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Journal articles on the topic 'REAL-TIME TASKS'

Author: Grafiati

Published: 11 September 2023

Last updated: 25 July 2025

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1

Udvanshi, Pankaj. "Scheduling of Real Time Tasks." IOSR Journal of Engineering 03, no. 6 (2013): 44–58. http://dx.doi.org/10.9790/3021-03624458.

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2

Oh, Y., and S. H. Son. "Scheduling Real-Time Tasks for Dependability." Journal of the Operational Research Society 48, no. 6 (1997): 629. http://dx.doi.org/10.2307/3010227.

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3

Shin, Kang G., Tein-Hsiang Lin, and Yann-Hang Lee. "Optimal Checkpointing of Real-Time Tasks." IEEE Transactions on Computers C-36, no. 11 (1987): 1328–41. http://dx.doi.org/10.1109/tc.1987.5009472.

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4

Behrouzian, Amir, Hadi Alizadeh Ara, Marc Geilen, Dip Goswami, and Twan Basten. "Firmness Analysis of Real-time Tasks." ACM Transactions on Embedded Computing Systems 19, no. 4 (2020): 1–24. http://dx.doi.org/10.1145/3398328.

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5

Oh, Y., and S. H. Son. "Scheduling real-time tasks for dependability." Journal of the Operational Research Society 48, no. 6 (1997): 629–39. http://dx.doi.org/10.1057/palgrave.jors.2600413.

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6

Oh, Y., and S. H. Son. "Scheduling real-time tasks for dependability." Journal of the Operational Research Society 48, no. 6 (1997): 629–39. http://dx.doi.org/10.1038/sj.jors.2600413.

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7

Moron, Celio Estevan, and Hussein Zedan. "On guaranteeing hard real-time tasks." Microprocessing and Microprogramming 38, no. 1-5 (1993): 485–90. http://dx.doi.org/10.1016/0165-6074(93)90185-n.

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8

Schwan, K., and H. Zhou. "Dynamic scheduling of hard real-time tasks and real-time threads." IEEE Transactions on Software Engineering 18, no. 8 (1992): 736–48. http://dx.doi.org/10.1109/32.153383.

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9

Pandey, Ankush. "A Real Time Approach to Compute Distance between Objects for Automated Tasks." Journal of Advanced Research in Dynamical and Control Systems 12, SP8 (2020): 968–83. http://dx.doi.org/10.5373/jardcs/v12sp8/20202602.

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10

Kin, Seng Lee. "Real-time Clock Periodic Tasks Concurrent Executor." International Journal of Computer Science and Information Technology Research 11, no. 4 (2023): 1–4. https://doi.org/10.5281/zenodo.8402201.

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<strong>Abstract:</strong> Real-time clock periodic tasks concurrent executor program is being written to execute multiple timed periodic tasks concurrently using modern CPU multithreading and multicores feature at specific RTC periodically. By using RTC as an absolute reference, we can analyze the status of multiple isolated devices on selected time instances to know about overall system status or each individual systems status. Multithreading program able to execute multiple tasks concurrently, making RTC periodic tasks possible. While single threaded sequential program only run multiple tas

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11

Ordinez, Leo, David Donari, Rodrigo Santos, and Javier Orozco. "Time is not Enough: Dealing with Behavior in Real-Time Systems." JUCS - Journal of Universal Computer Science 17, no. (11) (2011): 1572–604. https://doi.org/10.3217/jucs-017-11-1572.

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In this paper, the Behavioral Importance Priority Server (BIPS) algorithm is proposed to schedule sets of hard/soft real-time tasks. The mechanism postpones or advances the execution of the next instance of a task according to the value obtained from a function properly associated to the behavior of the task; as a consequence, there is a flexible adaptation of the bandwidth required by each server. A synchronization method is introduced to prevent deadlocks and priority inversions in the case of sets of tasks sharing resources along with the necessary and sufficient conditions for the schedula

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12

Palencia, J. C., and M. González Harbour. "Response time analysis of EDF distributed real-time systems." Journal of Embedded Computing 1, no. 2 (2005): 225–37. https://doi.org/10.3233/emc-2005-00017.

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Offset-based response time analysis of tasks scheduled with fixed priorities has demonstrated to be a powerful tool to analyze many task models with different kinds of timing constraints, like regular periodic tasks, suspending tasks, distributed systems, tasks with varying priorities, multiframe models, etc. Offset-based analysis techniques are capable of performing a global schedulability analysis in distributed systems, as opposed to the less efficient techniques that consider each processing or communication resource as independent. In this paper we extend the offset-based schedulability a

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13

Зинченко, Сергей Валериевич, and Валерий Петрович Зинченко. "THE SCHEDULING TASKS IN REAL-TIME SYSTEMS." Information systems, mechanics and control, no. 17 (December 29, 2017): 113–23. http://dx.doi.org/10.20535/2219-3804172017123927.

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14

Allen, R. K., A. Burns, and A. J. Wellings. "Sporadic tasks in hard real-time systems." ACM SIGAda Ada Letters XV, no. 5 (1995): 46–51. http://dx.doi.org/10.1145/221309.221313.

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15

Weiping Zhu. "Allocating Soft Real-Time Tasks on Cluster." SIMULATION 77, no. 5-6 (2001): 219–29. http://dx.doi.org/10.1177/003754970107700507.

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16

Mok, A. K., and D. Chen. "A multiframe model for real-time tasks." IEEE Transactions on Software Engineering 23, no. 10 (1997): 635–45. http://dx.doi.org/10.1109/32.637146.

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17

Chantem, T., Xiaobo Sharon Hu, and M. D. Lemmon. "Generalized Elastic Scheduling for Real-Time Tasks." IEEE Transactions on Computers 58, no. 4 (2009): 480–95. http://dx.doi.org/10.1109/tc.2008.175.

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18

Wedde, Horst F., Sabine Böhm, and Wolfgang Freund. "Real-time Transactions Need Their Constituting Tasks." IFAC Proceedings Volumes 34, no. 22 (2001): 277–82. http://dx.doi.org/10.1016/s1474-6670(17)32951-8.

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19

Altenbernd, P., and C. Ditze. "Allocation of Periodic Hard Real-Time Tasks." IFAC Proceedings Volumes 29, no. 6 (1996): 197–204. http://dx.doi.org/10.1016/s1474-6670(17)43764-5.

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20

Pospischil, G., P. Puschner, A. Vrchoticky, and R. Zainlinger. "Developing real-time tasks with predictable timing." IEEE Software 9, no. 5 (1992): 35–44. http://dx.doi.org/10.1109/52.156895.

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21

LEE, W. Y. "Optimal Scheduling for Real-Time Parallel Tasks." IEICE Transactions on Information and Systems E89-D, no. 6 (2006): 1962–66. http://dx.doi.org/10.1093/ietisy/e89-d.6.1962.

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22

Hong, K. S., and J. Y. T. Leung. "On-line scheduling of real-time tasks." IEEE Transactions on Computers 41, no. 10 (1992): 1326–31. http://dx.doi.org/10.1109/12.166609.

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23

Gerber, R., W. Pugh, and M. Saksena. "Parametric dispatching of hard real-time tasks." IEEE Transactions on Computers 44, no. 3 (1995): 471–79. http://dx.doi.org/10.1109/12.372041.

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24

Jackson, L. E., and G. N. Rouskas. "Deterministic preemptive scheduling of real-time tasks." Computer 35, no. 5 (2002): 72–79. http://dx.doi.org/10.1109/mc.2002.999778.

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25

Reimann, Sven, Wei Wu, and Steven Liu. "Real-Time Scheduling of PI Control Tasks." IEEE Transactions on Control Systems Technology 24, no. 3 (2016): 1118–25. http://dx.doi.org/10.1109/tcst.2015.2464304.

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26

Hwang, Chi‐Pan, and Cheng‐Seen Ho. "Hardware design of a real‐time Petri net model for real‐time tasks." Journal of the Chinese Institute of Engineers 18, no. 4 (1995): 481–92. http://dx.doi.org/10.1080/02533839.1995.9677713.

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27

Patel, Dinkan, and Anjuman Ranavadiya. "REVIEW OF TASK SCHEDULING METHODS FOR REAL TIME TASKS IN CLOUD ENVIRONMENT." International Journal of Engineering Technologies and Management Research 5, no. 1 (2020): 85–89. http://dx.doi.org/10.29121/ijetmr.v5.i1.2018.50.

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Cloud Computing is a type of Internet model that enables convenient, on-demand resources that can be used rapidly and with minimum effort. Cloud Computing can be IaaS, PaaS or SaaS. Scheduling of these tasks is important so that resources can be utilized efficiently with minimum time which in turn gives better performance. Real time tasks require dynamic scheduling as tasks cannot be known in advance as in static scheduling approach. There are different task scheduling algorithms that can be utilized to increase the performance in real time and performing these on virtual machines can prove to

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28

Dinkan, Patel, and Ranavadiya Anjuman. "REVIEW OF TASK SCHEDULING METHODS FOR REAL TIME TASKS IN CLOUD ENVIRONMENT." International Journal of Engineering Technologies and Management Research 5, no. 1 (2018): 85–89. https://doi.org/10.5281/zenodo.1171526.

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<strong><em>Cloud Computing is a type of Internet model that enables convenient, on-demand resources that can be used rapidly and with minimum effort. Cloud Computing can be IaaS, PaaS or SaaS. Scheduling of these tasks is important so that resources can be utilized efficiently with minimum time which in turn gives better performance. Real time tasks require dynamic scheduling as tasks cannot be known in advance as in static scheduling approach. There are different task scheduling algorithms that can be utilized to increase the performance in real time and performing these on virtual machines

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29

Wu, Jian Lang, Jing Kai Shi, and Yi Bin Wang. "Analysis on Scheduling Algorithms of Real-Time Hybrid Tasks." Applied Mechanics and Materials 644-650 (September 2014): 2253–57. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.2253.

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In real-time systems, periodic tasks and aperiodic tasks exist simultaneously. In a uniprocessor system, mainly there are Deferrable Server algorithm (DS) [1], Slack Stealing algorithm (SSA) [2] and their extended version for software/hardware hybrid real-time task scheduling. DS algorithm sets a high priority periodic task server to provide services for aperiodic tasks, while SSA algorithm computes tasks unoccupied time offline, and then schedule aperiodic tasks during the unoccupied period. The two algorithms are both proposed for soft real-time tasks, reducing the response time of the real-

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30

Lemmel, Julian, and Radu Grosu. "Real-Time Recurrent Reinforcement Learning." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 17 (2025): 18189–97. https://doi.org/10.1609/aaai.v39i17.34001.

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We introduce a biologically plausible RL framework for solving tasks in partially observable Markov decision processes (POMDPs). The proposed algorithm combines three integral parts: (1) A Meta-RL architecture, resembling the mammalian basal ganglia; (2) A biologically plausible reinforcement learning algorithm, exploiting temporal difference learning and eligibility traces to train the policy and the value-function; (3) An online automatic differentiation algorithm for computing the gradients with respect to parameters of a shared recurrent network backbone. Our experimental results show that

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31

LEE, Wan Yeon, Kyungwoo LEE, Kyong Hoon KIM, and Young Woong KO. "Processor-Minimum Scheduling of Real-Time Parallel Tasks." IEICE Transactions on Information and Systems E92-D, no. 4 (2009): 723–26. http://dx.doi.org/10.1587/transinf.e92.d.723.

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32

Gong, Min-Sik, Gun-Jae Jeong, Ye-Jin Song, Myoung-Jo Jung, Moon-Haeng Cho, and Cheol-Hoon Lee. "Power-Aware Scheduling for Mixed Real-Time Tasks." Journal of the Korea Contents Association 7, no. 1 (2007): 83–93. http://dx.doi.org/10.5392/jkca.2007.7.1.083.

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33

Hamidzadeb, B., and Y. Atif. "Dynamic scheduling of real-time tasks, by assignment." IEEE Concurrency 6, no. 4 (1998): 14–25. http://dx.doi.org/10.1109/4434.736402.

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34

Zhong, Xiliang, and Cheng-Zhong Xu. "System-wide energy minimization for real-time tasks." ACM Transactions on Embedded Computing Systems 7, no. 3 (2008): 1–24. http://dx.doi.org/10.1145/1347375.1347381.

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35

Lei, Zhenyang, Xiangdong Lei, and Jun Long. "Real-Time Scheduling Parallel Tasks on Multicore Platforms." Journal of Physics: Conference Series 1673 (November 2020): 012002. http://dx.doi.org/10.1088/1742-6596/1673/1/012002.

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36

Cheng, Albert Mo Kim, and Chen Feng. "Predictive thermal management for hard real-time tasks." ACM SIGBED Review 3, no. 1 (2006): 35–40. http://dx.doi.org/10.1145/1279711.1279719.

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37

Dertouzos, M. L., and A. K. Mok. "Multiprocessor online scheduling of hard-real-time tasks." IEEE Transactions on Software Engineering 15, no. 12 (1989): 1497–506. http://dx.doi.org/10.1109/32.58762.

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38

Aydin, H., R. Melhem, D. Mosse, and P. Mejia-Alvarez. "Power-aware scheduling for periodic real-time tasks." IEEE Transactions on Computers 53, no. 5 (2004): 584–600. http://dx.doi.org/10.1109/tc.2004.1275298.

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39

Levitin, Gregory, Liudong Xing, and Hanoch Ben-Haim. "Optimizing software rejuvenation policy for real time tasks." Reliability Engineering & System Safety 176 (August 2018): 202–8. http://dx.doi.org/10.1016/j.ress.2018.04.010.

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40

Bhuiyan, Ashikahmed, Zhishan Guo, Abusayeed Saifullah, Nan Guan, and Haoyi Xiong. "Energy-Efficient Real-Time Scheduling of DAG Tasks." ACM Transactions on Embedded Computing Systems 17, no. 5 (2018): 1–25. http://dx.doi.org/10.1145/3241049.

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41

Park, Moonju, and Yookun Cho. "Feasibility analysis of hard real-time periodic tasks." Journal of Systems and Software 73, no. 1 (2004): 89–100. http://dx.doi.org/10.1016/s0164-1212(03)00236-x.

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42

Eker, Johan, Per Hagander, and Karl-Erik Årzén. "A feedback scheduler for real-time controller tasks." Control Engineering Practice 8, no. 12 (2000): 1369–78. http://dx.doi.org/10.1016/s0967-0661(00)00086-1.

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43

Wu, Yue, Li-san Tang, and Hong-bin Yang. "Overload problem research on aperiodic real-time tasks." Journal of Shanghai University (English Edition) 13, no. 2 (2009): 136–41. http://dx.doi.org/10.1007/s11741-009-0209-2.

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44

Drozdowski, Maciej. "Real-time scheduling of linear speedup parallel tasks." Information Processing Letters 57, no. 1 (1996): 35–40. http://dx.doi.org/10.1016/0020-0190(95)00174-3.

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45

Di Natale, M., and J. A. Stankovic. "Scheduling distributed real-time tasks with minimum jitter." IEEE Transactions on Computers 49, no. 4 (2000): 303–16. http://dx.doi.org/10.1109/12.844344.

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46

Ripoll, Ismael, Alfons Crespo, and Aloysius K. Mok. "Improvement in feasibility testing for real-time tasks." Real-Time Systems 11, no. 1 (1996): 19–39. http://dx.doi.org/10.1007/bf00365519.

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47

Schmid, Ulrich. "Static priority scheduling of aperiodic real-time tasks." Random Structures and Algorithms 10, no. 1-2 (1997): 257–303. http://dx.doi.org/10.1002/(sici)1098-2418(199701/03)10:1/2<257::aid-rsa13>3.0.co;2-5.

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48

Li, Jing, Zheng Luo, David Ferry, Kunal Agrawal, Christopher Gill, and Chenyang Lu. "Global EDF scheduling for parallel real-time tasks." Real-Time Systems 51, no. 4 (2014): 395–439. http://dx.doi.org/10.1007/s11241-014-9213-9.

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49

Singh, Abhishek, Pontus Ekberg, and Sanjoy Baruah. "Uniprocessor scheduling of real-time synchronous dataflow tasks." Real-Time Systems 55, no. 1 (2018): 1–31. http://dx.doi.org/10.1007/s11241-018-9310-2.

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50

Gharbi, Atef, Hamza Gharsellaoui, and Mohamed Khalgui. "Real-Time Reconfigurations of Embedded Control Systems." International Journal of System Dynamics Applications 5, no. 3 (2016): 71–93. http://dx.doi.org/10.4018/ijsda.2016070104.

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This paper deals with the study of the reconfiguration of embedded control systems with safety following component-based approaches from the functional level to the operational level. The authors define the architecture of the Reconfiguration Agent which is modelled by nested state machines to apply local reconfigurations. They propose in this journal paper technical solutions to implement the whole agent-based architecture, by defining UML meta-models for both Control Components and also agents. To guarantee safety reconfigurations of tasks at run-time, they define service and reconfiguration

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