Abstract
Real-time embedded systems have tight time constraints on the system response to external events. They are being used in many mission critical applications like avionics, industrial control systems, etc. Both hardware and software components might have an impact on the real-time system’s overall performance. For the most part, hardware components are designed with large-scale production in mind. Memory management and key kernel components like the scheduler are only two examples of software that interacts with an operating system. The problem of real-time scheduling extends range of algorithms from simple uniprocessor to highly complex multiprocessor scheduling algorithms. Over the last decade, in order to meet the demands of ever-increasing performance from the commercial market and faced with the fundamental performance limits which could not be achieved on a single-core processor due to clock speed ceiling, semiconductor manufacturers transitioned to multicore processor architectures to achieve better performance needed for real-time embedded systems. Further online (dynamic) scheduling algorithms are more flexible than offline (static) algorithms. It has been suggested in this research to survey a variety of dynamic scheduling techniques for real-time embedded systems. The characteristics and limitations of real-time activities are the subject of our research. The choice of scheduling algorithm depends on its ability to fulfill task time restrictions demanded by-product requirements.
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References
Cho S, Lee SK, Anh S, Lin KJ (2002) Efficient real-time scheduling algorithms for multiprocessor systems. IEICE Trans Commun E85B:2859–2867
Bertogna M, Cirinei M, Lipari G (2005) Improved schedulability analysis of EDF on multiprocessor platforms ECRTS, pp 209–218
Kato S, Ishikawa Y (2009) Gang EDF scheduling of parallel task systems RTSS
Bertogna M, Baruah S (2010) Limited preemption EDF scheduling of sporadic task systems IEEE Trans. Ind Informat 6(4):579–591
He X-B (2010) An improved EDF scheduling algorithm based on fuzzy inference being suitable for embedded soft real-time systems in the uncertain environments. In: Proceedings 2nd IEEE international conference on advanced computer control, vol 5, pp 588–592
Thakor D, Shah A (2011) D_EDF: An efficient scheduling algorithm for real-time multiprocessor system. In: World Congress on information and communication technologies
Rehaiem G, Gharsellaoui H, Ben Ahmed S (2016) A neural networks based approach for the real-time scheduling of reconfigurable embedded systems with minimization of power consumption. In: IEEE/ACIS 15th international conference on computer and information science (ICIS), pp 1–6
Cho H, Ravindran B, Jensen ED (2006) An optimal realtime scheduling algorithm for multiprocessors RTSS, pp 101–110
Andersson B, Tovar E (2006) Multiprocessor scheduling with few preemptions RTCSA, pp 322–334
Anderson JH, Calandrino JM (2006) Parallel real-time task scheduling on multicore platforms RTSS
K Baital A Chakrabarti A Chakrabarti N Sharma V Balas 2016 An efficient dynamic scheduling of tasks for multicore real-time systems Advances in computing applications Springer Singapore 31 47
Baital K, Member IEEE, Chakrabarti A, Senior Member IEEE (2019) Dynamic scheduling of real-time tasks in heterogeneous multicore systems IEEE embedded systems letters, vol 11, no 1, pp 29–32
Yomsi PM, Sorel Y (2007) Extending rate monotonic analysis with exact cost of preemptions for hard real-time systems. In: Proceeding euromicro conference real-time systems, pp 280–290
Calandrino JM, Baumberger D, Li T, Hahn S, Anderson JH (2007) Soft real-time scheduling on performance asymmetric multicore platforms RTAS ‘07
Levin G, Funk S, Sadowski C, Pye I, Brandt S (2010) DP-Fair: a simple model for understanding optimal multiprocessor scheduling. IEEE 22nd euromicro conference on real-time systems
Saifullah A, Agrawal K, Lu C (2011) Multi-core real—time scheduling for generalized parallel task models IEEE real-time systems
Lee J, Easwaran A, Shin I (2011) Maximizing contention-free executions in multiprocessor scheduling. IEEE Real-Time Technol Appl Symp (RTAS) 235–244
Liu X, Ding Y-F, Zhu G-H (2013) Hardware scheduler of real-time operating system advanced science and technology letters (MulGraB 2013) 31
Mohammad P, Parvar ME, Saeed S (2008) A starvation free IMLFQ scheduling algorithm based on neural network. Int J Comput Intell Res 4(1):27–36
Chouhan D, Dilip Kumar SM, Ajay JA (2013) A MLFQ scheduling technique using M/M/c queues for grid computing. Int J Comput Sci 10(2)
Rinku DR, Asha Rani M (2020) Reinforcement learning based multi core scheduling (RLBMCS) for real time systems. Int J Electric Comput Eng (IJECE) 10(2):1805–1813
Levin G, Funk S, Sadowski C, Pye I, Brandt S (2010) DP-Fair: a simple model for understanding optimal multiprocessor scheduling. IEEE 22nd euromicro conference on real-time systems
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Rinku, D.R., Asha Rani, M., Suhruth Krishna, Y. (2023). Exploring the Scheduling Techniques for the RTOS. In: Tuba, M., Akashe, S., Joshi, A. (eds) ICT Infrastructure and Computing. Lecture Notes in Networks and Systems, vol 520. Springer, Singapore. https://doi.org/10.1007/978-981-19-5331-6_2
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DOI: https://doi.org/10.1007/978-981-19-5331-6_2
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