Open Access
Issue
ITM Web Conf.
Volume 54, 2023
2nd International Conference on Advances in Computing, Communication and Security (I3CS-2023)
Article Number 01005
Number of page(s) 8
Section Computing
DOI https://doi.org/10.1051/itmconf/20235401005
Published online 04 July 2023
  1. S. Saroj, A. Sharma, Novel CPU scheduling with variable time quantum based on mean difference of brust time, in, 2016 International Conference on Computing, Communication and Automation, ICCCA, 1342–1347, (2016) [CrossRef] [Google Scholar]
  2. S. Saha, S. Pal, A novel scheduling algorithm for cloud computing environment, in International conference on computational intelligence in data mining (CIDM), 387–398, (2015) [Google Scholar]
  3. Y. Zhao, X. Fei, Opportunities and challenges in running scientific workflows on the cloud, in 2011 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, 455–452, (2011) [CrossRef] [Google Scholar]
  4. Y. Wang, CLOSURE: a cloud scientific workflow scheduling algorithm based on attack-defense game model, Future Gener. Comput. Syst. 111, 460–174, (2020) [CrossRef] [Google Scholar]
  5. M. Rodriguez, Deadline based resource provisioning and scheduling algorithm for scientific workflows on clouds, IEEE Trans. Cloud Comput. 2 (2), 222–235, (2014) [CrossRef] [Google Scholar]
  6. Z. Li, Cost and energy aware scheduling algorithm for scientific workflows with deadline constraint in clouds, IEEE Trans. Serv. Comput. 11 (4), 713–726, (2018) [CrossRef] [Google Scholar]
  7. Z. Tong, A scheduling scheme in the cloud computing environment using deep Q-learning, Inf. Sci. 512, 1170–1191, (2020) [CrossRef] [Google Scholar]
  8. L. Zhang, Efficient scientific workflow scheduling for deadline-constrained parallel tasks in cloud computing environments, Inf. Sci. 531, 31–46, (2020) [CrossRef] [Google Scholar]
  9. J. Durillo, R. Prodan, MOHEFT: a multi-objective list-based method for workflow scheduling, in International Conference on Cloud Computing Technology and Science Proceedings, 185–192 (2012) [Google Scholar]
  10. Z. Zhu, Evolutionary multi-objective workflow scheduling in cloud, IEEE Trans. Parallel Distrib. Syst. 27 (5), 1344–1357, (2016) [CrossRef] [Google Scholar]
  11. M. Kalra, Multi-objective energy aware scheduling of deadline constrained workflows in clouds using hybrid approach, Wireless Pers. Commun. 116 (3), 1743–1764, (2021) [CrossRef] [Google Scholar]
  12. P. Pandey, Robust orchestration of concurrent application workflows in mobile device clouds, J. Parallel Distrib. Comput. 120, 101–114, (2018) [CrossRef] [Google Scholar]
  13. A. Berl, E. Gelenbe, D. Girolamo, G. Giuliani, D. Meer, Q. Dang, Pentikousis, K.: Energy- efficient cloud computing, The Comp. J., vol. 53, no. 7, 1045–1051, (2010) [CrossRef] [Google Scholar]
  14. Amazon EC2 Instance Types, http://aws.amazon.com/ec2/instancetypes/ [Google Scholar]
  15. J. Cao, Y. Wu, M. Li, Energy efficient allocation of virtual machines in cloud computing environment based on demand forecast, R. Li, J. Cao and J. Bourgeois (Eds) GPC 2012, LNCS 7296, 137–151, (2012) [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.