Genetic Algorithm for Load Balancing in Cloud Computing
##plugins.themes.bootstrap3.article.main##
Abstract
Cloud computing (CC) is a structural layout where virtual computers are involved and connected to the cloud service provider (CSP). The virtual computers establish a connection with CSP on the users’ behalf. VMs are overloaded by the uncertainty. The load may be caused by the CPU, memory, or network. In the preceding work, the genetic algorithm was used to migrate virtual machines (VMs). When a virtual machine is moved, the low-latency genetic algorithm shows a high-latency network. In this study, the evolutionary algorithm is used to migrate virtual machines. In this work, the suggested algorithm is utilized in MATLAB. The findings achieved are contrasted with those of an earlier algorithm.
References
Indresh Gangwar, Poonam Rana. Cloud Computing Overview: Services and Features. International Journal of Innovations & Advancement in Computer Science (IJIACS), Volume 3, Issue 1, 2014.
Srinivasa Rao V., Nageswara Rao N. K., E. Kusuma Kumari. Cloud Computing: An Overview. Journal of Theoretical and Applied Information Technology, Vol. 5, No. 2, 2009.
Mahantesh N. Birje, Praveen S. Challagidad, R. H. Goudar, Manisha T. Tapale. Cloud computing review: concepts, technology, challenges and security. International Journal of Cloud Computing, Vol. 6, No. 1, 2017.
Deepa B., Srigayathri S., Visalakshi S. A Review on Cloud Computing. International Journal of Trend in Research and Development, Vol. 4, No. 1, 2018.
Priyanshu Srivastava, Rizwan Khan. A Review Paper on Cloud Computing. International Journal of Advanced Research in Computer Science and Software Engineering, Volume 8, Issue 6, 2018.
Mohammad Oqail Ahmad, Dr. Rafiq ul Zaman Khan. The Cloud Computing: A Systematic Review. International Journal of Innovative Research in Computer and Communication Engineering, Vol. 3, Issue 5, 2015.
Majid Mehmood, Kinza Sattar, Asif Hussain Khan, Mujahid Afzal. Load Balancing Approach in Cloud Computing. Journal of Information Technology & Software Engineering, Vol. 5, No. 3, 2015.
W. Z. Jiang, Z. Q. Sheng. A New Task Scheduling Algorithm in Hybrid Cloud Environment. International Conference on Cloud and Service Computing, 2012, pp. 45–49.
S. Bilgaiyan, S. Sagnika, M. Das. Workflow scheduling in cloud computing environment using Cat Swarm Optimization. IEEE International Advance Computing Conference (IACC), 2014, pp. 680–685.
N. Chaudhary, M. Kalra. An improved Harmony Search algorithm with group technology model for scheduling workflows in cloud environment. 4th IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics (UPCON), 2017, pp. 73–77.
B. Wang, Y. Hou, M. Li. QuickN: Practical and Secure Nearest Neighbor Search on Encrypted Large-Scale Data. IEEE Transactions on Cloud Computing, Vol. 1, No. 16, pp. 265–273, 2020.
H. Zhu, Y. Jin. Multi-Objective Evolutionary Federated Learning. IEEE Transactions on Neural Networks and Learning Systems, Vol. 31, No. 4, pp. 1310–1322, 2020.
Z. L. Jiang, H. Guo, Y. Pan, Y. Liu, X. Wang, J. Zhang. Secure Neural Network in Federated Learning with Model Aggregation under Multiple Keys. IEEE CSCloud/EdgeCom, 2021, pp. 47–52.
T. Kong, L. Wang, D. Ma, K. Chen, Z. Xu, Y. Lu. ConfigRand: A Moving Target Defense Framework against Shared Kernel Information Leakages for Container-based Cloud. IEEE HPCC/SmartCity/DSS, 2020, pp. 794–801.
Z. Wu, K. Li, K. Li, J. Wang. Fast Boolean Queries with Minimized Leakage for Encrypted Databases in Cloud Computing. IEEE Access, Vol. 7, pp. 49418–49431, 2019.
B. H. K. Chen, P. Y. S. Cheung, P. Y. K. Cheung, Y. K. Kwok. CypherDB: A Novel Architecture for Outsourcing Secure Database Processing. IEEE Transactions on Cloud Computing, Vol. 6, No. 2, pp. 372–386, 2018.
S. Gao, G. Piao, J. Zhu, X. Ma, J. Ma. TrustAccess: A Trustworthy Secure Ciphertext-Policy and Attribute Hiding Access Control Scheme Based on Blockchain. IEEE Transactions on Vehicular Technology, Vol. 69, No. 6, pp. 5784–5798, 2020.
S. V. Usov, A. N. Mironenko. Solving optimization tasks of assigning roles within RBAC model. Dynamics of Systems, Mechanisms and Machines, 2019, pp. 1–7.
Y. Wang, Y. Ma, K. Xiang, Z. Liu, M. Li. A Role-Based Access Control System Using Attribute-Based Encryption. International Conference on Big Data and Artificial Intelligence (BDAI), 2018, pp. 128–133.
R. Ghazal, A. K. Malik, N. Qadeer, B. Raza, A. R. Shahid, H. Alquhayz. Intelligent Role-Based Access Control Model Using Semantic Business Roles. IEEE Access, Vol. 8, pp. 12253–12267, 2020.
K. Soni, S. Kumar. Comparison of RBAC and ABAC Security Models for Private Cloud. COMITCon, 2019, pp. 584–587.
K. Lee. Comments on Secure Data Sharing in Cloud Computing Using Revocable-Storage Identity-Based Encryption. IEEE Transactions on Cloud Computing, Vol. 8, No. 4, pp. 1299–1300, 2020.
Z. Y. Liu, Y. F. Tseng, R. Tso, Y. C. Chen, M. Mambo. Identity-Certifying Authority-Aided Identity-Based Searchable Encryption Framework. IEEE Systems Journal, 2021.
License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.