
(2) Fiftin Noviyanto

*corresponding author
AbstractThe current application of cloud computing focuses more on research problems. One of the main problems in the cloud is job allocation. Jobs are dynamically allocated to server processors. All cloud virtualized hardware is available to users on demand and can be dynamically upgraded. Resource scheduling is critical in research in the cloud, due to its large execution time and resource costs. The differences in resource scheduling criteria and parameters used cause various categories of Resource Scheduling Algorithms. Resource scheduling has a goal, identifying the right resources to schedule workloads in a timely manner and improving the effectiveness of resource utilization. In other words, minimizing workload completion time. Mapping the right workloads to resources will result in good scheduling. Another goal of resource scheduling is to identify adequate and appropriate workloads. So it can support scheduling of multiple workloads, to meet various QoS needs in cloud computing. The aim of this research is to determine the value of waiting time, idle time and makespan on cloud resources. The proposed method is to sort the arrival times of jobs with the least workload and place the jobs on a virtual view, before scheduling them on cloud resources. Experimental results show that the proposed method has an idle time of 25.3%, FCFS is 43.1% while for bacfilling it is 31.5%. The average makespan reduction for FCFS is 16.73%, for bacfilling it is 12.87%. The average decrease in AWT for FCFS was 13.3% for bacfilling of 12.03%. The results of this research can be applied to cloud rentals with flexible times. KeywordsCloud computing; Idle Time; Virtual view; Waiting time; Makespan
|
DOIhttps://doi.org/10.26555/ijain.v10i3.1421 |
Article metricsAbstract views : 182 | PDF views : 48 |
Cite |
Full Text![]() |
References
[1] F. Alhaidari, T. Balharith, and E. AL-Yahyan, “Comparative Analysis for Task Scheduling Algorithms on Cloud Computing,” in 2019 International Conference on Computer and Information Sciences (ICCIS), Apr. 2019, pp. 1–6, doi: 10.1109/ICCISci.2019.8716470.
[2] M. Ibrahim et al., “A Comparative Analysis of Task Scheduling Approaches in Cloud Computing,” in 2020 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGRID), May 2020, pp. 681–684, doi: 10.1109/CCGrid49817.2020.00-23.
[3] P. M. Rekha and M. Dakshayini, “Efficient task allocation approach using genetic algorithm for cloud environment,” Cluster Comput., vol. 22, no. 4, pp. 1241–1251, Dec. 2019, doi: 10.1007/s10586-019-02909-1.
[4] Y. Su, Z. Bai, and D. Xie, “The optimizing resource allocation and task scheduling based on cloud computing and Ant Colony Optimization Algorithm,” J. Ambient Intell. Humaniz. Comput., pp. 1–9, Aug. 2021, doi: 10.1007/s12652-021-03445-w.
[5] L. Golightly, V. Chang, Q. A. Xu, X. Gao, and B. S. C. Liu, “Adoption of cloud computing as innovation in the organization,” Int. J. Eng. Bus. Manag., vol. 14, p. 184797902210939, Jan. 2022, doi: 10.1177/18479790221093992.
[6] K. Braiki And H. Youssef, “Resource Management in Cloud Data Centers: A Survey,” in 2019 15th International Wireless Communications & Mobile Computing Conference (IWCMC), Jun. 2019, pp. 1007–1012, doi: 10.1109/IWCMC.2019.8766736.
[7] M. Kumar, S. C. Sharma, A. Goel, and S. P. Singh, “A comprehensive survey for scheduling techniques in cloud computing,” J. Netw. Comput. Appl., vol. 143, pp. 1–33, Oct. 2019, doi: 10.1016/j.jnca.2019.06.006.
[8] S. A. Murad, A. J. M. Muzahid, Z. R. M. Azmi, M. I. Hoque, and M. Kowsher, “A review on job scheduling technique in cloud computing and priority rule based intelligent framework,” J. King Saud Univ. - Comput. Inf. Sci., vol. 34, no. 6, pp. 2309–2331, Jun. 2022, doi: 10.1016/j.jksuci.2022.03.027.
[9] M. Usman Sana and Z. Li, “Efficiency aware scheduling techniques in cloud computing: a descriptive literature review,” PeerJ Comput. Sci., vol. 7, p. e509, May 2021, doi: 10.7717/peerj-cs.509.
[10] S. H. H. Madni, M. S. A. Latiff, Y. Coulibaly, and S. M. Abdulhamid, “Recent advancements in resource allocation techniques for cloud computing environment: a systematic review,” Cluster Comput., vol. 20, no. 3, pp. 2489–2533, Sep. 2017, doi: 10.1007/s10586-016-0684-4.
[11] A. A. Nayak and S. Shetty, “A Systematic Analysis on Task Scheduling Algorithms for Resource Allocation of Virtual Machines on Cloud Computing Environments,” in 2023 International Conference on Recent Trends in Electronics and Communication (ICRTEC), Feb. 2023, pp. 1–6, doi: 10.1109/ICRTEC56977.2023.10111894.
[12] K. Pradeep, N. Gobalakrishnan, N. Manikandan, L. J. Ali, P. K., and K. . Vijayakumar, “A Review on Task Scheduling using Optimization Algorithm in Clouds,” in 2021 5th International Conference on Trends in Electronics and Informatics (ICOEI), Jun. 2021, pp. 935–938, doi: 10.1109/ICOEI51242.2021.9452837.
[13] B. Wang, C. Wang, Y. Song, J. Cao, X. Cui, and L. Zhang, “A survey and taxonomy on workload scheduling and resource provisioning in hybrid clouds,” Cluster Comput., vol. 23, no. 4, pp. 2809–2834, Dec. 2020, doi: 10.1007/s10586-020-03048-8.
[14] S. Varshney, R. Sandhu, and P. K. Gupta, “QoS Based Resource Provisioning in Cloud Computing Environment: A Technical Survey,” in Communications in Computer and Information Science, vol. 1046, Springer, Singapore, 2019, pp. 711–723, doi: 10.1007/978-981-13-9942-8_66.
[15] S. S. Gill and R. Buyya, “Resource Provisioning Based Scheduling Framework for Execution of Heterogeneous and Clustered Workloads in Clouds: from Fundamental to Autonomic Offering,” J. Grid Comput., vol. 17, no. 3, pp. 385–417, Sep. 2019, doi: 10.1007/s10723-017-9424-0.
[16] M. Naghshnejad and M. Singhal, “A hybrid scheduling platform: a runtime prediction reliability aware scheduling platform to improve HPC scheduling performance,” J. Supercomput., vol. 76, no. 1, pp. 122–149, Jan. 2020, doi: 10.1007/s11227-019-03004-3.
[17] J. Natarajan, “Parallel Queue Scheduling in Dynamic Cloud Environment Using Backfilling Algorithm,” Int. J. Intell. Eng. Syst., vol. 11, no. 2, pp. 39–48, Apr. 2018, doi: 10.22266/ijies2018.0430.05.
[18] E. H. Houssein, A. G. Gad, Y. M. Wazery, and P. N. Suganthan, “Task Scheduling in Cloud Computing based on Meta-heuristics: Review, Taxonomy, Open Challenges, and Future Trends,” Swarm Evol. Comput., vol. 62, p. 100841, Apr. 2021, doi: 10.1016/j.swevo.2021.100841.
[19] W. Khallouli and J. Huang, “Cluster resource scheduling in cloud computing: literature review and research challenges,” J. Supercomput., vol. 78, no. 5, pp. 6898–6943, Apr. 2022, doi: 10.1007/s11227-021-04138-z.
[20] A. Arunarani, D. Manjula, and V. Sugumaran, “Task scheduling techniques in cloud computing: A literature survey,” Futur. Gener. Comput. Syst., vol. 91, pp. 407–415, Feb. 2019, doi: 10.1016/j.future.2018.09.014.
[21] R. Istrate, A. Poenaru, and F. Pop, “Advance Reservation System for Datacenters,” in 2016 IEEE 30th International Conference on Advanced Information Networking and Applications (AINA), Mar. 2016, vol. 2016-May, pp. 637–644, doi: 10.1109/AINA.2016.106.
[22] J. Chamberlain, E. Simhon, and D. Starobinski, “Preemptible queues with advance reservations: Strategic behavior and revenue management,” Eur. J. Oper. Res., vol. 293, no. 2, pp. 561–578, Sep. 2021, doi: 10.1016/j.ejor.2020.12.044.
[23] K. P. N. Jayasena and B. S. Thisarasinghe, “Optimized task scheduling on fog computing environment using meta heuristic algorithms,” in 2019 IEEE International Conference on Smart Cloud (SmartCloud), Dec. 2019, pp. 53–58, doi: 10.1109/SmartCloud.2019.00019.
[24] P. Mallik, A. K. Nayak, and R. Kumar Dalei, “Comparative Analysis of Various Task Scheduling Algorithms in Cloud Environment,” in 2021 19th OITS International Conference on Information Technology (OCIT), Dec. 2021, pp. 37–41, doi: 10.1109/OCIT53463.2021.00019.
[25] E. Hosseini, M. Nickray, and S. Ghanbari, “Optimized task scheduling for cost-latency trade-off in mobile fog computing using fuzzy analytical hierarchy process,” Comput. Networks, vol. 206, p. 108752, Apr. 2022, doi: 10.1016/j.comnet.2021.108752.
[26] N. Chitgar, H. Jazayeriy, and M. Rabiei, “DSCTS: Dynamic Stochastic Cloud Task Scheduling,” in 2019 5th Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS), Dec. 2019, pp. 1–5, doi: 10.1109/ICSPIS48872.2019.9066063.
[27] M. T. Alam Siddique, S. Sharmin, and T. Ahammad, “Performance Analysis and Comparison Among Different Task Scheduling Algorithms in Cloud Computing,” in 2020 2nd International Conference on Sustainable Technologies for Industry 4.0 (STI), Dec. 2020, pp. 1–6, doi: 10.1109/STI50764.2020.9350466.
[28] K. M. S. . Bandaranayake, K. P. . Jayasena, and B. T. G. S. Kumara, “An Efficient Task Scheduling Algorithm using Total Resource Execution Time Aware Algorithm in Cloud Computing,” in 2020 IEEE International Conference on Smart Cloud (SmartCloud), Nov. 2020, pp. 29–34, doi: 10.1109/SmartCloud49737.2020.00015.
[29] R. K. R. Indukuri, S. V. Penmasta, M. V. R. Sundari, and G. J. Moses, “Performance Evaluation of Deadline Aware Multi-stage Scheduling in Cloud Computing,” in 2016 IEEE 6th International Conference on Advanced Computing (IACC), Feb. 2016, pp. 229–234, doi: 10.1109/IACC.2016.51.
[30] P. Y. Zhang and M. C. Zhou, “Dynamic Cloud Task Scheduling Based on a Two-Stage Strategy,” IEEE Trans. Autom. Sci. Eng., vol. 15, no. 2, pp. 772–783, 2018, doi: 10.1109/TASE.2017.2693688.

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
___________________________________________________________
International Journal of Advances in Intelligent Informatics
ISSN 2442-6571 (print) | 2548-3161 (online)
Organized by UAD and ASCEE Computer Society
Published by Universitas Ahmad Dahlan
W: http://ijain.org
E: info@ijain.org (paper handling issues)
andri.pranolo.id@ieee.org (publication issues)
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0