A task scheduling model for multi-CPU and multi-hard disk drive in soft real-time systems

A task scheduling model for multi-CPU and multi-hard disk drive in soft real-time systems

Автор: Zeynab Mohseni, Vahdaneh Kiani, Amir Masoud Rahmani

Журнал: International Journal of Information Technology and Computer Science @ijitcs

Статья в выпуске: 1 Vol. 11, 2019 года.

Бесплатный доступ

In recent years, by increasing CPU and I/O devices demands, running multiple tasks simultaneously becomes a crucial issue. This paper presents a new task scheduling algorithm for multi-CPU and multi-Hard Disk Drive (HDD) in soft Real-Time (RT) systems, which reduces the number of missed tasks. The aim of this paper is to execute more parallel tasks by considering an efficient trade-off between energy consumption and total execution time. For study purposes, we analyzed the proposed scheduling algorithm, named HCS (Hard disk drive and CPU Scheduling) in terms of the task set utilization, the total execution time, the average waiting time and the number of missed tasks from their deadlines. The results show that HCS algorithm improves the above mentioned criteria compared to the HCS_UE (Hard disk drive and CPU Scheduling _Unchanged Execution time) algorithm.

Еще

Non-preemptive task scheduling, soft real-time system, Task parallelism, Multi-CPU, Multi-device

Короткий адрес: https://sciup.org/15016325

IDR: 15016325   |   DOI: 10.5815/ijitcs.2019.01.01

Список литературы A task scheduling model for multi-CPU and multi-hard disk drive in soft real-time systems

  • Kamga, C.M., 2012. CPU Frequency Emulation Based on DVFS, In: Utility and Cloud Computing (UCC), pp: 367– 374. DOI: 10.1109/UCC.2012.34.
  • Cho, S.J., S.H. Yun and J.W. Jean, 2015. A power saving DVFS algorithm based on Operational Intensity for embedded systems, In: IEICE Electronics Express, vol. 12, no. 3, Jan., pp: 1–7. DOI: http://doi.org/10.1587/elex.12.20141128.
  • Da-Ren, Ch., Ch. Young-Long and Ch. You-Shyang, 2014. Time and Energy Efficient DVS Scheduling for Real-Time Pinwheel Tasks. In: Journal of Applied Research and Technology, vol. 12, issue. 6, Dec., pp: 1025–1039. DOI: 10.1016/S1665-6423(14)71663-3.
  • Tang, Z., L. Qi, Z. Cheng, K. Li, S.U. Khan and K. Li, 2015. An Energy-Efficient Task Scheduling Algorithm in DVFS-enabled Cloud Environment. In: Journal of Grid Computing, April. DOI: 10.1007/s10723-015-9334-y.
  • Babaii, N., Rizvandi, J. Taheri and A.Y. Zomaya, 2011. Some observations on optimal frequency selection in DVFS-based energy consumption minimization. In: Journal of Parallel and Distributed Computing, vol. 71, issue 8, pp: 1154–1164. DOI: 10.1016/j.jpdc.2011.01.004.
  • Laszewski, G.V., L. Wang, A.J. Younge and X. He, 2009. Power-Aware Scheduling of Virtual Machines in DVFS-enabled Clusters. In: Cluster Computing and Workshops, pp: 1–10. DOI: 10.1109/CLUSTR.2009.5289182.
  • Tchamgoue, G.M., J. Seo, K.H. Kim and Y.K. Jun, 2015. Compositional Power-Aware Real-Time Scheduling with Discrete Frequency Levels. In: Journal of Systems Architecture. DOI:10.1016/j.sysarc.2015.05.003.
  • Wu, J., 2015. Energy-Efficient Scheduling of Real-Time Tasks with Shared Resources. In: Future Generation Computer Systems, May. DOI: 10.1016/j.future.2015.05.012.
  • Zhu, X., C. He, K. Li and X. Qin, 2012. Adaptive energy-efficient scheduling for real-time tasks on DVS-enabled heterogeneous clusters. In: Journal of Parallel and Distributed Computing, pp: 751–763. DOI: 10.1016/j.jpdc.2012.03.005.
  • Pedram, M. and K. Choi, 2005. Dynamic Voltage and Frequency Scaling for Energy-Efficient System Design. In: the Association for Computing Machinery. ISBN: 0-542-20387-1.
  • Liu, J. and J. Guo, 2015. Energy efficient scheduling of real-time tasks on multi-core processors with voltage islands. In: Future Generation Computer Systems, Jun. DOI: 10.1016/j.future.2015.06.003.
  • Tavares, E., P. Maciel, B. Silva and M.N. Oliveira, 2008. Hard real-time tasks' scheduling considering voltage scaling, precedence and exclusion relations. In: Information Processing Letters, vol. 108, issue. 2, Sept., pp: 50–59. DOI: 10.1016/j.ipl.2008.03.020.
  • Swaminathan, V., K. Chakrabarty and S.S. Iyengar, 2001. Dynamic I/O Power Management for Hard Real-time Systems. In: Hardware/Software Codesign, pp: 237–242. DOI: 10.1145/371636.371742.
  • Zhang, Y. and R. Guo, 2014. Power-aware fixed priority scheduling for sporadic tasks in hard real-time systems. In: Journal of Systems and Software, vol. 90, pp: 128–137. DOI:10.1016/j.jss.2013.12.032.
  • Awan, M.A. and S.M. Petters, 2015. Intra-task device scheduling for real-time embedded systems. In: Journal of Systems Architecture, vol. 61, issue. 8, Sep., pp: 321–340. DOI: 10.1016/j.sysarc.2015.07.001.
  • Fan, M., Q. Han, S. Liu, S. Ren, G. Quan and S.Ren, 2015. Enhanced fixed-priority real-time scheduling on multi-core platforms by exploiting task period relationship. In: Journal of Systems and Software, vol. 99, Jan., pp: 85–96. DOI:10.1016/j.jss.2014.09.010.
  • Kong, F., Y. Wang, Q. Deng and W. Yi, 2010. Minimizing Multi-Resource Energy for Real-Time Systems with Discrete Operation Modes. In: Real-Time Systems (ECRTS), 2010 22nd Euromicro Conference on, pp: 113–122. DOI: 10.1109/ECRTS.2010.18.
  • Zhang, Y-W. and R-f. Guo, 2013. Power-aware scheduling algorithms for sporadic tasks in real-time systems. In: Journal of Systems and Software, vol. 86, issue. 10, Oct., pp: 2611–2619. DOI:10.1016/j.jss.2013.04.075.
  • Jianjun, L., LihChyun, S. and Jian-Jia, C., 2013 Energy-Efficient Scheduling in Non preemptive Systems With Real-Time Constraints. In: IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 43, issue. 2, March., pp: 332-344. DOI: 10.1109/TSMCA.2012.2199305.
  • Mihai, P. and Tulika, M., 2014. Task Scheduling on Adaptive Multi-Core. In: IEEE Transactions on Computers, vol. 63, issue. 10, Oct., pp: 2590-2603. DOI: 10.1109/TC.2013.115.
  • Inoue, T., A. Aikebaier, T. Enokido and M. Takizawa, 2011. A Power Consumption Model of a Storage Server. In: Network-Based Information Systems (NBiS), pp: 382–387. DOI 10.1109/NBiS.2011.64.
  • Hylick, A., R. Sohan, A. Rice and B. Jones, 2008. An Analysis of Hard Drive Energy Consumption. In: Modeling, Analysis and Simulation of Computers and Telecommunication Systems, pp: 1–10. DOI: 10.1109/MASCOT.2008.4770567.
  • Mountrouidou, X., Riska, A. and Smirni, E. 2011. Saving power without compromising disk drive reliability. In: Green Computing Conference and Workshops (IGCC), pp: 1–6. DOI bookmark: http://doi.ieeecomputersociety.org/10.1109/IGCC.2011.6008570
  • Yun, H., A., Waqar and S., Gondi, 2016. BWLOCK: A Dynamic Memory Access Control Framework for Soft Real-Time Applications on Multicore Platforms. In: IEEE Transactions on Computers, vol. 66, issue. 7, Dec., pp: 1247-1252. DOI: 10.1109/TC.2016.2640961.
  • Kiani, V., Z. Mohseni and A.M. Rahmani, 2015. Real Time Scheduling for CPU and Hard Disk Requirements-Based Periodic Task with the Aim of Minimizing Energy Consumption. In: International Journal of Information Technology and Computer Science (IJITCS). DOI: 10.5815/ijitcs.2015.10.07.
  • Jacob,B. and Wang, D. 2007 Memory systems, Cache, DRAM, Disk eBook ISBN: 978-0-12-379751-3, Release date: Sep. 2007, http://store.elsevier.com/product.jsp?isbn=9780123797513. eBook ISBN: 9780080553849.
  • Torres, G., 2007. How The Memory Cache Works. In: Hardware secrets, Sep. http://www.hardwaresecrets.com/how-the-cache-memory-works/.
  • Anjum, M.D.M. and H., Wang, 2016. Dynamic scheduling and analysis of real time systems with multiprocessors. In: Digital Communications and Networks, vol. 2, issue. 3, Aug., pp: 130-138. DOI: https://doi.org/10.1016/j.dcan.2016.06.004.
  • Konar, D., S. Bhattacharyya, K. Sharma and S. Sharma, 2017. An improved Hybrid Quantum-Inspired Genetic Algorithm (HQIGA) for scheduling of real-time task in multiprocessor system. In: Applied Soft Computing, vol. 53, pp: 296-307. DOI: https://doi.org/10.1016/j.asoc.2016.12.051.
  • Kumar Samal, A., R. Mall and C. Tripathy, 2014. Fault tolerant scheduling of hard real-time tasks on multiprocessor system using a hybrid genetic algorithm. In: Swarm and Evolutionary Computation, vol.14, pp: 92-105. DOI: https://doi.org/10.1016/j.swevo.2013.10.002.
  • Intel® Xeon® Processor E5-2670 v3. http://ark.intel.com/products/81709/Intel-Xeon Processor-E5-2670-v3-30M-Cache-2_30-GHz, (Accessed on 09/25/2015).
  • Intel® Xeon® Processor E5 v2 Product Family, Datasheet, vol. 2, March. 2014. http://www.intel.com/content/www/us/en/processors/xeon/xeon-e5-v2-datasheet-vol-2.html.
  • HP SAS Hard Drives, c04311358 – DA – 12244 North America – Version 50 – June 1, 2015. http://www8.hp.com/us/en/products/oas/product-detail.html?oid=5163353.
  • Grochowski, E. and R.D. Halem, 2003. Technological impact of magnetic hard disk drives on storage systems. In: IBM SYSTEMSJOURNAL, vol. 42, no. 2. DOI: 10.1147/sj.422.0338.
Еще
Статья научная
QR-код для установки приложения SciUp Наведите камеру и скачайте бесплатное приложение SciUp