Relevant bibliographies by topics / Scheduling tasks

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Scheduling tasks'

Author: Grafiati
Published: 4 June 2021
Last updated: 10 February 2022

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Journal articles on the topic "Scheduling tasks"

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Kaur, Ramandeep, and Navpreet Kaur. "A Study for VM Placement Schemes in Cloud." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 8 (August 30, 2017): 208. http://dx.doi.org/10.23956/ijarcsse.v7i8.52.

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The cloud computing can be essentially expressed as aconveyance of computing condition where distinctive assets are conveyed as a support of the client or different occupants over the web. The task scheduling basically concentrates on improving the productive use of assets and henceforth decrease in task fruition time. Task scheduling is utilized to allot certain tasks to specific assets at a specific time occurrence. A wide range of systems has been exhibited to take care of the issues of scheduling of various tasks. Task scheduling enhances the productive use of asset and yields less reaction time with the goal that the execution of submitted tasks happens inside a conceivable least time. This paper talks about the investigation of need, length and due date based task scheduling calculations utilized as a part of cloud computing.
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Ju, Jiubin, Yong Wang, and Yu Yin. "Scheduling PVM tasks." Journal of Computer Science and Technology 12, no. 2 (March 1997): 167–76. http://dx.doi.org/10.1007/bf02951336.

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Korst, Jan, Emile Aarts, and Jan Karel Lenstra. "Scheduling Periodic Tasks." INFORMS Journal on Computing 8, no. 4 (November 1996): 428–35. http://dx.doi.org/10.1287/ijoc.8.4.428.

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Ju, Jiubin, and Yong Wang. "Scheduling PVM tasks." ACM SIGOPS Operating Systems Review 30, no. 3 (July 1996): 22–31. http://dx.doi.org/10.1145/230908.230914.

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Turek, John, Joel L. Wolf, Krishna R. Pattipati, and Philip S. Yu. "Scheduling parallelizable tasks." ACM SIGMETRICS Performance Evaluation Review 20, no. 1 (June 1992): 225–36. http://dx.doi.org/10.1145/149439.133111.

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Barg-Walkow, Laura H., and Wendy A. Rogers. "Modeling Task Scheduling in Complex Healthcare Environments: Identifying Relevant Factors." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 61, no. 1 (September 2017): 772–75. http://dx.doi.org/10.1177/1541931213601677.

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Multiple task coordination involves scheduling tasks, completing tasks, and integrating tasks into a workflow. Task scheduling can influence outcomes of safety, satisfaction, and efficiency when completing tasks. This is especially important in complex life-critical environments such as healthcare, which incurs many situations where there are multiple tasks and limited resources for addressing all tasks. One approach for understanding tasks coordination is the Strategic Task Overload Management (STOM) model, which is a model for task scheduling behavior. In this theoretical paper, we discuss how this model can be extended to a complex healthcare environment. There are additional considerations (e.g., time) which must be considered when applying this model to healthcare. Ultimately, understanding how emergency physicians make multiple task scheduling decisions will advance theories and models, such as STOM, which can then in turn be implemented to improve scheduling behaviors in complex healthcare environments.
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Lei, Zhenyang, Xiangdong Lei, and Jun Long. "Memory-Aware Scheduling Parallel Real-Time Tasks for Multicore Systems." International Journal of Software Engineering and Knowledge Engineering 31, no. 04 (April 2021): 613–34. http://dx.doi.org/10.1142/s0218194021400106.

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Shared resources on the multicore chip, such as main memory, are increasingly becoming a point of contention. Traditional real-time task scheduling policies focus on solely on the CPU, and do not take in account memory access and cache effects. In this paper, we propose parallel real-time tasks scheduling (PRTTS) policy on multicore platforms. Each set of tasks is represented as a directed acyclic graph (DAG). The priorities of tasks are assigned according to task periods Rate Monotonic (RM). Each task is composed of three phases. The first phase is read memory stage, the second phase is execution phase and the third phase is write memory phase. The tasks use locks and critical sections to protect data access. The global scheduler maintains the task pool in which tasks are ready to be executed which can run on any core. PRTTS scheduling policy consists of two levels: the first level scheduling schedules ready real-time tasks in the task pool to cores, and the second level scheduling schedules real-time tasks on cores. Tasks can preempt the core on running tasks of low priority. The priorities of tasks which want to access memory are dynamically increased above all tasks that do not access memory. When the data accessed by a task is in the cache, the priority of the task is raised to the highest priority, and the task is scheduled immediately to preempt the core on running the task not accessing memory. After accessing memory, the priority of these tasks is restored to the original priority and these tasks are pended, the preempted task continues to run on the core. This paper analyzes the schedulability of PRTTS scheduling policy. We derive an upper-bound on the worst-case response-time for parallel real-time tasks. A series of extensive simulation experiments have been performed to evaluate the performance of proposed PRTTS scheduling policy. The results of simulation experiment show that PRTTS scheduling policy offers better performance in terms of core utilization and schedulability rate of tasks.
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Nayak, Suvendu Chandan, and Chitaranjan Tripathy. "An Improved Task Scheduling Mechanism Using Multi-Criteria Decision Making in Cloud Computing." International Journal of Information Technology and Web Engineering 14, no. 2 (April 2019): 92–117. http://dx.doi.org/10.4018/ijitwe.2019040106.

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In this work, the authors propose Multi-criteria Decision-making to schedule deadline based tasks in cloud computing. The existing backfilling task scheduling algorithm could not handle similar tasks for scheduling. In backfilling algorithm, tasks are backfilled to provide ideal resources to schedule other deadline sensitive tasks. However, the task to be backfilled is selected on first come, first serve (FCFS) basis from scheduling queue. The scheduling performances require to be improved when, there are similar tasks. In this proposed work, the authors propose to implement MCDM technique, TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) to improve the performance of the backfilling algorithm through scheduling deadline sensitive tasks in cloud computing. It resolves the conflicts among the similar tasks that is used as a decision support system. The work is simulated with synthetic data sets based on slack values of the tasks. The performance results affirm the task completion and reduction in task rejection compared to the existing backfilling algorithm.
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Fu, Weina, Shuai Liu, and Gautam Srivastava. "Optimization of Big Data Scheduling in Social Networks." Entropy 21, no. 9 (September 17, 2019): 902. http://dx.doi.org/10.3390/e21090902.

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In social network big data scheduling, it is easy for target data to conflict in the same data node. Of the different kinds of entropy measures, this paper focuses on the optimization of target entropy. Therefore, this paper presents an optimized method for the scheduling of big data in social networks and also takes into account each task’s amount of data communication during target data transmission to construct a big data scheduling model. Firstly, the task scheduling model is constructed to solve the problem of conflicting target data in the same data node. Next, the necessary conditions for the scheduling of tasks are analyzed. Then, the a periodic task distribution function is calculated. Finally, tasks are scheduled based on the minimum product of the corresponding resource level and the minimum execution time of each task is calculated. Experimental results show that our optimized scheduling model quickly optimizes the scheduling of social network data and solves the problem of strong data collision.
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K. Jairam Naik, Dr, and B. Veda Vidhya. "A Group Tasks Scheduling Algorithm for Cloud Computing Networks based on QoS." International Journal of Engineering & Technology 7, no. 4.6 (September 25, 2018): 53. http://dx.doi.org/10.14419/ijet.v7i4.6.20236.

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This article introduces a Novel Group-Tasks Scheduling Algorithm (NGTSA) which is used for allocating the tasks in the network of cloud computing by means of pertaining quality of services to gratify user’s desires. The tasks are categorized into five classes by the anticipated algorithm. Every one group contains the tasks with akin attributes (like, types of the users and tasks, size and latency of the task). Once the tasks are allocated to a precise group, scheduler starts assigning these tasks to accessible services. This assignment of tasks was performed in two steps: In Step-I is to decide which group tasks is to be scheduled foremost. Such decision will be based on the attributes of the tasks of each group. Hence, the groups which have higher task’s attribute values are scheduled foremost. Step-II is for taking internal decision that is which task from the selected group is scheduled foremost. This decision will be based on time needed for task’s execution. Therefore, the task which has the lowest time for execution will schedule foremost.
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Dissertations / Theses on the topic "Scheduling tasks"

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Olsson, Granlund David. "Automated Scheduling of Mining Operation Tasks." Thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-83222.

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The task of scheduling mining operations is a strikingly tough task yet it is still largely done manually by hand or with the help of simple gantt planning tools. This thesis aim is to explore the feasibility of an automatic scheduling solution that can incorporate the constraints specific to mining operations. A constraint programming based solution is presented and evaluated based on its correctness, viability and performance. With its rich set of operators, the constraint programming library OR-Tools is able to capture most of the mining specific constraints and two different objective functions are developed to suit different use cases. One is the well established makespan objective which purpose is to minimize the completion time of the last task. The second objective function, named the sub goal deviation objective, minimizes the deviation from the overall production goal divided into sub goals.  The underlying scheduling problem is notoriously hard to solve optimally for large instances. This is supported by several related studies and also by experimental results. To mitigate the performance degradation for large scheduling instances, an iterative solver strategy is presented. With this strategy the scheduler is able to solve much larger instances and initial tests resulted in the same objective values as the optimal strategy. A rescheduling procedure is presented to support schedule maintenance due to unforeseen circumstances such as delays or machine breakdowns. It is concluded that automatic scheduling and rescheduling is feasible but that it first needs to be evaluated by experienced schedulers in the field before being applied in a production environment.
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Bast, Holger. "Provably optimal scheduling of similar tasks." [S.l. : s.n.], 2000. http://www.bsz-bw.de/cgi-bin/xvms.cgi?SWB10976167.

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Eygelaar, Anton Burger. "Resource constrained step scheduling of project tasks." Thesis, Stellenbosch : University of Stellenbosch, 2008. http://hdl.handle.net/10019.1/4494.

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Thesis (MScEng (Civil Engineering))--University of Stellenbosch, 2008.
Thesis presented in partial fulfilment of the requirements for the degree of Master of Science in Civil Engineering at the University of Stellenbosch.
ENGLISH ABSTRACT: The logical scheduling of activities in an engineering project currently relies heavily on the experience and intuition of the persons responsible for the schedule. In large projects the complexity of the schedule far exceeds the capacity of human intuition, and systematic techniques are required to compute a consistent sequence of activities. In this study a simple model of the engineering process is described. Based on certain specified relationships between components of the model, a consistent sequence of activities is determined in the form of a logical step schedule. The problem of resource constraints receives special attention. Engineering projects are often executed with limited resources and determining the impact of such restrictions on the logical step schedule is important. This study investigates activityshifting strategies to find a near-optimal sequence of activities that guarantees consistent evolution of deliverables while resolving resource conflicts within the context of logical step schedules.
AFRIKAANSE OPSOMMING: Die logiese skedulering van aktiwiteite in ‘n ingenieursprojek steun swaar op die ondervinding en intuisie van die persone wat verantwoordelik is vir die skedule. In groot projekte is die kompleksiteit van die skedule veel hoër as die kapasiteit van die menslike intuisie, en sistematiese tegnieke word benodig om ‘n konsekwente volgorde van aktiwiteite te bereken. In hierdie studie word ‘n eenvoudige model van die ingenieursproses beskryf. Gebasseer op sommige relasies tussen komponente van die model, kan ‘n konsekwente volgorde van aktiwiteite bepaal word in die vorm van ‘n logiese stap-skedule. Die probleem van beperkte hulpbronne ontvang spesiale aandag. Ingenieursprojekte word dikwels uitgevoer met beperkte hulpbronne en dit is belangrik om die impak daarvan op die logiese stap-skedule te bepaal. Die studie ondersoek die gebruik van aktiwiteit-skuiwende strategieë om ‘n nabyoptimale volgorde van aktiwiteite te vind wat konsekwente ontwikkeling van die projekprodukte waarborg, terwyl hulpbron konflikte opgelos word binne die konteks van ‘n logiese stap-skedule.
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Jovanovska, Delfina. "Scheduling Time-Sensitive Tasks using a Combination of Proportional-Share and Priority Scheduling Algorithms." Ohio University / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1300244698.

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Teller, Justin Stevenson. "Scheduling Tasks on Heterogeneous Chip Multiprocessors with Reconfigurable Hardware." The Ohio State University, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=osu1211985748.

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Müller, Dirk, and Matthias Werner. "Improved Heuristics for Partitioned Multiprocessor Scheduling Based on Rate-Monotonic Small-Tasks." Universitätsbibliothek Chemnitz, 2012. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-80762.

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Partitioned preemptive EDF scheduling is very similar to bin packing, but there is a subtle difference. Estimating the probability of schedulability under a given total utilization has been studied empirically before. Here, we show an approach for closed-form formulae for the problem, starting with n = 3 tasks on m = 2 processors.
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Lowe, Timothy James. "Constraint techniques applied to teamworking tasks in clothing industry production." Thesis, Manchester Metropolitan University, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.389493.

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Nemati, Farhang. "Partitioned Scheduling of Real-Time Tasks on Multi-core Platforms." Licentiate thesis, Mälardalen University, School of Innovation, Design and Engineering, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-9595.

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In recent years multiprocessor architectures have become mainstream, and multi-core processors are found in products ranging from small portable cell phones to large computer servers. In parallel, research on real-time systems has mainly focused on traditional single-core processors. Hence, in order for real-time systems to fully leverage on the extra capacity offered by new multi-core processors, new design techniques, scheduling approaches, and real-time analysis methods have to be developed.

In the multi-core and multiprocessor domain there are mainly two scheduling approaches, global and partitioned scheduling. Under global scheduling each task can execute on any processor at any time while under partitioned scheduling tasks are statically allocated to processors and migration of tasks among processors is not allowed. Besides simplicity and efficiency of partitioned scheduling protocols, existing scheduling and synchronization methods developed for single-core processor platforms can more easily be extended to partitioned scheduling. This also simplifies migration of existing systems to multi-cores. An important issue related to partitioned scheduling is distribution of tasks among processors which is a bin-packing problem.

In this thesis we propose a partitioning framework for distributing tasks on the processors of multi-core platforms. Depending on the type of performance we desire to achieve, the framework may distribute a task set differently, e.g., in an application in which tasks process huge amounts of data the goal of the framework may be to decrease cache misses.Furthermore, we propose a blocking-aware partitioning heuristic algorithm to distribute tasks onto the processors of a multi-core architecture. The objective of the proposed algorithm is to decrease blocking overhead of tasks which reduces the total utilization and has the potential to reduce the number of required processors.Finally, we have implemented a tool to facilitate evaluation and comparison of different multiprocessor scheduling and synchronization approaches, as well as different partitioning heuristics. We have applied the tool in the evaluation of several partitioning heuristic algorithms, and the tool is flexible to which any new scheduling or synchronization protocol as well as any new partitioning heuristic can easily be added.

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Varghese, B., M. Alamgir Hossain, and Keshav P. Dahal. "Scheduling of tasks in multiprocessor system using hybrid genetic algorithms." Springer Verlag, 2007. http://hdl.handle.net/10454/2552.

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This paper presents an investigation into the optimal scheduling of realtime tasks of a multiprocessor system using hybrid genetic algorithms (GAs). A comparative study of heuristic approaches such as `Earliest Deadline First (EDF)¿ and `Shortest Computation Time First (SCTF)¿ and genetic algorithm is explored and demonstrated. The results of the simulation study using MATLAB is presented and discussed. Finally, conclusions are drawn from the results obtained that genetic algorithm can be used for scheduling of real-time tasks to meet deadlines, in turn to obtain high processor utilization.
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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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Books on the topic "Scheduling tasks"

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Jones, James Patton. NAS requirements checklist for job queuing/scheduling software. [Washington, D.C: National Aeronautics and Space Administration, 1996.

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Drozdowski, Maciej. Selected problems of scheduling tasks in multiprocessor computer systems. Poznań: Politechnika Poznańska, 1997.

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Livingston, David L. Investigation of automated task learning, decomposition and scheduling. Norfolk, Va: Old Dominion University Research Foundation, 1990.

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1941-, Lewis T. G., and Ali Hesham H, eds. Task scheduling in parallel and distributed systems. Englewood Cliffs, N.J: Prentice Hall, 1994.

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Wassenhove, Luk N. van. Single machine scheduling to minimize total late work. Fontainebleau: INSEAD, 1991.

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Hariri, A. M. A. Single machine scheduling to minimize total weighted late work. Fontainebleau: INSEAD, 1992.

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Brown, Gerald J. Dynamic task scheduling for the Knowledge Worker System. Champaign, IL: U.S. Army Construction Engineering Research Laboratory, 1996.

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Chen, Quan, and Minyi Guo. Task Scheduling for Multi-core and Parallel Architectures. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-6238-4.

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Wassenhove, Luk N. van. Exact and approximation algorithms for the operational fixed interval scheduling problem. Fontainebleau: INSEAD, 1992.

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Wassenhove, Luk N. van. A fully polynomial approximation scheme for scheduling a single machine to minimize total weighted late work. Fontainebleau: INSEAD, 1991.

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Book chapters on the topic "Scheduling tasks"

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Brucker, Peter. "Multiprocessor Tasks." In Scheduling Algorithms, 298–320. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-662-03612-9_11.

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Brucker, Peter. "Multiprocessor Tasks." In Scheduling Algorithms, 264–83. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/978-3-662-03088-2_10.

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Brucker, Peter. "Multiprocessor Tasks." In Scheduling Algorithms, 313–41. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-662-04550-3_11.

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Brucker, Peter. "Multiprocessor Tasks." In Scheduling Algorithms, 313–41. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24804-0_11.

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Thomas, Keir. "Scheduling Tasks." In Beginning Ubuntu Linux, 473–78. Berkeley, CA: Apress, 2006. http://dx.doi.org/10.1007/978-1-4302-0137-3_33.

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Thomas, Keir, and Jaime Sicam. "Scheduling Tasks." In Beginning Ubuntu Linux, 613–18. Berkeley, CA: Apress, 2008. http://dx.doi.org/10.1007/978-1-4302-0649-1_32.

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Raggi, Emilio, Keir Thomas, and Sander van Vugt. "Scheduling Tasks." In Beginning Ubuntu Linux, 485–92. Berkeley, CA: Apress, 2011. http://dx.doi.org/10.1007/978-1-4302-3627-6_24.

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Raggi, Emilio, Keir Thomas, Trevor Parsons, Andy Channelle, and Sander van Vugt. "Scheduling Tasks." In Beginning Ubuntu Linux, 481–88. Berkeley, CA: Apress, 2010. http://dx.doi.org/10.1007/978-1-4302-3040-3_24.

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Norman, Matthew. "Scheduling Tasks." In Essential ColdFusion fast, 233–39. London: Springer London, 2001. http://dx.doi.org/10.1007/978-1-4471-0333-2_16.

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Drozdowski, Maciej. "Parallel Tasks." In Scheduling for Parallel Processing, 87–208. London: Springer London, 2009. http://dx.doi.org/10.1007/978-1-84882-310-5_5.

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Conference papers on the topic "Scheduling tasks"

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Turek, John, Joel L. Wolf, Krishna R. Pattipati, and Philip S. Yu. "Scheduling parallelizable tasks." In the 1992 ACM SIGMETRICS joint international conference. New York, New York, USA: ACM Press, 1992. http://dx.doi.org/10.1145/133057.133111.

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Li, Haibing, and Hairong Zhao. "Scheduling Coupled-Tasks on a Single Machine." In 2007 IEEE Symposium on Computational Intelligence in Scheduling. IEEE, 2007. http://dx.doi.org/10.1109/scis.2007.367681.

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Li, Feng, Lin Zhang, and Yuanjun Laili. "Multi-Task Scheduling Based on QoS Evaluation in Cloud Manufacturing System." In ASME 2017 12th International Manufacturing Science and Engineering Conference collocated with the JSME/ASME 2017 6th International Conference on Materials and Processing. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/msec2017-2839.

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Cloud manufacturing (CMfg) mode provides an effective means to intensely utilize distributed resources and manufacturing capability for personalized production. Increasing personalized customization implies more and more heterogeneous tasks and hence more sorts of requirements for services. As the granularity of tasks vary with changing users and products, the solution (or scheme) of task scheduling should be different. In order to efficiently provide the most suitable solution for each kind of tasks, different scheduling ways should be adopted under different circumstances. In this paper, we study scheduling issues for heterogeneous tasks with variable granularity and present two kinds of optimal scheduling mode based on user-oriented comprehensive evaluation. Then different encoding schemes relied on the genetic algorithm are proposed according to different scheduling strategies.
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Lee, Jongwon, Sungyoung Lee, and Hyungill Kim. "Scheduling of hard aperiodic tasks." In the ACM SIGPLAN 1995 workshop. New York, New York, USA: ACM Press, 1995. http://dx.doi.org/10.1145/216636.216647.

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Guerout, Tom, and Mahdi Ben Alaya. "Autonomic Energy-Aware Tasks Scheduling." In 2013 IEEE 22nd International Workshop On Enabling Technologies: Infrastructure For Collaborative Enterprises (WETICE). IEEE, 2013. http://dx.doi.org/10.1109/wetice.2013.29.

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Turek, John, Joel L. Wolf, and Philip S. Yu. "Approximate algorithms scheduling parallelizable tasks." In the fourth annual ACM symposium. New York, New York, USA: ACM Press, 1992. http://dx.doi.org/10.1145/140901.141909.

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Orr, James, and Sanjoy Baruah. "Multiprocessor scheduling of elastic tasks." In the 27th International Conference. New York, New York, USA: ACM Press, 2019. http://dx.doi.org/10.1145/3356401.3356403.

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Wang, Lizhe, Jie Tao, Gregor von Laszewski, and Dan Chen. "Power Aware Scheduling for Parallel Tasks via Task Clustering." In 2010 IEEE 16th International Conference on Parallel and Distributed Systems (ICPADS). IEEE, 2010. http://dx.doi.org/10.1109/icpads.2010.128.

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Desimone, R. "The application of AI planning technology to crisis management tasks." In IEE Colloquium on Intelligent Planning and Scheduling Solutions. IEE, 1996. http://dx.doi.org/10.1049/ic:19961104.

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Sun, Hongyang, Redouane Elghazi, Ana Gainaru, Guillaume Aupy, and Padma Raghavan. "Scheduling Parallel Tasks under Multiple Resources: List Scheduling vs. Pack Scheduling." In 2018 IEEE International Parallel and Distributed Processing Symposium (IPDPS). IEEE, 2018. http://dx.doi.org/10.1109/ipdps.2018.00029.

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Reports on the topic "Scheduling tasks"

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Chintamaneni, Prasad R., Xiaoping Yuan, Satish K. Tripathi, and Ashok K. Agrawala. Scheduling Tasks in a Real-Time System. Fort Belvoir, VA: Defense Technical Information Center, February 1988. http://dx.doi.org/10.21236/ada198862.

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2

Andersson, Bjorn A., Dionisio de Niz, Hyoseung Kim, Mark Klein, and Ragunathan Rajkumar. Scheduling Constrained-Deadline Sporadic Parallel Tasks Considering Memory Contention. Fort Belvoir, VA: Defense Technical Information Center, October 2014. http://dx.doi.org/10.21236/ada610918.

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3

Olivier, Stephen. Design issues in the semantics and scheduling of asynchronous tasks. Office of Scientific and Technical Information (OSTI), July 2013. http://dx.doi.org/10.2172/1092998.

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Andersson, Bjorn A., and Gurulingesh Raravi. Scheduling Constrained-Deadline Parallel Tasks on Two-type Heterogeneous Multiprocessors. Fort Belvoir, VA: Defense Technical Information Center, January 2015. http://dx.doi.org/10.21236/ada614630.

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5

Jeffay, Kevin. Scheduling Sporadic Tasks with Shared Resources in Hard-Real-Time Systems. Fort Belvoir, VA: Defense Technical Information Center, November 1990. http://dx.doi.org/10.21236/ada242043.

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6

Miller, Allan. Nonpreemptive run-time scheduling issues on a multitasked, multiprogrammed multiprocessor with dependencies, bidimensional tasks, folding and dynamic graphs. Office of Scientific and Technical Information (OSTI), May 1987. http://dx.doi.org/10.2172/5546139.

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7

Amela, R., R. Badia, S. Böhm, R. Tosi, C. Soriano, and R. Rossi. D4.2 Profiling report of the partner’s tools, complete with performance suggestions. Scipedia, 2021. http://dx.doi.org/10.23967/exaqute.2021.2.023.

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Abstract:
This deliverable focuses on the proling activities developed in the project with the partner's applications. To perform this proling activities, a couple of benchmarks were dened in collaboration with WP5. The rst benchmark is an embarrassingly parallel benchmark that performs a read and then multiple writes of the same object, with the objective of stressing the memory and storage systems and evaluate the overhead when these reads and writes are performed in parallel. A second benchmark is dened based on the Continuation Multi Level Monte Carlo (C-MLMC) algorithm. While this algorithm is normally executed using multiple levels, for the proling and performance analysis objectives, the execution of a single level was enough since the forthcoming levels have similar performance characteristics. Additionally, while the simulation tasks can be executed as parallel (multi-threaded tasks), in the benchmark, single threaded tasks were executed to increase the number of simulations to be scheduled and stress the scheduling engines. A set of experiments based on these two benchmarks have been executed in the MareNostrum 4 supercomputer and using PyCOMPSs as underlying programming model and dynamic scheduler of the tasks involved in the executions. While the rst benchmark was executed several times in a single iteration, the second benchmark was executed in an iterative manner, with cycles of 1) Execution and trace generation; 2) Performance analysis; 3) Improvements. This had enabled to perform several improvements in the benchmark and in the scheduler of PyCOMPSs. The initial iterations focused on the C-MLMC structure itself, performing re-factors of the code to remove ne grain and sequential tasks and merging them in larger granularity tasks. The next iterations focused on improving the PyCOMPSs scheduler, removing existent bottlenecks and increasing its performance by making the scheduler a multithreaded engine. While the results can still be improved, we are satised with the results since the granularity of the simulations run in this evaluation step are much ner than the one that will be used for the real scenarios. The deliverable nishes with some recommendations that should be followed along the project in order to obtain good performance in the execution of the project codes.
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Dunlap, Greg, and Dipankar Dasgupta. An Administrative Tool for Distributed Security Task Scheduling. Fort Belvoir, VA: Defense Technical Information Center, January 2002. http://dx.doi.org/10.21236/ada405140.

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9

Swaminathan, Vishnu, and Krishnendu Chakrabarty. Real-Time Task Scheduling for Energy-Aware Embedded Systems. Fort Belvoir, VA: Defense Technical Information Center, January 2005. http://dx.doi.org/10.21236/ada439593.

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Swaminathan, Vishnu, and Krishnendu Chakrabarty. Investigating the Effect of Voltage-Switching on Low-Energy Task Scheduling in Hard Real-Time Systems. Fort Belvoir, VA: Defense Technical Information Center, January 2005. http://dx.doi.org/10.21236/ada440180.

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