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20 September 2024
Reseach Article

Multi Optimized Job Scheduling Framework for VM with Enhanced Migration in a Multi Cloud Environment

by Md Tauqir Azam Kausar, Sanjay Pachauri
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 186 - Number 29
Year of Publication: 2024
Authors: Md Tauqir Azam Kausar, Sanjay Pachauri
10.5120/ijca2024923800

Md Tauqir Azam Kausar, Sanjay Pachauri . Multi Optimized Job Scheduling Framework for VM with Enhanced Migration in a Multi Cloud Environment. International Journal of Computer Applications. 186, 29 ( Jul 2024), 1-14. DOI=10.5120/ijca2024923800

@article{ 10.5120/ijca2024923800,
author = { Md Tauqir Azam Kausar, Sanjay Pachauri },
title = { Multi Optimized Job Scheduling Framework for VM with Enhanced Migration in a Multi Cloud Environment },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2024 },
volume = { 186 },
number = { 29 },
month = { Jul },
year = { 2024 },
issn = { 0975-8887 },
pages = { 1-14 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume186/number29/multi-optimized-job-scheduling-framework-for-vm-with-enhanced-migration-in-a-multi-cloud-environment/ },
doi = { 10.5120/ijca2024923800 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-07-26T23:00:28.733431+05:30
%A Md Tauqir Azam Kausar
%A Sanjay Pachauri
%T Multi Optimized Job Scheduling Framework for VM with Enhanced Migration in a Multi Cloud Environment
%J International Journal of Computer Applications
%@ 0975-8887
%V 186
%N 29
%P 1-14
%D 2024
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Optimization job scheduling of virtual machines in a cloud computing for tasks is considered as NP-hard problem specifically for large task sizes in the cloud. Hence many techniques for job scheduling have been presented previously but they did not consider the combined task scheduling and resource allocation, which reduces the flexibility, increase traffic, congestion, and reduces computation processing time. Hence a novel technique, namely Multi Optimized Job scheduling Framework for VM with enhanced migration in a Multi Cloud Environment has been proposed, in which the load balancers with multi-level optimizations that utilizes the runner root algorithm and Differential evolution algorithm with Levy distribution to schedule the job and determines the VM to be allotted for the job based on international and national level optimization. Moreover, the previous techniques concentrate only on the migration that extends VM lifespan, lacking Quality of Service (QoS) and unsatisfied the end users. Hence a novel technique Active Inactive data migration algorithm is used to prevent fluctuating migration between Virtual Machines and recursive algorithm keeps on iterating the same operation on the server with the lowest virtual load and Optimum Cost Function is to prevent unnecessary migration cost. During VM migration, several applications were affected during a live VM migration that caused a network fault, which is eliminated by a novel Data replacing approach which is used to transfer the exact size of data to the active PM. Overall, the proposed method is to perform an efficient job scheduling in multi cloud environment with optimized VM migration.

References
  1. Shukri, S. E., Al-Sayyed, R., Hudaib, A., &Mirjalili, S. (2021). Enhanced multi-verse optimizer for task scheduling in cloud computing environments. Expert Systems with Applications, 168, 114230.
  2. Alouffi, B., Hasnain, M., Alharbi, A., Alosaimi, W., Alyami, H., &Ayaz, M. (2021). A Systematic Literature Review on Cloud Computing Security: Threats and Mitigation Strategies. IEEE Access, 9, 57792-57807.
  3. Orazio, T., Domenico, C., &Pietro, M. (2021). TORCH: a TOSCA-Based Orchestrator of Multi-Cloud Containerised Applications. Journal of Grid Computing, 19(1).
  4. Pinto, A. R. N. (2021). Multi-Site and Multi-Cloud Deployment of Complex Information Systems.
  5. Bello, S. A., Oyedele, L. O., Akinade, O. O., Bilal, M., Delgado, J. M. D., Akanbi, L. A., ... &Owolabi, H. A. (2021). Cloud computing in construction industry: Use cases, benefits and challenges. Automation in Construction, 122, 103441.
  6. Pandey, Ashish, Prasad Calyam, Zhen Lyu, and Trupti Joshi. "Fuzzy-Engineered Multi-Cloud Resource Brokering for Data-intensive Applications." In 2021 IEEE/ACM 21st International Symposium on Cluster, Cloud and Internet Computing (CCGrid), pp. 257-266. IEEE, 2021.
  7. Ali, R., Shen, Y., Huang, X., Zhang, J. and Ali, A., 2017, July. VMR: virtual machine replacement algorithm for QoS and energy-awareness in cloud data centers. In 2017 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC) (Vol. 2, pp. 230-233). IEEE.
  8. Sadeeq, M. M., Abdulkareem, N. M., Zeebaree, S. R., Ahmed, D. M., Sami, A. S., &Zebari, R. R. (2021). IoT and Cloud computing issues, challenges and opportunities: A review. Qubahan Academic Journal, 1(2), 1-7.
  9. Tang, X. (2021). Reliability-Aware Cost-Efficient Scientific Workflows Scheduling Strategy on Multi-Cloud Systems. IEEE Transactions on Cloud Computing.
  10. Shahidinejad, A., Ghobaei-Arani, M., &Masdari, M. (2021). Resource provisioning using workload clustering in cloud computing environment: a hybrid approach. Cluster Computing, 24(1), 319-342.
  11. Shafiq, D. A., Jhanjhi, N. Z., Abdullah, A., &Alzain, M. A. (2021). A Load Balancing Algorithm for the Data Centres to Optimize Cloud Computing Applications. IEEE Access, 9, 41731-41744.
  12. Cai, X., Geng, S., Wu, D., Cai, J., & Chen, J. (2020). A Multicloud-Model-Based Many-Objective Intelligent Algorithm for Efficient Task Scheduling in Internet of Things. IEEE Internet of Things Journal, 8(12), 9645-9653.
  13. Zhang, B., Zeng, Z., Shi, X., Yang, J., Veeravalli, B., & Li, K. (2021). A novel cooperative resource provisioning strategy for Multi-Cloud load balancing. Journal of Parallel and Distributed Computing, 152, 98-107.
  14. Masdari, M., &Zangakani, M. (2020). Efficient task and workflow scheduling in inter-cloud environments: challenges and opportunities. The Journal of Supercomputing, 76(1), 499-535.
  15. Gupta, A. and Namasudra, S., 2022. A novel technique for accelerating live migration in cloud computing. Automated Software Engineering, 29(1), p.34.
  16. Khurana, S., & Singh, R. (2020). Workflow scheduling and reliability improvement by hybrid intelligence optimization approach with task ranking. EAI Endorsed Transactions on Scalable Information Systems, 7(24).
  17. Nabi, S., Ibrahim, M., & Jimenez, J. M. (2021). DRALBA: Dynamic and Resource Aware Load Balanced Scheduling Approach for Cloud Computing. IEEE Access, 9, 61283-61297.
  18. Sujana, J., Raj, R. V., &Revathi, T. (2022). Fuzzy-Based Workflow Scheduling in Multi-Cloud Environment. In Operationalizing Multi-Cloud Environments (pp. 201-215). Springer, Cham.
  19. Xie, F., Yan, J., &Shen, J. (2020, February). A Bandwidth and Latency Based Replica Selection Mechanism for Data-Intensive Workflow Applications in the Multi-Cloud Environment. In Proceedings of the Australasian Computer Science Week Multiconference (pp. 1-8).
  20. Ulabedin, Z., &Nazir, B. (2021). Replication and data management-based workflow scheduling algorithm for multi-cloud data centre platform. The Journal of Supercomputing, 1-30.
  21. Jena, Tamanna, and J. R. Mohanty. "GA-based customer-conscious resource allocation and task scheduling in multi-cloud computing." Arabian Journal for Science and Engineering 43, no. 8 (2018): 4115-4130.
  22. Ramasubbareddy, Somula, and R. Sasikala. "RTTSMCE: a response time aware task scheduling in multi-cloudlet environment." International Journal of Computers and Applications 43, no. 7 (2021): 691-696.
  23. Cai, X., Geng, S., Wu, D., Cai, J., & Chen, J. (2020). A Multicloud-Model-Based Many-Objective Intelligent Algorithm for Efficient Task Scheduling in Internet of Things. IEEE Internet of Things Journal, 8(12), 9645-9653.
  24. Chen, Z., Lin, K., Lin, B., Chen, X., Zheng, X., &Rong, C. (2020). Adaptive Resource Allocation and Consolidation for Scientific Workflow Scheduling in Multi-Cloud Environments. IEEE Access, 8, 190173-190183.
  25. Farid, M., Latip, R., Hussin, M., & Hamid, N. A. W. A. (2020). Scheduling scientific workflow using multi-objective algorithm with fuzzy resource utilization in multi-cloud environment. IEEE Access, 8, 24309-24322
  26. Thirumalaiselvan, C., and V. Venkatachalam. "A strategic performance of virtual task scheduling in multi cloud environment." Cluster Computing 22, no. 4 (2019): 9589-9597.
  27. XAVIER, VM ARUL, AND ANNADURAI, S. (2018) Chaotic social spider algorithm for load balance aware task scheduling in cloud computing. Cluster Computing, pp. 1- 11
  28. Hamad, S.A., Omara, F.A.: Genetic-based task scheduling algorithm in cloud computing environment. Int. J. Adv. Comput. Sci. Appl. 7(4), 550–556 (2016)
  29. Zhang, B., Zeng, Z., Shi, X., Yang, J., Veeravalli, B. and Li, K., 2021. A novel cooperative resource provisioning strategy for Multi-Cloud load balancing. Journal of Parallel and Distributed Computing, 152, pp.98-107.
  30. Tsakalozos, K., Verroios, V., Roussopoulos, M. and Delis, A., 2017. Live VM migration under time-constraints in share-nothing IaaS-clouds. IEEE Transactions on Parallel and Distributed Systems, 28(8), pp.2285-2298.
  31. M. H. Ferdaus, M. Murshed, R. N. Calheiros, and R. Buyya, ``An algorithmfor network and data-aware placement of multi-tier applications in cloud data centers,'' J. Netw. Comput. Appl., vol. 98, pp. 65_83, Nov. 2017.
Index Terms

Computer Science
Information Sciences
VM: Virtual Machine

Keywords

VMs migration Load balancing Live Migration Federation Runner root algorithm Conjugate function Steepest Descent Method Recursive algorithm Permutated sorting function Optimum Cost Function Levy distribution.