Relevant bibliographies by topics / Scheduling of Unreliable Jobs

Academic literature on the topic 'Scheduling of Unreliable Jobs'

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Published: 14 March 2023

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Journal articles on the topic "Scheduling of Unreliable Jobs"

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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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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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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Scheduling of Unreliable Jobs"

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Mario, Benini. "Improving Decision Making in Real-world Applications by Solving Combinatorial Optimization Problems." Doctoral thesis, Università di Siena, 2022. https://hdl.handle.net/11365/1221594.

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The motivation for this work is to study complex real-world scenarios and provide tools that can actually improve decision-making in those problems. To do so, we mainly adopt techniques from the fields of Operations Research and Combinatorial Optimization. In this dissertation, we focus on three real-world applications from different industries that can be modeled as combinatorial optimization problems and address them with operations research techniques. The dissertation is divided in chapters, each of which is related to a different topic. In Chapter 1, a problem concerning the transportation of biological samples from draw centers to a main laboratory for analysis is presented. The problem arises from a healthcare application in Bologna, Italy, where the healthcare authority decided to centralize the analysis of all biological samples of the area to a main laboratory, in order to exploit economies of scales and reduce the costs for samples’ analysis. Of course, such an improvement goal also created a new complex problem: all the samples must be transported from draw centers to the main lab. A fleet of vehicle is available for the transportation and must collect the samples from draw centers during given times of the day and deliver them within a certain time, since samples are perishable. Vehicles can also exploit the existence of dedicated centers that can extend the lifespan of the samples and where samples can be transferred from one vehicle to another. It is clear from this brief description how hard it could be to decide which is the routing of all the vehicles which minimizes the traveling costs while delivering all samples on time. For this problem we developed different mixed integer linear programming models, metaheuristic algorithms, and grouping policies for the samples that are able to tackle the complexity of the problem and improve routing decisions. All methods have been tested through an extensive computational campaign using real-world data, showing the effectiveness of the proposed approaches. In Chapter 2, a problem related to the agricultural industry is presented. The problem arises from a real-world application in Italy and it is that of planning the use of the available land of a farm for a given number of years, given a set of crops that can be grown. The objective is to maximize the farmer’s profit, but the farmer is subject to several rules both from an agronomic and from a regulation point of view. In fact, many constraints exist regarding agronomic principles, such as maximum replanting, botanical family constraints and crop rotation issues. One of the goals of this work is indeed that of evaluating the risks and benefits of following or not the best practices regarding crop rotation issues in the Mediterranean pedo-climatic context. Furthermore, we want to evaluate the effectiveness of public and private initiatives regarding sustainable agriculture. In fact, it is more and more important nowadays to face these challenges in the food supply chain, which is one of the most discussed industries when it comes to sustainability. In particular, we analyze two different initiatives, namely the Common Agricultural Policy by the European Union and “La Carta del Mulino” by Barilla Group S.p.A.. Both initiatives introduce economic incentives for the farmers following virtuous behaviors from a sustainability point of view. Practically, these behaviors are constraints increasing the complexity of the problem and the difficulty in the decision-making process. For this problem, we will give a formal characterization and study its complexity, also analyzing special cases. We will also present a network-flow based model to solve a special case of the problem and integer linear programming models developed to solve three variants accounting for different sustainability scenarios. Real-world data from 23 Italian farms were used in an extensive computational campaign. The analysis of the results shows that the models can be helpful tools for farmers to plan their production and for authorities to evaluate the effectiveness (and efficiency) of their sustainability initiatives. In Chapter 3, we discuss a problem concerning the sequencing of unreliable jobs on parallel machines. Even if the problem is not taken from a specific application, it may have several applications in real-world scenarios, such as in manufacturing and planning of complex computations on multi-processors computers. In this problem, we have n unreliable jobs providing a reward when successfully completed, but each job has a probability of not being carried out. We have m parallel identical machines at our disposal, and we want to schedule the jobs on the machines in order to maximize the total expected reward. To increase the probability of completing the jobs, we create m copies of each job and schedule each copy on a different machine. For this problem, we will present a complexity analysis showing that the problem is NP-complete for two machines. For the problem with two machines, we derived some theoretical properties and developed a quadratic integer programming model, a tabu search algorithm, and an upper bound based on the Three-Dimensional Assignment problem. A computational campaign on different sets of instances shows that the tabu search outperforms the model. Then we focused on the general case with m machines. In particular, we developed several heuristics and proved some theoretical results, including the worst case performance guarantee of two heuristics. We also devised a generalized tabu search algorithm and a new, improved, upper bounding scheme based on a relaxation of the problem. Computational experiments are performed for the new methods on the problems with two and three machines. The results show that good optimality gaps are reached on all the instances.
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Song, Bin 1970. "Scheduling adaptively parallel jobs." Thesis, Massachusetts Institute of Technology, 1998. http://hdl.handle.net/1721.1/50354.

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Han, Kai. "Scheduling Distributed Real-Time Tasks in Unreliable and Untrustworthy Systems." Diss., Virginia Tech, 2010. http://hdl.handle.net/10919/26917.

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In this dissertation, we consider scheduling distributed soft real-time tasks in unreliable (e.g., those with arbitrary node and network failures) and untrustworthy systems (e.g., those with Byzantine node behaviors). We present a distributed real-time scheduling algorithm called Gamma. Gamma considers a distributed (i.e., multi-node) task model where tasks are subject to Time/Utility Function (or TUF) end-to-end time constraints, and the scheduling optimality criterion of maximizing the total accrued utility. The algorithm makes three novel contributions. First, Gamma uses gossip for reliably propagating task scheduling parameters and for discovering task execution nodes. Second, Gamma achieves distributed real-time mutual exclusion in unreliable environments. Third, the algorithm guards against potential disruption of message propagation due to Byzantine attacks using a mechanism called Launcher-Attacker-Infective-Susceptible-Immunized-Removed-Consumer (or LAISIRC). By doing so, the algorithm schedules tasks with probabilistic termination-time satisfactions, despite system unreliability and untrustworthiness. We analytically establish several timeliness and non-timeliness properties of the algorithm including probabilistic end-to-end task termination time satisfactions, optimality of message overheads, mutual exclusion guarantees, and the mathematical model of the LAISIRC mechanism. We conducted simulation-based experimental studies and compared Gamma with its competitors. Our experimental studies reveal that Gammaâ s scheduling algorithm accrues greater utility and satisfies a greater number of deadlines than do competitor algorithms (e.g., HVDF) by as much as 47% and 45%, respectively. LAISIRC is more tolerant to Byzantine attacks than competitor protocols (e.g., Path Verification) by obtaining as much as 28% higher correctness ratio. Gammaâ s mutual exclusion algorithm accrues greater utility than do competitor algorithms (e.g., EDF-Sigma) by as much as 25%. Further, we implemented the basic Gamma algorithm in the Emulab/ChronOS 250-node testbed, and measured the algorithmâ s performance. Our implementation measurements validate our theoretical analysis and the algorithm's effectiveness and robustness.
Ph. D.
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Ramachandra, Girish. "Scheduling Precedence Related Jobs on Identical Parallel Processors." NCSU, 2002. http://www.lib.ncsu.edu/theses/available/etd-20020121-185145.

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The problem of concern to us in this thesis is the scheduling ofprecedence-related jobs non-preemptively on two identical parallelprocessors to minimize the sum of the weighted completion times. The problemis known to be NP-hard.We develop, in chapter 2, a binary integer program which iscapable of solving only small size problems (no larger than 12jobs) to optimality at the present time. We also present a linearprogramming(LP) model adopted from the literature todetermine the lower bound on the optimum. This LP stands us ingood stead when we perform the optimization via the GeneticAlgorithm approach (which is the subject matter of chapter 3). Wealso present a dynamic programming formulation based on theapproach used for solving the "weighted earliness-tardiness"problem. Although DP expands somewhat the size of the problemsthat can be solved to optimality, its computing burden becomesonerous for more than 25 jobs.In an attempt to solve larger, and more realistic problems, a GeneticAlgorithm(GA) is presented in chapter 3. The salient feature of the GAmodel is that the "initial population" of trial solutions are not allrandomly generated but are constituted from a set of priority rules whichare known to be "good" relaxation (in the sense of being "close" to theoptimum) of the original problem. Also, generation of infeasible solutionsis avoided by the use of post-processing procedures after crossover andmutation operations. Computational results show that the GA approach arrivesto within 20% of the lower bound (and hence of the optimum) in very fewiterations.

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Simonneau, Nicolas. "Hybrid flow shop scheduling with prescription constraints on jobs." Thesis, Virginia Tech, 2003. http://hdl.handle.net/10919/9659.

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The sponsor of the thesis is the Composite Unit of AIRBUS Nantes plant, which manufactures aircraft composite. The basic process to manufacture composite parts is to lay-up raw composite material on a tool and involves very costly means and raw material. This process can be modeled as a two-stage hybrid flow shop problem with specific constraints, particularly prescription constraints on the jobs. This thesis restates the practical problem as a scheduling problem by doing hypotheses and restrictions. Then, it designs a mathematical model based on time-indexed variables. This model has been implemented in an IP solver to solve real based scenarios. A heuristic algorithm is developed for obtaining good solutions quickly. Finally, the heuristic is used to increase the execution speed of the IP solver. This thesis concludes by a discussion on the advantages and disadvantages of each option (IP solver vs. heuristic software) for the sponsor.
Master of Science
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Yeleswarapu, Radhika M. "Scheduling Of 2-Operation Jobs On A Single Machine To Minimize The Number Of Tardy Jobs." [Tampa, Fla.] : University of South Florida, 2003. http://purl.fcla.edu/fcla/etd/SFE0000216.

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Cutler, Mark Christopher. "A study of scheduling operations with preemptive jobs and global system interruptions /." Thesis, Connect to this title online; UW restricted, 1998. http://hdl.handle.net/1773/8729.

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Spegal, Christopher S. "Unrelated Machine Scheduling with Deteriorating Jobs and Non-zero Ready Times." Ohio University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou154672272196773.

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Gangammanavar, Harshavardhana J. "OPTIMAL CODING AND SCHEDULING TECHNIQUES FOR BROADCASTING DEADLINE CONSTRAINT TRAFFIC OVER UNRELIABLE WIRELESS CHANNELS." The Ohio State University, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=osu1262111942.

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Korkmaz, Gediz. "Batch Scheduling Of Incompatible Jobs On A Single Reactor With Dynamic Arrivals." Master's thesis, METU, 2004. http://etd.lib.metu.edu.tr/upload/12605096/index.pdf.

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In this study, a single machine batch-scheduling problem with incompatible jobs and dynamic arrivals is examined. The objective function is the minimization of the total flow time of the jobs. For solving problems a case specific branch and bound algorithm with a heuristic upper bound scheme and two alternative lower bound procedures is used. An extensive computational experiment is conducted to investigate the effects of certain parameters on the computation time. For the most difficult parameter combination branch and bound algorithm can solve the problems about 25 jobs with 4 different job types in a 10 minutes time on average. For the problem types with higher number of jobs and the most difficult parameter combination proposed upper bound heuristic can be used to obtain near optimal solutions.
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Books on the topic "Scheduling of Unreliable Jobs"

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Liu, Jane W. S. Scheduling real-time, periodic jobs using imprecise results. [Washington, DC?: National Aeronautics and Space Administration, 1987.

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Liu, Jane W. S. Scheduling real-time, periodic jobs using imprecise results. [Washington, DC?: National Aeronautics and Space Administration, 1987.

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Seitz, Thomas. High-level scheduling of parallel jobs in the IBM 3090 production environment. Ithaca, N.Y: Cornell Theory Center, Cornell University, 1991.

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Foulds, L. R. A crash analysis of scheduling to minimize the number of late jobs. Loughborough: Loughborough University Business School, 1993.

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Indian Institute of Management, Ahmedabad., ed. Scheduling partially ordered jobs under resource constraints to optimize non-regular performance measures. Ahmedabad: Indian Institute of Management, 2003.

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Kumar, Anurag. Performance analysis and scheduling of stochastic fork-join jobs in a multicomputer system. Bangalore: Dept. of Electrical Communication Engineering, Indian Institute of Science, 1990.

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Yuk, Lin Ka. Scheduling two-phase jobs with arbitrary time lags in a single-server system. Birmingham: University of Birmingham, 1994.

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Sarkar, Prabir Kumar. Scheduling of n jobs on one machine for a non-regular measure of performance. Ottawa: National Library of Canada, 1990.

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S, Liu Jane W., Lin Kwei-Jay, and United States. National Aeronautics and Space Administration., eds. Scheduling periodic jobs using imprecise results: By Jen-Yao Chung, Jane W.S. Liu, [and] Kwei-Jay Lin. [Washington, DC: National Aeronautics and Space Administration, 1989.

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Inc, ebrary, ed. Mastering Oracle Scheduler in Oracle 11g databases: Schedule, manage, and execute jobs that automate your business processes. Birmingham, U.K: Packt Publishing Ltd., 2009.

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Book chapters on the topic "Scheduling of Unreliable Jobs"

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Mucchetti, Mark. "Scheduling Jobs." In BigQuery for Data Warehousing, 209–30. Berkeley, CA: Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-6186-6_10.

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Bodlaender, Hans L., Klaus Jansen, and Gerhard J. Woeginger. "Scheduling with incompatible jobs." In Graph-Theoretic Concepts in Computer Science, 37–49. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/3-540-56402-0_34.

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Tamir, Tami. "Scheduling with Bully Selfish Jobs." In Lecture Notes in Computer Science, 355–67. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13122-6_34.

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Boyar, Joan, and Lene M. Favrholdt. "Scheduling Jobs on Grid Processors." In Algorithm Theory – SWAT 2006, 17–28. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11785293_5.

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Bougeret, Marin, Pierre Francois Dutot, Klaus Jansen, Christina Robenek, and Denis Trystram. "Scheduling Jobs on Heterogeneous Platforms." In Lecture Notes in Computer Science, 271–83. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22685-4_25.

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Abed, Fidaa, Lin Chen, Yann Disser, Martin Groß, Nicole Megow, Julie Meißner, Alexander T. Richter, and Roman Rischke. "Scheduling Maintenance Jobs in Networks." In Lecture Notes in Computer Science, 19–30. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-57586-5_3.

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Liberatore, Vincenzo. "Scheduling Jobs Before Shut-Down." In Algorithm Theory - SWAT 2000, 175–88. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-44985-x_16.

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Shih, Chi-sheng, Jane W. S. Liu, and Infan Kuok Cheong. "Scheduling Jobs with Multiple Feasible Intervals." In Real-Time and Embedded Computing Systems and Applications, 53–71. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24686-2_4.

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Bampis, Evripidis, Vincent Chau, Dimitrios Letsios, Giorgio Lucarelli, Ioannis Milis, and Georgios Zois. "Energy Efficient Scheduling of MapReduce Jobs." In Lecture Notes in Computer Science, 198–209. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-09873-9_17.

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Bachman, Aleksander, and Adam Janiak. "Scheduling Deteriorating Jobs with Ready Times." In Operations Research Proceedings 1999, 342–48. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-642-58300-1_52.

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Conference papers on the topic "Scheduling of Unreliable Jobs"

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Hsu, Yu-Pin, Yu-Chih Huang, and Shin-Lin Shieh. "Scheduling Stochastic Real-Time Jobs In Unreliable Workers." In 2020 IEEE Wireless Communications and Networking Conference (WCNC). IEEE, 2020. http://dx.doi.org/10.1109/wcnc45663.2020.9120733.

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Shokripour, Amin, and Mohamed Othman. "Scheduling Jobs in a Heterogeneous System with Unreliable Network Links by DLT." In 2010 Second International Conference on Communication Software and Networks. IEEE, 2010. http://dx.doi.org/10.1109/iccsn.2010.114.

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Chang, Ruay-Shiung, Chih-Yuan Lin, and Chun-Fu Lin. "Scheduling Jobs in Grids Adaptively." In 2009 IEEE International Symposium on Parallel and Distributed Processing with Applications. IEEE, 2009. http://dx.doi.org/10.1109/ispa.2009.75.

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Wang, Qing, Ying Chen, and Min Huang. "Hybrid flow shop scheduling of new arrival jobs and locked jobs." In 2021 33rd Chinese Control and Decision Conference (CCDC). IEEE, 2021. http://dx.doi.org/10.1109/ccdc52312.2021.9602295.

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Henzinger, Thomas A., Vasu Singh, Thomas Wies, and Damien Zufferey. "Scheduling large jobs by abstraction refinement." In the sixth conference. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/1966445.1966476.

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Frank, Werner,. "Scheduling Jobs with Equal Processing Times." In Information Control Problems in Manufacturing, edited by Bakhtadze, Natalia, Chair Dolgui, Alexandre and Bakhtadze, Natalia. Elsevier, 2009. http://dx.doi.org/10.3182/20090603-3-ru-2001.00210.

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Carvalho, Renata M., Ricardo M. F. Lima, Adriano L. I. Oliveira, and Felix C. G. Santos. "Scheduling parallel jobs for multiphysics simulators." In 2010 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2010. http://dx.doi.org/10.1109/cec.2010.5586180.

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Fox, Kyle, Sungjin Im, and Benjamin Moseley. "Energy Efficient Scheduling of Parallelizable Jobs." In Proceedings of the Twenty-Fourth Annual ACM-SIAM Symposium on Discrete Algorithms. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2013. http://dx.doi.org/10.1137/1.9781611973105.68.

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Hung, Chien-Chun, Leana Golubchik, and Minlan Yu. "Scheduling jobs across geo-distributed datacenters." In SoCC '15: ACM Symposium on Cloud Computing. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2806777.2806780.

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10

Roci, Albana, and Reggie Davidrajuh. "Scheduling Jobs in Multi-grid Environment." In 2018 UKSim-AMSS 20th International Conference on Computer Modelling and Simulation (UKSim). IEEE, 2018. http://dx.doi.org/10.1109/uksim.2018.00024.

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