The International Arab Journal of Information Technology (IAJIT)


Achieving better Resource Utilization by Implementing a High-Performance Intelligent Framework in a Distributed Environment

Multi-distributed high-performance computers from many companies are aggregated into a single computing platform to provide handlers with uniform contact besides convention outlines. Job arrangement strategies in High-Performance Computing (HPC) environments are lacking in flexibility, so an enhanced computational intelligence automated system in the task ready queue, refinement of the principal planner aimed at every job, and increased arrangement of the job setting up plan are proposed in this paper, which introduces an improved task scheduling model. The swarm intelligence method is used in core task scheduling to reduce the average scheduling time for completing tasks by assigning jobs to each node in the most efficient manner possible. The suggested scheduling technique outperforms the standard work scheduling approach in simulations. Task waiting times can be reduced, system throughput increased, task response times improved, and system resources better utilized by using a job setting up method created on group Acumen systems.

[1] Amer D., Attiya G., Zeidan I., and Nasr A., “Elite Learning Harris Hawks Optimizer for Multi- Objective Task Scheduling in Cloud Computing,” The Journal of Supercomputing, vol. 78, pp. 2793- 2818, 2022. 03977-0

[2] Anzt H., Cojean T., Flegar G., and Göbel F., “Ginkgo: A Modern Linear Operator Algebra Framework for High Performance Computing,” ACM Transactions on Mathematical Software, vol. 48, no. 1, pp. 1-33, 2022. DOI:10.1145/3480935

[3] Bartolini A., Borghesi A., Lombardi M., Milano M., and Benini L., “Anomaly Detection Using Autoencoders in High Performance Computing Systems,” in Proceedings the of 31st AAAI Conference on Innovative Applications of Artificial Intelligence, Hawaii, pp. 9428-9433, 2019.

[4] Borghesi A., Libri A., Benini L., and Bartolini A., “Online Anomaly Detection in HPC Systems,” in Proceedings of the IEEE International Conference on Artificial Intelligence Circuits and Systems, Hsinchu, pp. 229-233, 2019.

[5] Carvalho T., Soares F., Vita R., Francisco R., Basto J., and Alcalá S., “A Systematic Literature Review of Machine Learning Methods Applied to Predictive Maintenance,” Computers and Industrial Engineering, vol. 137, pp. 106024, 2019.

[6] Essien A. and Giannetti C., “ A Deep Learning Model for Smart Manufacturing Using Convolutional LSTM Neural Network Autoencoders,” IEEE Transactions on Industrial Informatics, vol. 16, no. 9, pp. 6069-6078, 2020. DOI:10.1109/TII.2020.2967556

[7] Gupta S., Iyer S., Agarwal G., and Manoharan P., “Efficient Prioritization and Processor Selection Schemes for HEFT Algorithm: A Makespan Optimizer for Task Scheduling in Cloud Environment,” Electronics, vol. 11, no. 16, pp. 1- 15, 2022.

[8] Jamil B., Ijaz H., Shojafar M., Munir K., and Buyya R., “Resource Allocation and Task Scheduling in Fog Computing and Internet of Everything Environments: A Taxonomy, Review, and Future Directions,” ACM Computing Surveys, vol. 54, no. 11s, pp. 1-38, 2022.

[9] Jena B., Nayak G., and Saxena S., High- Performance Medical Image Processing, Apple Academic Press, 2022. DOI:10.1201/9781003190011-12 298 The International Arab Journal of Information Technology, Vol. 21, No. 2, March 2024

[10] Kruekaew B. and Kimpan W., “Multi-Objective Task Scheduling Optimization for Load Balancing in Cloud Computing Environment Using Hybrid Artificial Bee Colony Algorithm with Reinforcement Learning,” IEEE Access, vol. 10, pp. 17803-17818, 2022.

[11] Li C., Zhang C., Ma B., and Luo Y., “Efficient Multi-Attribute Precedence-based Task Scheduling for Edge Computing in Geo- Distributed Cloud Environment,” Knowledge and Information Systems, vol. 64, pp. 175-205, 2022. 021-01627-8

[12] Nayak S., Parida S., Tripathy C., and Pattnaik P., “An Enhanced Deadline Constraint-based Task Scheduling Mechanism for Cloud Environment,” Journal of King Saud University-Computer and Information Sciences, vol. 34, no. 2, pp. 282-294, 2022.

[13] Pirozmand P., Javadpour A., Nazarian H., Pinto P., Mirkamali S., and Ja’fari F., “GSAGA: A Hybrid Algorithm for Task Scheduling in Cloud Infrastructure,” The Journal of Supercomputing, vol. 78, no. 4, pp. 17423-17449, 2022..

[14] Sellami B., Hakiri A., Yahia S., and Berthou P., “Energy-Aware Task Scheduling and Offloading Using Deep Reinforcement Learning in SDN- Enabled IoT Network,” Computer Networks, vol. 210, pp. 108957, 2022.

[15] Shukla A., Kumar S., and Singh H., “Fault Tolerance-Based Load Balancing Approach for Web Resources in Cloud Environment,” The International Arab Journal of Information Technology, vol. 17, no. 2, pp. 225-232, 2020.,%20 No.%202/17514.pdf

[16] Talaat F., Ali H., Saraya M., and Saleh A., “Effective Scheduling Algorithm for Load Balancing in Fog Environment Using CNN and MPSO,” Knowledge and Information Systems, vol. 64, no. 3, pp. 773-797, 2022.

[17] Tripathi G. and Kumar R., “A Heuristic-Based Task Scheduling Policy for QoS Improvement in Cloud,” International Journal of Cloud Applications and Computing, vol. 12, no. 1, pp. 1- 22, 2022. DOI:10.4018/IJCAC.295238

[18] Wang X., Wang C., Bai M., Ma Q., and Li G., “HTD: Heterogeneous Throughput-Driven Task Scheduling Algorithm in MapReduce,” Distributed and Parallel Databases, vol. 40, no. 1, pp. 135-163, 2022.

[19] Yadav A., Tripathi K., and Sharma S., “An Enhanced Multi-Objective Fireworks Algorithm for Task Scheduling in Fog Computing Environment,” Cluster Computing, vol. 25, no. 2, pp. 983-998, 2022.

[20] Yurek O. and Birant D., “Remaining Useful Life Estimation for Predictive Maintenance Using Feature Engineering,” in Proceedings of the Innovations in Intelligent Systems and Applications Conference, Izmir, pp. 1-5, 2019. DOI:10.1109/ASYU48272.2019.8946397