A Novel Probabilistic Strategy for Delay Corrected Allocation in Shared Resource Systems

1. Khan, S. U., I. Ahmad. Non-Cooperative, Semi-Cooperative, and Cooperative Games-Based Grid Resource Allocation. – In: Proc. of 20th International Parallel and Distributed Processing Symposium IPDPS’2006, 2006. http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=16393810.1109/IPDPS.2006.1639358Search in Google Scholar

2. Niyato, D., A. V. Vasilakos, Z. Kun. Resource and Revenue Sharing with Coalition Formation of Cloud Providers: Game Theoretic Approach. – In: Proc. of 11th IEEE/ACM International Symposium on Cluster, Grid and Cloud Computing, May 2011, pp. 215-224. http://dx.doi.org/10.1109/CCGrid.2011.3010.1109/CCGrid.2011.30Search in Google Scholar

3. Hong, M. An Alternating Direction Method Approach to Cloud Traffic Management. December 2014. http://arxiv.org/pdf/1407.8309.pdfSearch in Google Scholar

4. Xu, Y,. J. Chen, J. Wang, Y. Xu, Q. Wu, A. Anpalagan. Centralized-Distributed Spectrum Access for Small Cell Networks: A Cloud-Based Game Solution. February 2015. http://arxiv.org/pdf/1502.06670.pdfSearch in Google Scholar

5. Bala, A., I. Chena. A Survey of Various Scheduling Algorithms in Cloud Environment. – International Journal of Engineering Inventions, Vol. 1, 2011, No 2. http://www.ijeijournal.com/papers/v1i2/F0123639.pdfSearch in Google Scholar

6. Liu, K., Y. Yang, J. Chen, X. Liu, D. Yuan, H. Jin. A Compromised-Time-Cost Scheduling Algorithm in SwinDeW-C for Instance-Intensive Cost-Constrained Workflows on Cloud Computing Platform. – International Journal of High Performance Computing Applications, Vol. 24, May 2010, No 4, pp. 445-456.10.1177/1094342010369114Search in Google Scholar

7. Pandey, S., L. Wu1, S. M. Guru, R. Buyya. A Particle Swarm Optimization-Based Heuristic for Scheduling Workflow Applications in Cloud Computing Environments. – In: Proc. of 24th IEEE International Conference on Advanced Information Networking and Applications (AINA’2010), April 2010, pp. 400-407.10.1109/AINA.2010.31Search in Google Scholar

8. Yang, Y., K. Liu, J. Chen, X. Liu, D. Yuan, H. Jin. An Algorithm in SwinDeW-C for Scheduling Transaction Intensive Cost-Constrained Cloud Workflows. – In: Proc. of 4th IEEE International Conference on e-Science, Indianapolis, USA, December 2008, pp. 374-375.10.1109/eScience.2008.93Search in Google Scholar

9. Lin, C., S. Lu. Scheduling Scientific Workflows Elastically for Cloud Computing. – In: Proc. of IEEE 4th International Conference on Cloud Computing, July 2011, pp. 746-747.10.1109/CLOUD.2011.110Search in Google Scholar

10. Xu, M., L. Cui, H. Wang., Y. Bi. A Multiple QoS Constrained Scheduling Strategy of Multiple Workflows for Cloud Computing. – In: Proc. of IEEE International Symposium on Parallel and Distributed Processing, August 2009, pp. 629-634.10.1109/ISPA.2009.95Search in Google Scholar

11. Venticinque, S., R. Aversa, B. Di Martino, M. Rak, D. Petcu. A Cloud Agency for SLA Negotiation and Management. – In: M. R. Guarracino et al., Eds. Parallel Processing Workshops, Euro-Par’2010. – Lecture Notes in Computer Science. Vol. 6586. Berlin, Heidelberg, Springer, 2011.Search in Google Scholar

12. Goswami, B., S. Saha. Article: Resource Allocation Modeling in Abstraction Using Predator-Prey Dynamics: A Qualitative Analysis. – International Journal of Computer Applications, Vol. 61, 6-13 January 2013, No 6.10.5120/9930-4562Search in Google Scholar

13. Dressler, F., O. B. Akan. A Survey on Bio-Inspired Networking. – Journal of Computer Networks, Elsevier, Vol. 54, 2010, No 6, pp. 281-290.10.1016/j.comnet.2009.10.024Search in Google Scholar

14. Jalaparti, V., G. Nguyen, I. Gupta, M. Caeser. Cloud Resource Allocation Games. Technical Report, University of Illinois. http://hdl.handle.net/2142/17427.Search in Google Scholar

15. Xu, X., H. Yu. A Game Theory Approach to Fair and Efficient Resource Allocation in Cloud Computing. – Mathematical Problems in Engineering, Vol. 2014, 2014, Article ID 915878, 14 pages. doi:10.1155/2014/915878.10.1155/2014/915878Search in Google Scholar

16. Pillai, P. S., S. Rao. Resource Allocation in Cloud Computing Using the Uncertainty Principle of Game Theory. – IEEE System Journal, Vol. PP, May 2014, No 99, pp. 1-12.Search in Google Scholar

17. Li, Y., A. M. K. Cheng. Static Approximation Algorithms for Regularity-Based Resource Partitioning. – In: 33rd Real-Time Systems Symposium (RTSS’12), 2012, IEEE, pp. 137-148.10.1109/RTSS.2012.66Search in Google Scholar

18. Rajkumar, S. R. Resource Optimization Using Virtual Machine Swapping Circuits, Power and Computing Technologies. – In: International Conferences on Circuits, Power and Computing Technologies (ICCPCT’13), 2013.Search in Google Scholar

19. Wei, Y., C.-Z. Xu. Dynamic Balance Configuration of Multi Resource in Virtual Cluster. – In: Proc. of 21st IEEE International Symposium on Modelling, Analysis & Simulation of Computer and Telecommunication Systems (MASCOTS’13), 2013, pp. 60-69.10.1109/MASCOTS.2013.14Search in Google Scholar

20. Yu, H., W. Shi, T. Bai. An Open-Stack Based Resource Optimization Scheduling Framework. – In: 6th International Symposium on Computational Intelligence and Design, 2013, IEEE.Search in Google Scholar

21. Saha, S., J. Sarkar, M. N. Anand, A. Dwivedi, N. Dwivedi, R. Roy, S. Rao. A Novel Revenue Optimization Model to Address the Operation and Maintenance Cost of a Data Center. – Journal of Cloud Computing: Advances, Systems and Applications, Springer, Vol. 5, 2016, Issue 1, pp 1-23.10.1186/s13677-016-0063-ySearch in Google Scholar

22. Sarasvathi, V. N., S. N. Iyengar, S. Saha. QoS Guaranteed Intelligent Routing Using Hybrid PSO-GA in Wireless Mesh Networks. – Cybernetics and Information Technologies, Vol. 15, 2015, No 1, pp 69-83.10.1515/cait-2015-0007Search in Google Scholar

23. Mohanchandra, K., S. Saha, S. K. Murthy, G. M. Lingaraju. Distinct Adoption of k-Nearest Neighbor and Support Vector Machine in Classifying EEG Signals of Mental Tasks. – International Journal of Intelligent Engineering Informatics, Vol. 3, 2015, No 3.10.1504/IJIEI.2015.073064Search in Google Scholar

24. Mohanchandra, K., S. Saha, G. M. Lingaraju. EEG Based Brain Computer Interface for Speech Communication: Principles and Applications. – In: Brain-Computer Interfaces: Springer International Publishing, 2015, pp. 273-293.Search in Google Scholar

25. Kumar, D., S. K. Meher. Granular Neural Networks Models with Class-Belonging Granulation. – In: Proc. of International IEEE Conference on Contemporary Computing and Informatics (IC3I’14), 2014, pp. 1198-1202.10.1109/IC3I.2014.7019743Search in Google Scholar

26. Mohanchandra, K., S. Saha. Optimal Channel Selection for Robust EEG Single-Trial Analysis. – AASRI Procedia, Vol. 9, 2014, Elsevier, pp. 64-71.10.1016/j.aasri.2014.09.012Search in Google Scholar

27. Bora, K., S. Saha, S. Agrawal, M. Safonova, S. Routh, A. Narasimhamurthy. CD-HPF: New Habitability Score via Data Analytic Modeling Elsvier. – Astronomy and Computing, Vol. 17, 2016, pp. 129-143.10.1016/j.ascom.2016.08.001Search in Google Scholar

28. Khaidem., L., S. Saha, S. R. Dey. Predicting the Direction of Stock Market Prices Using Random Forest arXiv Preprint arXiv:1605.00003.Search in Google Scholar

29. Saha, S., N. Jangid, A. Mathur, A. M. Narsimhamurthy. DSRS: Estimation and Forecasting of Journal Influence in the Science and Technology Domain via a Lightweight Quantitative Approach COLLNET. – Journal of Scientometrics and Information Management, Vol. 10, 2016, No 1, pp. 41-70.10.1080/09737766.2016.1177939Search in Google Scholar