Abstract

In an organization, resource allocation to a request is a complex task. Traditionally, resource allocation is done through manually with high time consumption. Similarly, collision is occurring for allocating a single resource to multiple requests. Thus, leads to complex problems and slow-down the working process. The existing resource allocation technique, allocate resources continuously to a specific request and omit another request. This kind of allocation technique also leads to lots of critical issues. That is the non-allocated process never gets a resource. To overcome these issues, the Round Robin based Resource allocation and Utilization technique is proposed in this work. The Round Robin technique allocates resources to the request in an efficient with equal priority. Similarly, the proposed technique reduces collision and takes less time for mapping a resource with a request. The experimental results shows improved accuracy than the traditional resource allocation technique.

Keywords

Resource Allocation, Round Robin, Logistic Regression,

Downloads

Download data is not yet available.

References

  1. D. Kondo H. Casanova, E. Wing, F. Berman, (2002) Models and Scheduling Mechanisms for Global Computing Applications, Proceedings 16th International Parallel and Distributed Processing Symposium (IPDPS '02), IEEE.
  2. Philippe Baptiste, Polynomial Time Algorithms for Minimizing the Weighted Number of Late Jobs on A Single Machine with Equal Processing Times, Journal of Scheduling, 2 (1999) 245-252.
  3. P. Holman, J.H. Anderson, Adapting Pfair Scheduling for Symmetric Multiprocessors, Journal of Embedded Computing,1 (2005) 543-564.
  4. Tao song, yuelin wang, guiling li, shanchen pang, Server Consolidation Energy-Saving Algorithm Based on Resource Reservation and Resource Allocation Strategy, IEEE access, 7 (2019) 171452.
  5. S. Wang, Y. Wang, J. Sun, Compromised resources allocation model for emergency response. In Third International Conference on Natural Computation, IEEE, 4 (2007) 446-450.
  6. M.B. Gawali, & S.K. Shinde, Task scheduling and resource allocation in cloud computing using a heuristic approach, Journal of Cloud Computing, 7(2018), 4.
  7. M. Haladu, & J. Samual, Optimizing task scheduling and resource allocation in cloud data center, using enhanced Min-Min algorithm, IOSR Journal of Computer Engineering, (2016) 2278-0661.
  8. A. Amjad Gawanmeh, A. Alomari, & A. April, Optimizing resource allocation scheduling in cloud computing services, Journal of Theoretical & Applied Information Technology, 95(2017) 31-39.
  9. Guangzhong Sun, Bin Fan, Guolin, Yinghua Zhou, Study on Scheduling Strategy for Global Computing Application, Seventh International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT'06), IEEE.
  10. B.S. Murygan, V. Vasudevan, B. Ganeshpandi, (2016) Intelligent scheduling system using agent-based resource allocation in cloud, 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT), IEEE.
  11. J.T Tsai, J.C. Fang, J.H. Chou, Optimized task scheduling and resource allocation on cloud computing environment using improved differential evolution algorithm, Computers & Operations Research, 40 (2013) 3045-3055.
  12. S.T. Maguluri, & R. Srikant, Scheduling jobs with unknown duration in clouds, IEEE/ACM Transactions On Networking, 22(2013) 1938-1951.
  13. C. Cheng, J. Li Y. Wang, An energy-saving task scheduling strategy based on vacation queuing theory in cloud computing, Tsinghua Science and Technology, 20 (2015) 28-39.
  14. W. Lin, C.Liang, J.Z. Wang, R. Buyya, Bandwidth-aware divisible task scheduling for cloud computing, Software: Practice and Experience, 44 (2014) 163-174.
  15. D. Ergu, G. Kou, Y. Peng, Y. Shi, Y. Shi, The analytic hierarchy process: task scheduling and resource allocation in cloud computing environment, The Journal of Super computing, 64 (2013) 835-848.
  16. X. Zhu, L. T Yang, H. Chen, J. Wang, S. Yin, X. Liu, Real-time tasks-oriented energy-aware scheduling in virtualized clouds, IEEE Transactions on Cloud Computing, 2 (2014) 168-180.
  17. X. Liu, Y. Zha, Q.Yin, Y. Peng, L. Qin, Scheduling parallel jobs with tentative runs and consolidation in the cloud, Journal of Systems and Software, 104 (2015) 141-151.
  18. A.E. Keshk, A.B. El-Sisi, M.A. Tawfeek, Cloud task scheduling for load balancing based on intelligent strategy, International Journal of Intelligent Systems and Applications, 6 (2014) 25.
  19. S Ghanbari, M. Othman, W.J. Leong, M. R. A. Bakar, (2014) Multi-criteria-based algorithm for scheduling divisible load, In: Proceedings of the first international conference on advanced data and information engineering (DaEng-2013), Springer, 547.
  20. S. Ghanbari, M. Othman, M.R.A. Bakar, W.J. Leong, Priority-based divisible load scheduling using analytical hierarchy process, Applied Mathematics and Information Sciences, 9 (2015) 2541.
  21. M. Sumathi, S. Sangeetha, (2019) Survey on Sensitive Data Handling – Challenges and Solutions in Cloud Storage System, Advances in Intelligent Systems and Computing, Springer, 189-196.