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Journal articles on the topic 'REAL-TIME TASKS'

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Published: 11 September 2023

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1

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

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2

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 (July 30, 2020): 968–83. http://dx.doi.org/10.5373/jardcs/v12sp8/20202602.

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3

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

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4

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

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5

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 (July 16, 2020): 1–24. http://dx.doi.org/10.1145/3398328.

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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 (June 1997): 629–39. http://dx.doi.org/10.1057/palgrave.jors.2600413.

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7

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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8

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

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9

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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10

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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11

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

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12

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

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13

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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14

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

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15

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

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16

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

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17

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

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18

LEE, W. Y. "Optimal Scheduling for Real-Time Parallel Tasks." IEICE Transactions on Information and Systems E89-D, no. 6 (June 1, 2006): 1962–66. http://dx.doi.org/10.1093/ietisy/e89-d.6.1962.

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19

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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20

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

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21

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

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22

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

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23

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 (June 1995): 481–92. http://dx.doi.org/10.1080/02533839.1995.9677713.

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24

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 (February 7, 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 be useful. Here a review of various task scheduling algorithms is done which can be used to perform the task and allocate resources so that performance can be increased.
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25

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-time tasks, but cannot guarantee that these aperiodic real-time tasks received can meet deadlines. In this paper, through combination of DS algorithm and EDF (Earliest Deadline First) algorithm [6], a new algorithm called DS-EDF is introduced, which can scheduling hard real-time aperiodic tasks on the DS server. This algorithm is not only suitable for uniprocessor systems, but also has the ability to extend to multiprocessor systems.
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26

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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27

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 (January 28, 2007): 83–93. http://dx.doi.org/10.5392/jkca.2007.7.1.083.

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28

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

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29

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

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30

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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31

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

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32

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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33

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 (May 2004): 584–600. http://dx.doi.org/10.1109/tc.2004.1275298.

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34

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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35

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 (November 22, 2018): 1–25. http://dx.doi.org/10.1145/3241049.

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36

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

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37

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

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38

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 (April 2009): 136–41. http://dx.doi.org/10.1007/s11741-009-0209-2.

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39

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

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40

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

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41

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

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42

Schmid, Ulrich. "Static priority scheduling of aperiodic real-time tasks." Random Structures and Algorithms 10, no. 1-2 (January 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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43

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 (October 28, 2014): 395–439. http://dx.doi.org/10.1007/s11241-014-9213-9.

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44

Singh, Abhishek, Pontus Ekberg, and Sanjoy Baruah. "Uniprocessor scheduling of real-time synchronous dataflow tasks." Real-Time Systems 55, no. 1 (May 21, 2018): 1–31. http://dx.doi.org/10.1007/s11241-018-9310-2.

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45

Gharbi, Atef, Hamza Gharsellaoui, and Mohamed Khalgui. "Real-Time Reconfigurations of Embedded Control Systems." International Journal of System Dynamics Applications 5, no. 3 (July 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 processes for tasks and use the semaphore concept to ensure safety mutual exclusions. As a method to ensure the scheduling between periodic tasks with precedence and mutual exclusion constraints, the authors apply the priority ceiling protocol.
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46

Missimer, Katherine, Manos Athanassoulis, and Richard West. "Telomere: Real-Time NAND Flash Storage." ACM Transactions on Embedded Computing Systems 21, no. 1 (January 31, 2022): 1–24. http://dx.doi.org/10.1145/3479157.

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Modern solid-state disks achieve high data transfer rates due to their massive internal parallelism. However, out-of-place updates for flash memory incur garbage collection costs when valid data needs to be copied during space reclamation. The root cause of this extra cost is that solid-state disks are not always able to accurately determine data lifetime and group together data that expires before the space needs to be reclaimed. Real-time systems found in autonomous vehicles, industrial control systems, and assembly-line robots store data from hundreds of sensors and often have predictable data lifetimes. These systems require guaranteed high storage bandwidth for read and write operations by mission-critical real-time tasks. In this article, we depart from the traditional block device interface to guarantee the high throughput needed to process large volumes of data. Using data lifetime information from the application layer, our proposed real-time design, called Telomere , is able to intelligently lay out data in NAND flash memory and eliminate valid page copies during garbage collection. Telomere’s real-time admission control is able to guarantee tasks their required read and write operations within their periods. Under randomly generated tasksets containing 500 tasks, Telomere achieves 30% higher throughput with a 5% storage cost compared to pre-existing techniques.
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47

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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48

Fu, Chun Yan, Hong Zhou, Mao Song Ge, Xiao Qu, and Yong Li Wang. "A Quasi-Real-Time MapReduce Schedule Algorithm." Advanced Materials Research 694-697 (May 2013): 2458–61. http://dx.doi.org/10.4028/www.scientific.net/amr.694-697.2458.

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In this paper, we extend and rewrite MapReduce dispatcher and its quasi-real-time schedule algorithm to support operation scheduling in time-limited. MapReduce dispatcher has an evaluation of completion time of tasks in dependence of rate of progress of tasks at hand, and allocated resource dynamically to every task when they are running. Experimental investigation shows that, the algorithm increase the resource utilization of the MapReduce system, and the goals of quasi-real-time MapReduce schedule has been achieved.
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49

Da-Ren, Chen, Chen Young-Long, and Chen You-Shyang. "Time and Energy Efficient DVS Scheduling for Real-Time Pinwheel Tasks." Journal of Applied Research and Technology 12, no. 6 (December 2014): 1025–39. http://dx.doi.org/10.1016/s1665-6423(14)71663-3.

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50

Koh, Jae-Hwan, and Byoung-Wook Choi. "On Benchmarking of Real-time Mechanisms in Various Periodic Tasks for Real-time Embedded Linux." Journal of Korea Robotics Society 7, no. 4 (November 30, 2012): 292–98. http://dx.doi.org/10.7746/jkros.2012.7.4.292.

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