring tools Ganglia and Network Weather Service are used to gather hardware and network details, respectively. The experimental results demonstrate that, the proposed approach effectively schedule the grid jobs with fault-tolerant way thereby reduces TTR of the jobs submitted in the grid. Also, it increases the percentage of jobs completed within specified deadline and making the grid trustworthy.

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6

Agnetis, Alessandro, and Thomas Lidbetter. "The Largest-Z-ratio-First algorithm is 0.8531-approximate for scheduling unreliable jobs on m parallel machines." Operations Research Letters 48, no. 4 (July 2020): 405–9. http://dx.doi.org/10.1016/j.orl.2020.05.006.

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7

Lee, Jonghyuk, Sungjin Choi, Taeweon Suh, and Heonchang Yu. "Mobility-aware balanced scheduling algorithm in mobile Grid based on mobile agent." Knowledge Engineering Review 29, no. 4 (September 2014): 409–32. http://dx.doi.org/10.1017/s0269888914000149.

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AbstractThe emerging Grid is extending the scope of resources to mobile devices and sensors that are connected through loosely connected networks. Nowadays, the number of mobile device users is increasing dramatically and the mobile devices provide various capabilities such as location awareness that are not normally incorporated in fixed Grid resources. Nevertheless, mobile devices exhibit inferior characteristics such as poor performance, limited battery life, and unreliable communication, compared with fixed Grid resources. Especially, the intermittent disconnection from network owing to users’ movements adversely affects performance, and this characteristic makes it inefficient and troublesome to adopt the synchronous message delivery in mobile Grid. This paper presents a mobile Grid system architecture based on mobile agents that support the location management and the asynchronous message delivery in a multi-domain proxy environment. We propose a novel balanced scheduling algorithm that takes users’ mobility into account in scheduling. We analyzed users mobility patterns to quantitatively measure the resource availability, which is classified into three types: full availability, partial availability, and unavailability. We also propose an adaptive load-balancing technique by classifying mobile devices into nine groups depending on availability and by utilizing adaptability based on the multi-level feedback queue to handle the job type change. The experimental results show that our scheduling algorithm provides a superior performance in terms of execution times to the one without considering mobility and adaptive load-balancing.

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8

Agnetis, Alessandro, Paolo Detti, Marco Pranzo, and Manbir S. Sodhi. "Sequencing unreliable jobs on parallel machines." Journal of Scheduling 12, no. 1 (July 19, 2008): 45–54. http://dx.doi.org/10.1007/s10951-008-0076-6.

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9

Bodlaender, Hans L., Klaus Jansen, and Gerhard J. Woeginger. "Scheduling with incompatible jobs." Discrete Applied Mathematics 55, no. 3 (December 1994): 219–32. http://dx.doi.org/10.1016/0166-218x(94)90009-4.

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10

Wang, Kai, Vincent Chau, and Minming Li. "Scheduling fully parallel jobs." Journal of Scheduling 21, no. 6 (April 16, 2018): 619–31. http://dx.doi.org/10.1007/s10951-018-0563-3.

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11

Wang, Pu Patrick. "Releasing N jobs to an unreliable machine." Computers & Industrial Engineering 26, no. 4 (October 1994): 661–71. http://dx.doi.org/10.1016/0360-8352(94)90004-3.

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12

Anton, E., R. Righter, and I. M. Verloop. "Scheduling under redundancy." ACM SIGMETRICS Performance Evaluation Review 50, no. 2 (August 30, 2022): 30–32. http://dx.doi.org/10.1145/3561074.3561085.

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Анотація:

In the present extended abstract we investigate the impact that the scheduling policy has on the performance of redundancy systems when the usual exponentially distributed i.i.d. copies assumption is relaxed. In particular, we investigate the performance, in terms of the total number of jobs in the system, not only for redundancy-oblivious policies, such as FCFS (First-Come-First-Serve) and ROS (Random- Order-of-Service), but also for redundancy-aware policies of the form Π1-?2, where Π1 discriminates among job classes and Π2 discriminates among jobs of the same class. Examples of first-level policies are LRF (Least-Redundant-First) and MRF (Most-Redundant-First), where under LRF, respectively MRF, within a server jobs with fewer copies, respectively more copies, have priority over jobs with more copies, respectively fewer copies. Second-level policies could be FCFS or ROS.

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13

Abed, Fidaa, Lin Chen, Yann Disser, Martin Groß, Nicole Megow, Julie Meißner, Alexander T. Richter, and Roman Rischke. "Scheduling maintenance jobs in networks." Theoretical Computer Science 754 (January 2019): 107–21. http://dx.doi.org/10.1016/j.tcs.2018.02.020.

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14

Błażewicz, J., and M. Drozdowski. "Scheduling divisible jobs on hypercubes†." Parallel Computing 21, no. 12 (December 1995): 1945–56. http://dx.doi.org/10.1016/0167-8191(95)00046-1.

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15

Boyar, Joan, and Lene M. Favrholdt. "Scheduling Jobs on Grid Processors." Algorithmica 57, no. 4 (November 22, 2008): 819–47. http://dx.doi.org/10.1007/s00453-008-9257-0.

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16

Bar-Ilan, Judit, and David Peleg. "Scheduling Jobs Using Common Resources." Information and Computation 125, no. 1 (February 1996): 52–61. http://dx.doi.org/10.1006/inco.1996.0020.

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17

Tamir, Tami. "Scheduling with Bully Selfish Jobs." Theory of Computing Systems 50, no. 1 (June 11, 2011): 124–46. http://dx.doi.org/10.1007/s00224-011-9336-5.

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18

Zhao, Chuan-Li, and Heng-Yong Tang. "Scheduling deteriorating jobs under disruption." International Journal of Production Economics 125, no. 2 (June 2010): 294–99. http://dx.doi.org/10.1016/j.ijpe.2010.02.009.

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19

Righter, Rhonda, and Susan XU. "Scheduling jobs on heterogeneous processors." Annals of Operations Research 29, no. 1 (December 1991): 587–601. http://dx.doi.org/10.1007/bf02283615.

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20

Wang, J.-B., J.-J. Wang, and P. Ji. "Scheduling jobs with chain precedence constraints and deteriorating jobs." Journal of the Operational Research Society 62, no. 9 (September 2011): 1765–70. http://dx.doi.org/10.1057/jors.2010.120.

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21

Zou, Juan, and Yuzhong Zhang. "Scheduling Simple Linear Deteriorating Jobs with Rejection." Mathematical Problems in Engineering 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/170475.

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Анотація:

We consider the problems of scheduling deteriorating jobs with release dates on a single machine (parallel machines) and jobs can be rejected by paying penalties. The processing time of a job is a simple linear increasing function of its starting time. For a single machine model, the objective is to minimize the maximum lateness of the accepted jobs plus the total penalty of the rejected jobs. We show that the problem is NP-hard in the strong sense and presents a fully polynomial time approximation scheme to solve it when all jobs have agreeable release dates and due dates. For parallel-machine model, the objective is to minimize the maximum delivery completion time of the accepted jobs plus the total penalty of the rejected jobs. When the jobs have identical release dates, we first propose a fully polynomial time approximation scheme to solve it. Then, we present a heuristic algorithm for the case where all jobs have to be accepted and evaluate its efficiency by computational experiments.

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22

Moghaddam, Atefeh, Lionel Amodeo, Farouk Yalaoui, and Behrooz Karimi. "Single Machine Scheduling with Rejection." International Journal of Applied Evolutionary Computation 3, no. 2 (April 2012): 42–61. http://dx.doi.org/10.4018/jaec.2012040103.

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In this paper, the authors consider a single machine scheduling problem with rejection. In traditional research, it is assumed all jobs must be processed. However, in the real-world situation, certain jobs can be rejected. In this study, the jobs can be either accepted and scheduled or be rejected at the cost of a penalty. Two objective functions are considered simultaneously: (1) minimization of the sum of weighted completion times for the accepted jobs, and (2) minimization of the sum of penalties for the rejected jobs. The authors apply two-phase method (TPM), which is a general technique to solve bi-objective combinatorial optimization problems, to find all supported and non-supported solutions for small-sized problems. The authors present a mathematical model for implementing both phases. On the other hand, three different bi-objective simulated annealing algorithms have also been developed to find a good estimation of Pareto-optimal solutions for large-sized problems. Finally the authors discuss the results obtained from each of these algorithms.

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23

Romanuke, Vadim. "Minimal Total Weighted Tardiness in Tight-Tardy Single Machine Preemptive Idling-Free Scheduling." Applied Computer Systems 24, no. 2 (December 1, 2019): 150–60. http://dx.doi.org/10.2478/acss-2019-0019.

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Abstract Two possibilities of obtaining the minimal total weighted tardiness in tight-tardy single machine preemptive idling-free scheduling are studied. The Boolean linear programming model, which allows obtaining the exactly minimal tardiness, becomes too time-consuming as either the number of jobs or numbers of job parts increase. Therefore, a heuristic based on remaining available and processing periods is used instead. The heuristic schedules 2 jobs always with the minimal tardiness. In scheduling 3 to 7 jobs, the risk of missing the minimal tardiness is just 1.5 % to 3.2 %. It is expected that scheduling 12 and more jobs has at the most the same risk or even lower. In scheduling 10 jobs without a timeout, the heuristic is almost 1 million times faster than the exact model. The exact model is still applicable for scheduling 3 to 5 jobs, where the averaged computation time varies from 0.1 s to 1.02 s. However, the maximal computation time for 6 jobs is close to 1 minute. Further increment of jobs may delay obtaining the minimal tardiness at least for a few minutes, but 7 jobs still can be scheduled at worst for 7 minutes. When scheduling 8 jobs and more, the exact model should be substituted with the heuristic.

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24

Li, Shi-Sheng, De-Liang Qian, and Ren-Xia Chen. "Proportionate Flow Shop Scheduling with Rejection." Asia-Pacific Journal of Operational Research 34, no. 04 (August 2017): 1750015. http://dx.doi.org/10.1142/s0217595917500154.

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We consider the problem of scheduling [Formula: see text] jobs with rejection on a set of [Formula: see text] machines in a proportionate flow shop system where the job processing times are machine-independent. The goal is to find a schedule to minimize the scheduling cost of all accepted jobs plus the total penalty of all rejected jobs. Two variations of the scheduling cost are considered. The first is the maximum tardiness and the second is the total weighted completion time. For the first problem, we first show that it is [Formula: see text]-hard, then we construct a pseudo-polynomial time algorithm to solve it and an [Formula: see text] time for the case where the jobs have the same processing time. For the second problem, we first show that it is [Formula: see text]-hard, then we design [Formula: see text] time algorithms for the case where the jobs have the same weight and for the case where the jobs have the same processing time.

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25

Brzęczek, Tomasz, and Dariusz Nowak. "Genetic Algorithm Modification for Production Scheduling." Foundations of Computing and Decision Sciences 38, no. 4 (December 1, 2013): 299–309. http://dx.doi.org/10.2478/fcds-2013-0015.

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Abstract Scheduling of production soundly affects its capacity especially if system does complex production jobs. In the theoretical part of the article an overview of the scheduling methods proposed in the literature was presented. In this paper it was stated a variant of job shop problem, in which jobs can overlap in some machines and omit others. Authors designed and presented here genetic algorithm to optimize solution of such a problem. The algorithm finds jobs sequence priority and in accordance with it schedules operations and calculates their completion time. An adequate problem was met in an examined plant, where 20 production jobs consisted of 11 to 20 operations assigned to at most 15 machines. Such big parameter numbers are crucial for big formal models and their solution algorithms. The designed algorithm proved to deal with parameters scale, as it found the schedule with 23,8% shorter jobs completion time in comparison with FIFO heuristic, that has been used so far by the plant.

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26

Agnetis, Alessandro, Mario Benini, Paolo Detti, Ben Hermans, and Marco Pranzo. "Replication and sequencing of unreliable jobs on parallel machines." Computers & Operations Research 139 (March 2022): 105634. http://dx.doi.org/10.1016/j.cor.2021.105634.

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27

Shim, Sang Oh, and Seong Woo Choi. "Scheduling Jobs on Dedicated Parallel Machines." Applied Mechanics and Materials 433-435 (October 2013): 2363–66. http://dx.doi.org/10.4028/www.scientific.net/amm.433-435.2363.

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This paper considers scheduling problem on dedicated parallel machines where several types of machines are grouped into one process. The dedicated machine is that a job with a specific recipe should be processed on the dedicated machine even though the job can be produced on any other machine originally. In this process, a setup is required when different jobs are done consecutively. To minimize the completion time of the last job, a scheduling method is developed. Computational experiments are performed on a number of test problems and results show that the suggested algorithm give good solutions in a reasonable amount of computation time.

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28

Kravchenko, Svetlana A., and Frank Werner. "Scheduling Jobs with Equal Processing Times." IFAC Proceedings Volumes 42, no. 4 (2009): 1262–67. http://dx.doi.org/10.3182/20090603-3-ru-2001.0042.

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29

Indika, S. H. Sathish, and Douglas R. Shier. "Optimization Models for Scheduling of Jobs." Journal of Research of the National Institute of Standards and Technology 111, no. 2 (March 2006): 103. http://dx.doi.org/10.6028/jres.111.009.

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30

Gordon, V. S., and A. B. Dolgui. "Scheduling problems with partially ordered jobs." Automation and Remote Control 71, no. 10 (October 2010): 2029–37. http://dx.doi.org/10.1134/s0005117910100036.

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31

Han, Ching-Chih, Kwei-Jay Lin, and Jane W. S. Liu. "Scheduling Jobs with Temporal Distance Constraints." SIAM Journal on Computing 24, no. 5 (October 1995): 1104–21. http://dx.doi.org/10.1137/s0097539791218081.

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32

Baruah, Sanjoy, Vincenzo Bonifaci, Gianlorenzo D'Angelo, Haohan Li, Alberto Marchetti-Spaccamela, Nicole Megow, and Leen Stougie. "Scheduling Real-Time Mixed-Criticality Jobs." IEEE Transactions on Computers 61, no. 8 (August 2012): 1140–52. http://dx.doi.org/10.1109/tc.2011.142.

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33

Mosheiov, G. "A note on scheduling deteriorating jobs." Mathematical and Computer Modelling 41, no. 8-9 (April 2005): 883–86. http://dx.doi.org/10.1016/j.mcm.2004.09.004.

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34

Xing, Wenxun, and Jiawei Zhang. "Parallel machine scheduling with splitting jobs." Discrete Applied Mathematics 103, no. 1-3 (July 2000): 259–69. http://dx.doi.org/10.1016/s0166-218x(00)00176-1.

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35

Wang, Zhenbo, and Wenxun Xing. "Parallel machine scheduling with special jobs." Tsinghua Science and Technology 11, no. 1 (February 2006): 107–10. http://dx.doi.org/10.1016/s1007-0214(06)70162-6.

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36

Fox, Kyle, Sungjin Im, and Benjamin Moseley. "Energy efficient scheduling of parallelizable jobs." Theoretical Computer Science 726 (May 2018): 30–40. http://dx.doi.org/10.1016/j.tcs.2018.02.021.

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37

Dereniowski, Dariusz, and Wiesław Kubiak. "Shared processor scheduling of multiprocessor jobs." European Journal of Operational Research 282, no. 2 (April 2020): 464–77. http://dx.doi.org/10.1016/j.ejor.2019.09.033.

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38

Demange, M., D. de Werra, J. Monnot, and V. Th Paschos. "Time slot scheduling of compatible jobs." Journal of Scheduling 10, no. 2 (February 1, 2007): 111–27. http://dx.doi.org/10.1007/s10951-006-0003-7.

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39

Barketau, M. S., T. C. E. Cheng, C. T. Ng, Vladimir Kotov, and Mikhail Y. Kovalyov. "Batch scheduling of step deteriorating jobs." Journal of Scheduling 11, no. 1 (November 20, 2007): 17–28. http://dx.doi.org/10.1007/s10951-007-0047-3.

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40

Hezarkhani, Behzad, and Wiesław Kubiak. "Decentralized subcontractor scheduling with divisible jobs." Journal of Scheduling 18, no. 5 (June 2, 2015): 497–511. http://dx.doi.org/10.1007/s10951-015-0432-2.

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41

Knust, S., N. V. Shakhlevich, S. Waldherr, and C. Weiß. "Shop scheduling problems with pliable jobs." Journal of Scheduling 22, no. 6 (April 2, 2019): 635–61. http://dx.doi.org/10.1007/s10951-019-00607-9.

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42

Gawiejnowicz, Stanisław, and Lidia Pankowska. "Scheduling jobs with varying processing times." Information Processing Letters 54, no. 3 (May 1995): 175–78. http://dx.doi.org/10.1016/0020-0190(95)00009-2.

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43

Jansen, Klaus, and Ralf Thöle. "Approximation Algorithms for Scheduling Parallel Jobs." SIAM Journal on Computing 39, no. 8 (January 2010): 3571–615. http://dx.doi.org/10.1137/080736491.

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44

Janiak, A., and R. Rudek. "Scheduling jobs under an aging effect." Journal of the Operational Research Society 61, no. 6 (June 2010): 1041–48. http://dx.doi.org/10.1057/jors.2009.30.

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45

Srinivasan, Sudha, Savitha Krishnamoorthy, and P. Sadayappan. "Robust scheduling of moldable parallel jobs." International Journal of High Performance Computing and Networking 2, no. 2/3/4 (2004): 120. http://dx.doi.org/10.1504/ijhpcn.2004.008913.

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