Heterogeneous High-Performance System Algorithm Based on Computer Big Data Technology

Authors

  • Dongyang Pan Xinyang Vocational and Technical College

DOI:

https://doi.org/10.4108/eetsis.3789

Keywords:

Priority queue division, Heterogeneous system, High performance computing, Time of completion, Dispatching Efficiency

Abstract

INTRODUCTION: In this paper, a scheduling algorithm for heterogeneous systems based on prioritization (PQDSA) is proposed. This algorithm is a sort method based on directed acyclic graph (DAG). The key nodes in the network are grouped according to the communication and computing costs in the network. This increases the parallelism between task schedules and reduces the completion time of work sets. Then, a method of assigning multiple tasks to multiple processors using interpolation is proposed. The PQDSA method can effectively reduce the time of scheduling multiple tasks and improve the scheduling effect. PQDSA is compared with EDL-θ and EDF scheduling methods. The results show that this method has better scheduling efficiency.

References

Saritha, S., Mamatha, E., Reddy, C. S., & Rajadurai, P. A model for overflow queuing network with two-station heterogeneous system. International Journal of Process Management and Benchmarking, 2022; 12(2):147-158.

Tian, S., Ren, W., Deng, Q., Zou, S., & Li, Y. A predictive energy consumption scheduling algorithm for multiprocessor heterogeneous system. IEEE Transactions on Green Communications and Networking, 2021;6(2):979-991.

Moori, A., Barekatain, B., & Akbari, M. LATOC: an enhanced load balancing algorithm based on hybrid AHP-TOPSIS and OPSO algorithms in cloud computing. The Journal of Supercomputing, 2022;78(4):4882-4910.

Ben Alla, H., Ben Alla, S., Ezzati, A., & Touhafi, A. A novel multiclass priority algorithm for task scheduling in cloud computing. The Journal of Supercomputing,2021; 77(10):11514-11555.

Wagner, C., Dhanaraj, N., Rizzo, T., Contreras, J., Liang, H., Lewin, G., & Pinciroli, C. SMAC: Symbiotic multi-agent construction. IEEE Robotics and Automation Letters, 2021;6(2):3200-3207.

Liu, L., Tang, J., Liu, S., Yu, B., Xie, Y., & Gaudiot, J. L. π-rt: A runtime framework to enable energy-efficient real-time robotic vision applications on heterogeneous architectures. Computer, 2021;54(4):14-25.

Hu, B., Cao, Z., & Zhou, M. Energy-minimized scheduling of real-time parallel workflows on heterogeneous distributed computing systems. IEEE Transactions on Services Computing, 2021;15(5):2766-2779.

Mack, J., Arda, S. E., Ogras, U. Y., & Akoglu, A. Performant, multi-objective scheduling of highly interleaved task graphs on heterogeneous system on chip devices. IEEE Transactions on Parallel and Distributed Systems, 2021;33(9):2148-2162.

Ulugbek, A., & Azamat, Q. Model of optimal distribution of network resources with constraints on quality of service indicators. Bulletin of Electrical Engineering and Informatics, 2023;12(2):835-842.

Jenila, L., & Canessane, R. A. Cross Layer Based Dynamic Traffic Scheduling Algorithm for Wireless Multimedia Sensor Network. IJEER, 2022;10(2):399-404.

Khenwar, M., Sisodia, A., Vishnoi, S., & Kumar, R. Exploration: Cloud Computing Scheduling Techniques. Scandinavian Journal of Information Systems, 2023;35(1):673-679.

Madhura, R., Uthariaraj, V. R., & Elizabeth, B. L. An efficient list‐based task scheduling algorithm for heterogeneous distributed computing environment. Software: Practice and Experience, 2023;53(2):390-412.

Huang, J., Li, R., An, J., Zeng, H., & Chang, W. A DVFS-weakly dependent energy-efficient scheduling approach for deadline-constrained parallel applications on heterogeneous systems. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2021;40(12):2481-2494.

Kaur, N., Kumar, A., & Kumar, R. 2022; TRAP: task-resource adaptive pairing for efficient scheduling in fog computing. Cluster Computing, 25(6):4257-4273.

Hamid, L., Jadoon, A., & Asghar, H. Comparative analysis of task level heuristic scheduling algorithms in cloud computing. The Journal of Supercomputing, 2022;78(11):12931-12949.

Tran-Dang, H., & Kim, D. S. FRATO: Fog resource based adaptive task offloading for delay-minimizing IoT service provisioning. IEEE Transactions on Parallel and Distributed Systems, 2021; 32(10):2491-2508.

Azizi, S., Othman, M., & Khamfroush, H. DECO: A Deadline-Aware and Energy-Efficient Algorithm for Task Offloading in Mobile Edge Computing. IEEE Systems Journal, 2022;17(1):952-963.

Yesil, S., & Ozturk, O. Scheduling for heterogeneous systems in accelerator-rich environments. The Journal of Supercomputing, 2022;78(1):200-221.

Serdaroglu, K. C., & Baydere, S. An efficient multipriority data packet traffic scheduling approach for fog of things. IEEE Internet of Things Journal, 2021;9(1):525-534.

Liu, J., Huang, J., Li, Z., Li, Y., Wang, J., & He, T. Achieving per-flow fairness and high utilization with limited priority queues in data center. IEEE/ACM Transactions on Networking, 2022;30(5):2374-2387.

Downloads

Published

18-10-2023

How to Cite

1.
Pan D. Heterogeneous High-Performance System Algorithm Based on Computer Big Data Technology. EAI Endorsed Scal Inf Syst [Internet]. 2023 Oct. 18 [cited 2024 May 20];11(1). Available from: https://publications.eai.eu/index.php/sis/article/view/3789