Have a personal or library account? Click to login
Uncertainty Aware T2SS Based Dyna-Q-Learning Framework for Task Scheduling in Grid Computing Cover

Uncertainty Aware T2SS Based Dyna-Q-Learning Framework for Task Scheduling in Grid Computing

By: K. Bhargavi and  Sajjan G. Shiva  
Open Access
|Sep 2022

References

  1. 1. Qasaimeh, M., R. S. Al-Qassas, S. Aljawarneh. Recent Development in Smart Grid Authentication Approaches: A Systematic Literature Review. – Cybernetics and Information Technologies, Vol. 19, 2019, No 1, pp. 27-52.10.2478/cait-2019-0002
  2. 2. Dabrowski, C. Reliability in Grid Computing Systems. – Concurrency and Computation: Practice and Experience, Vol. 21, 2009, No 8, pp. 927-959.10.1002/cpe.1410
  3. 3. Sadashiv, N., S. D. Kumar. Cluster, Grid and Cloud Computing: A Detailed Comparison. – In: Proc. of 6th International Conference on Computer Science & Education (ICCSE’11), IEEE, August 2011, pp. 477-482.10.1109/ICCSE.2011.6028683
  4. 4. Casanova, H. Distributed Computing Research Issues in Grid Computing. – ACM SIGAct News, Vol. 33, 2002, No 3, pp. 50-70.10.1145/582475.582486
  5. 5. Yu, J., R. Buyya, K. Ramamohanarao. Workflow Scheduling Algorithms for Grid Computing. – In: Proc. of Metaheuristics for Scheduling in Distributed Computing Environments, 2008, Berlin, Heidelberg, Springer, pp. 173-214.10.1007/978-3-540-69277-5_7
  6. 6. Maji, P. K., R. Biswas, A. R. Roy. Soft Set Theory. – Computers & Mathematics with Applications, Vol. 45, 2003, No 4-5, pp. 555-562.10.1016/S0898-1221(03)00016-6
  7. 7. Hayat, K., M. I. Ali, B. Y. Cao, X. P. Yang. A New Type-2 Soft Set: Type-2 Soft Graphs and Their Applications. – Advances in Fuzzy Systems, 2017.10.1155/2017/6162753
  8. 8. Gu, S., T. Lillicrap, I. Sutskever, S. Levine. Continuous Deep q-Learning with Model-Based Acceleration. – In: Proc. of International Conference on Machine Learning, PMLR, June 2016, pp. 2829-2838.
  9. 9. Jeaunita, T. J., V. Sarasvathi. A Multi-Agent Reinforcement Learning-Based Optimized Routing for QoS in IoT. – Cybernetics and Information Technologies, Vol. 21, 2021, No 4, pp. 45-61.10.2478/cait-2021-0042
  10. 10. Eng, K., A. Muhammed, M. A. Mohamed, S. Hasan. A Hybrid Heuristic of Variable Neighbourhood Descent and Great Deluge Algorithm for Efficient Task Scheduling in Grid Computing. – European Journal of Operational Research, Vol. 284, 2020, No 1, pp. 75-86.10.1016/j.ejor.2019.12.006
  11. 11. Bhatia, M. K. Task Scheduling in Grid Computing: A Review. – Advances in Computational Sciences and Technology, Vol. 10, 2017 No 6, pp. 1707-1714.
  12. 12. Casagrande, L. C., G. P. Koslovski, C. C. Miers, M. A. Pillon. DeepScheduling: Grid Computing Job Scheduler Based on Deep Reinforcement Learning. – In: Proc. of International Conference on Advanced Information Networking and Applications, April 2020, Springer Cham, pp. 1032-1044.10.1007/978-3-030-44041-1_89
  13. 13. Eng, K., A. Muhammed, M. A. Mohamed, S. Hasan. A Hybrid Heuristic of Variable Neighbourhood Descent and Great Deluge Algorithm for Efficient Task Scheduling in Grid Computing. – European Journal of Operational Research, Vol. 284, 2020, No 1, pp. 75-86.10.1016/j.ejor.2019.12.006
  14. 14. Umar, R., A. Pujiyanta. Development of First Come First Serve-Ejecting Based Dynamic Scheduling (FCFS-EDS) Simulation Scheduling Method for MPI Job in a Grid System. – Journal of Engineering and Applied Sciences, Vol. 12, 2017, No 8, pp. 1972-1978.
  15. 15. Tang, K., W. Jiang, R. Cui, Y. Wu. A Memory-Based Task Scheduling Algorithm for Grid Computing Based on Heterogeneous Platform and Homogeneous Tasks. – International Journal of Web and Grid Services, Vol. 16, 2020, No 3, pp. 287-304.10.1504/IJWGS.2020.109473
  16. 16. Zeigler, B. P., A. Muzy, E. Kofman. Theory of Modeling and Simulation: Discrete Event & Iterative System Computational Foundations. Academic Press, 2018.
  17. 17. Zhang, J., G. Ding, Y. Zou, S. Qin, J. Fu. Review of Job Shop Scheduling Research and Its New Perspectives under Industry 4.0. – Journal of Intelligent Manufacturing, Vol. 30, 2019, No 4, pp. 1809-1830.10.1007/s10845-017-1350-2
  18. 18. Nie, R., S. He, F. Liu, X. Luan, H. Shen. Hmm-Based Asynchronous Controller Design of Markovian Jumping Lur’e Systems within a Finite-Time Interval. – IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2020.10.1109/TSMC.2020.2964643
  19. 19. Bhattacharya, S., S. Badyal, T. Wheeler, S. Gil, D. Bertsekas. Reinforcement Learning for POMDP: Partitioned Rollout and Policy Iteration with Application to Autonomous Sequential Repair Problems. – IEEE Robotics and Automation Letters, Vol. 5, 2020, No 3, pp. 3967-3974.10.1109/LRA.2020.2978451
  20. 20. Heath, A., N. Kunst, C. Jackson, M. Strong, F. Alarid-Escudero, J. D. Goldhaber-Fiebert, H. Jalal. Calculating the Expected Value of Sample Information in Practice: Considerations from 3 Case Studies. – Medical Decision Making, Vol. 40, 2020, No 3, pp. 314-326.10.1177/0272989X20912402
  21. 21. Hironaka, T., M. B. Giles, T. Goda, H. Thom. Multilevel Monte Carlo Estimation of the Expected Value of Sample Information. – SIAM/ASA Journal on Uncertainty Quantification, Vol. 8, 2020, No 3, pp. 1236-1259.10.1137/19M1284981
  22. 22. Klusacek, D., M. Soysal, F. Suter. Alea-Complex Job Scheduling Simulator. – In: 13th International Conference on Parallel Processing and Applied Mathematics, September 2019.10.1007/978-3-030-43222-5_19
  23. 23. Kada, B., H. Kalla. An Efficient Fault-Tolerant Scheduling Approach with Energy Minimization for Hard Real-Time Embedded Systems. – In: Proc. of International Workshop on Distributed Computing for Emerging Smart Networks, October 2019, Springer Cham., pp. 102-117.10.1007/978-3-030-40131-3_7
  24. 24. Toshev, A. Particle Swarm Optimization and Tabu Search Hybrid Algorithm for Flexible Job Shop Scheduling Problem-Analysis of Test Results. – Cybernetics and Information Technologies, Vol. 19, 2019, No 4, pp. 26-44.10.2478/cait-2019-0034
  25. 25. Ivanova-Rohling, V. N., N. Rohling. Evaluating Machine Learning Approaches for Discovering Optimal Sets of Projection Operators for Quantum State Tomography of Qubit Systems. – Cybernetics and Information Technologies, Vol. 20, 2020, No 6, pp. 61-73.10.2478/cait-2020-0061
  26. 26. Eleliemy, A., A. Mohammed, F. M. Ciorba. Exploring the Relation between Two Levels of Scheduling Using a Novel Simulation Approach. – In: Proc. of 16th International Symposium on Parallel and Distributed Computing (ISPDC’17), 2017, pp. 26-33.10.1109/ISPDC.2017.23
DOI: https://doi.org/10.2478/cait-2022-0027 | Journal eISSN: 1314-4081 | Journal ISSN: 1311-9702
Language: English
Page range: 48 - 67
Submitted on: Mar 25, 2022
|
Accepted on: Jul 28, 2022
|
Published on: Sep 22, 2022
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2022 K. Bhargavi, Sajjan G. Shiva, published by Bulgarian Academy of Sciences, Institute of Information and Communication Technologies
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.