Energy-Aware Scheduling in Real-Time Systems: An Analysis of Fair Share Scheduling and Priority-Driven Preemptive Scheduling
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 33093
Energy-Aware Scheduling in Real-Time Systems: An Analysis of Fair Share Scheduling and Priority-Driven Preemptive Scheduling

Authors: Su Xiaohan, Jin Chicheng, Liu Yijing, Burra Venkata Durga Kumar

Abstract:

Energy-aware scheduling in real-time systems aims to minimize energy consumption, but issues related to resource reservation and timing constraints remain challenges. This study focuses on analyzing two scheduling algorithms, Fair-Share Scheduling (FFS) and Priority-Driven Preemptive Scheduling (PDPS), for solving these issues and energy-aware scheduling in real-time systems. Based on research on both algorithms and the processes of solving two problems, it can be found that FFS ensures fair allocation of resources but needs to improve with an imbalanced system load. And PDPS prioritizes tasks based on criticality to meet timing constraints through preemption but relies heavily on task prioritization and may not be energy efficient. Therefore, improvements to both algorithms with energy-aware features will be proposed. Future work should focus on developing hybrid scheduling techniques that minimize energy consumption through intelligent task prioritization, resource allocation, and meeting time constraints.

Keywords: Energy-aware scheduling, fair-share scheduling, priority-driven preemptive scheduling, real-time systems, optimization, resource reservation, timing constraints.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 120

References:


[1] Adibhatla, B. et al. (2021) “Top Interview Questions for a Data Engineer Job Profile,” Analytics India Magazine (Preprint). Available at: https://analyticsindiamag.com/top-interview-questions-for-a-data-engineer-job-profile/.
[2] Burns, A. and Davis, R.H. (2017) “A Survey of Research into Mixed Criticality Systems,” ACM Computing Surveys, 50(6), pp. 1–37. Available at: https://doi.org/10.1145/3131347.
[3] Chen, Y., Li, Y., & Chen, J. (2018). Research on Real-Time Scheduling Algorithm Based on Energy Efficiency. Journal of Physics: Conference Series, 1053(1), 012131. Available at: https://doi.org/10.1088/1742-6596/1053/1/012131
[4] GeeksforGeeks (2021) “Fair share CPU scheduling,” GeeksforGeeks (Preprint). Available at: https://www.geeksforgeeks.org/fair-share-cpu-scheduling.
[5] Guo, Y., Wang, K., & Yang, J. (2018). Energy-aware real-time task scheduling for single-processor systems: A survey. Journal of Systems Architecture, 84, 1-14. Available at: https://www.ibm.com/docs/en/spectrum-lsf/10.1.0?topic=scheduling-understand-fair-share.
[6] Hosseinioun, P. et al. (2020) “A new energy-aware tasks scheduling approach in fog computing using a hybrid meta-heuristic algorithm,” Journal of Parallel and Distributed Computing, 143, pp. 88–96. Available at: https://doi.org/10.1016/j.jpdc.2020.04.008.
[7] IBM Documentation (no date). Available at: https://www.ibm.com/docs/en/spectrum-lsf/10.1.0?topic=scheduling-understand-fair-share.
[8] Kumar, S., & Singh, K. (2019). Energy-aware scheduling algorithms for real-time systems: A review. Journal of Ambient Intelligence and Humanized Computing, 10(5), 1787–1800. Available at: https://doi.org/10.1007/s12652-018-0798-3
[9] Liu, C. L., & Layland, J. W. (2019). Scheduling algorithms for multiprogramming in a hard real-time environment. Journal of the ACM, 20(1), 46–61. Available at: https://doi.org/10.1145/321738.321743
[10] Maghsoudlou, H., Afshar-Nadjafi, B. and Niaki, S.T.A. (2021) “A framework for a preemptive multi-skilled project scheduling problem with time-of-use energy tariffs,” Energy Systems, 12(2), pp. 431–458. Available at: https://doi.org/10.1007/s12667-019-00374-8.
[11] Pagani, S., Cardellini, V., Grassi, V., & Lo Presti, F. (2021). Fair share scheduling for cloud computing: A survey. Journal of Network and Computer Applications, 183, 102976. Available at: https://doi.org/10.1016/j.jnca.2021.102976
[12] Piao, X. and Park, M. (2015) “On-Line Dynamic Voltage Scaling for EDZL Scheduling on Symmetric Multiprocessor Real-Time Systems,” International Journal of Multimedia and Ubiquitous Engineering (Preprint). Available at: https://doi.org/10.14257/ijmue.2015.10.7.18.
[13] Praveenchandar, J. and Tamilarasi, A. (2022) “Retraction Note to Dynamic resource allocation with optimized task scheduling and improved power management in cloud computing,” Journal of Ambient Intelligence and Humanized Computing, 14(S1), p. 115. Available at: https://doi.org/10.1007/s12652-022-03970-2.
[14] Qin, Y. et al. (2022) “Dynamic voltage scaling based energy-minimized partial task offloading in fog networks,” Wireless Networks, 28(8), pp. 3337–3347. Available at: https://doi.org/10.1007/s11276-022-03052-3.
[15] Rahman, M. M., Islam, M. R., & Islam, M. M. (2020). A Survey of Real-Time Scheduling Algorithms for Multiprocessor Systems. In Proceedings of the International Conference on Computer, Communication, Chemical, Material and Electronic Engineering (pp. 124–131). Available at: https://doi.org/10.1145/3388231.3388245
[16] Rubaiee, S. and Yildirim, M. (2019) “An energy-aware multiobjective ant colony algorithm to minimize total completion time and energy cost on a single-machine preemptive scheduling,” Elsevier, 127, pp. 240–252. Available at: https://doi.org/10.1016/j.cie.2018.12.020.
[17] Sangaiah, A.K. et al. (2021) “Energy-Aware Geographic Routing for Real-Time Workforce Monitoring in Industrial Informatics,” IEEE Internet of Things Journal, 8(12), pp. 9753–9762. Available at: https://doi.org/10.1109/jiot.2021.3056419.
[18] Utrera, G., Farreras, M. and Fornes, J. (2019) “Task Packing: Efficient task scheduling in unbalanced parallel programs to maximize CPU utilization,” Journal of Parallel and Distributed Computing, 134, pp. 37–49. Available at: https://doi.org/10.1016/j.jpdc.2019.08.003.
[19] Vaghela, F.N. and Serasiya, S. (2022) “Fair Share Management for Resource Allocation in Multi Cloud Environment,” International Journal of Progressive Research in Engineering Management and Science, 02(05), pp. 32–35.
[20] Zhang, Y. et al. (2020) “Interval optimization based coordination scheduling of gas–electricity coupled system considering wind power uncertainty, dynamic process of natural gas flow and demand response management,” Energy Reports, 6, pp. 216–227. Available at: https://doi.org/10.1016/j.egyr.2019.12.013.
[21] Zhang, Y. (2023) “Energy efficient non-preemptive scheduling of imprecise mixed-criticality real-time tasks,” Sustainable Computing: Informatics and Systems, 37, p. 100840. Available at: https://doi.org/10.1016/j.suscom.2022.100840.
[22] Zhou, J. et al. (2020) “Security-Critical Energy-Aware Task Scheduling for Heterogeneous Real-Time MPSoCs in IoT,” IEEE Transactions on Services Computing, 13(4), pp. 745–758. Available at: https://doi.org/10.1109/tsc.2019.2963301.