CS Optimized Task Scheduling for Cloud Data Management
CS Optimized Task Scheduling for Cloud Data Management
|© 2022 by IJETT Journal|
|Year of Publication : 2022|
|Authors : Mandeep Singh, Shashi Bhushan
|DOI : 10.14445/22315381/IJETT-V70I6P214|
How to Cite?
Mandeep Singh, Shashi Bhushan, "CS Optimized Task Scheduling for Cloud Data Management," International Journal of Engineering Trends and Technology, vol. 70, no. 6, pp. 114-121, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I6P214
Task scheduling is the most recent networking technology in cloud computing. Among various technologies, the concept of Virtualization, dynamic sharing, delivering quality service, and load balancing are some of the most attractive ones that require high attention. While scheduling tasks and sharing applications, the most important challenge is to minimize execution time while maintaining the quality of service in terms of Service Level Agreement (SLA) and energy consumption. In the present paper, the authors proposed a Cuckoo Search Optimization to improve the local search strategy and schedule tasks in the cloud computing environment. This iterative search mechanism integrated efficient task scheduling with the neural architecture to achieve secure scheduling. The simulation analysis performed up to 1000 tasks for 100 user requests in terms of SLA violation, and energy consumption demonstrated the effectiveness of the proposed CS optimized, secure scheduling.
Cloud Computing, Cuckoo Search, Modified Best Fit Decreasing, Neural Network, Scheduling.
 A. Beloglazov, C. Abawajyb and R. Buyya, Energy-Aware Resource Allocation Heuristics for Efficient Management of Data Center for Cloud Computing, Future Generation & Computer Systems. 28(5) (2012) 755–768.
 B. Wang, L. Fagui and L. Weiwei, Energy-Efficient VM Scheduling Based on Deep Reinforcement Learning, Future Generation Computer Systems. 125 (2021) 616-628.
 C. Sudhakar and T. Ramesh, Energy-Efficient VM Scheduling and Routing in a Multi-Tenant Cloud Data Center, Sustainable Computing, Informatics and Systems. 22 (2019) 139-151.
 C. D. Wei, F. L. ChunhuaGu, C. Yaohui, R. Ulysse, L. Xiaoke, and W. Geng, DFA-VMP: An Efficient and Secure Virtual Machine Placement Strategy Under Cloud Environment, Peer-to-Peer Networking, and Applications. 11(2) (2018) 318-333.
 A. Gupta, and S. Namasudra, A Novel Technique for Accelerating Live Migration in Cloud Computing, Automated Software Engineering. 29(1) (2022) 1-21.
 N. Mostafa, A. Ibrahim, and H. A. Ali, Optimization of Live Virtual Machine Migration in Cloud Computing: A Survey and Future Directions, Journal of Network and Computer Applications. 110 (2018) 1-10.
 H. Li, G. Zhu, C. Cui, H. Tang, Y. Dou, and C. He, Energy-Efficient Migration and Consolidation Algorithm of Virtual Machines in Data Centers for Cloud Computing, Computing. 98(3) (2016) 303-317.
 B. B. Naik, D. Singh, and A. B. Samaddar, FHCS: Hybridised Optimization for Virtual Machine Migration and Task Scheduling in a Cloud Data Center, IET Communications. 14(12) (2020) 1942-1948.
 H. O. Salami, A. Bala, S. M. Sait, And I. Ismail, The Journal of Supercomputing, An Energy-Efficient Cuckoo Search Algorithm for Virtual Machine Placement in Cloud Computing Data Centers. 77(11) (2021) 13330-13357.
 K. Tuli, and A. Kaur, Hybridization of Harmony and Cuckoo Search for Managing the Task Scheduling in Cloud Environment, in Proceedings of Data Analytics and Management. (2022) 749-761.
 D. M. Zhao, J. T. Zhou, and K. Li, An Energy-Aware Algorithm for Virtual Machine Placement in Cloud Computing, IEEE Access. 7 (2019) 55659-55668.
 I. Leila and H. Materwala, Energy-Aware VM Placement and Task Scheduling in Cloud-Iot Computing: Classification and performance Evaluation, IEEE Internet of Things Journal. 5(6) (2018) 5166-5176.
 K. Balaji, P. Sai Kiran, and M. Sunil Kumar, Power-Aware Virtual Machine Placement in IAAS Cloud Using Discrete Firefly Algorithm, Applied Nanoscience. (2022) 1-9.
 K. Vincent, E. Madelaine, and F. Hermenier, Scheduling Live Migration of Virtual Machines, IEEE Transactions on Cloud Computing. 8(1) (2017) 282-296.
 Ahmad and R. Wasim, A Survey on Virtual Machine Migration and Server Consolidation Frameworks for Cloud Data Centers, Journal of Network and Computer Applications. 52 (2015) 11-25.
 E. Barlaskar, J. S. Yumnam, and B. Issac, Enhanced Cuckoo Search Algorithm for Virtual Machine Placement in Cloud Data Centers, International Journal of Grid and Utility Computing. 9(1) (2018) 1-17.
 Z. Zhong, K. Chen, X. Zhai, and S. Zhou, Virtual Machine-Based Task Scheduling Algorithm in a Cloud Computing Environment, Science and Technology. 21(6) (2016) 660-667.
 K. Pradeep, and T. Prem Jacob, A Hybrid Approach for Task Scheduling Using the Cuckoo and Harmony Search in a Cloud Computing Environment, Wireless Personal Communications. 101(4) (2018) 2287-2311.
 S. N. Raghavendra, K. M. Jogendra, and C. C. Smitha, A Secured and Effective Load Monitoring and Scheduling Migration VM in Cloud Computing, In IOP Conference Series: Materials Science and Engineering. 981(2) (2020) 22-39.
 H. Nashaat, N. Ashry, and R. Rizk, Smart Elastic Scheduling Algorithm for Virtual Machine Migration in Cloud Computing, The Journal of Supercomputing. 75(7) (2019) 3842-3865.
 D. Shalu, and D. Singh, Artificial Neural Network-Based Virtual Machine Allocation in Cloud Computing, Journal of Discrete Mathematical Sciences and Cryptography. 24(6) (2021) 1739-1750.
 A. Radhakrishnan, and V. Kavitha, Energy Conservation in Cloud Data Centers by Minimizing Virtual Machines Migration through an Artificial Neural Network, Computing. 98(11) (2016) 1185-1202.
 G. Kumar, and P. Vivekanandan, Energy-Efficient Scheduling for Cloud Data Centers Using Heuristic-Based Migration, Cluster Computing. 22(6) (2019) 14073-14080.
 B. Muthulakshmi, and K. Somasundaram, A Hybrid ABC-SA-Based Optimized Scheduling and Resource Allocation for Cloud Environment, Cluster Computing. 22(5) (2019) 10769-10777.
 Chen, Z., Wei P & Li Y, Combining Neural Network-Based Method with Heuristic Policy for Optimal Task Scheduling in Hierarchical Edge Cloud, Digital Communications and Networks. (2022).
 Chhabra A, Huang K. C, Bacanin N & Rashid T. A, Optimizing Bag-of-Tasks Scheduling on Cloud Data Centers Using Hybrid Swarm-Intelligence Meta-Heuristic, The Journal of Supercomputing. 78(7) (2022) 9121-9183.
 Mahmoud H, Thabet M, Khafagy M. H & Omara F. A, Multiobjective Task Scheduling in Cloud Environment Using Decision Tree Algorithm. IEEE Access. 10 (2022) 36140-36151.
 Yang, Xin-She, and S. Deb, Cuckoo Search: Recent Advances and Applications, Neural Computing and Applications. 24(1) (2014) 169-174