Design and Implementation of Virtualization Cloud Computing System Intelligent Terminal Application Layer
DOI:
https://doi.org/10.13052/jicts2245-800X.1222Keywords:
Intelligent terminal, application layer, virtualization, cloud ComputingAbstract
Cloud task scheduling has become a trend, and the shortcomings of traditional scheduling algorithms can be optimized through mathematical models of other cloud task scheduling scenarios. In order to improve the virtualization data processing effect of intelligent terminal application layer, this paper proposes an improved krill swarm optimization algorithm based on adaptive weight. The optimization of cluster load balancing and task average response time ratio are used to improve the convergence and accuracy of task scheduling algorithm. Moreover, this paper uses CloudSim simulation tool to conduct experiments to verify the effectiveness of the proposed model. In addition, this paper proposes an application-based virtualization method, which virtualizes the application programs inside the host machine into the virtualization software inside the virtual machine, so that the virtual machine can access it. Finally, this paper verifies the reliability of the proposed method with experiments, thus providing a theoretical reference for the subsequent design of intelligent terminal application layer virtualization cloud computing system. Compared with the traditional way of using physical hardware, using virtual machine hardware is more flexible, efficient and safe, which brings great convenience to the development and deployment of applications.
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Shukur, H., Zeebaree, S., Zebari, R., Zeebaree, D., Ahmed, O., and Salih, A. (2020). Cloud computing virtualization of resources allocation for distributed systems. Journal of Applied Science and Technology Trends, 1(2), 98–105.
Bhardwaj, A., and Krishna, C. R. (2021). Virtualization in cloud computing: Moving from hypervisor to containerization – a survey. Arabian Journal for Science and Engineering, 46(9), 8585–8601.
Katal, A., Dahiya, S., and Choudhury, T. (2023). Energy efficiency in cloud computing data centers: a survey on software technologies. Cluster Computing, 26(3), 1845–1875.
El Kafhali, S., El Mir, I., and Hanini, M. (2022). Security threats, defense mechanisms, challenges, and future directions in cloud computing. Archives of Computational Methods in Engineering, 29(1), 223–246.
Pallathadka, H., Sajja, G. S., Phasinam, K., Ritonga, M., Naved, M., Bansal, R., and Quiñonez-Choquecota, J. (2022). An investigation of various applications and related challenges in cloud computing. Materials Today: Proceedings, 51(3), 2245–2248.
Vinoth, S., Vemula, H. L., Haralayya, B., Mamgain, P., Hasan, M. F., and Naved, M. (2022). Application of cloud computing in banking and e-commerce and related security threats. Materials Today: Proceedings, 51(2), 2172–2175.
Zhang, P., Zhou, M., and Wang, X. (2020). An intelligent optimization method for optimal virtual machine allocation in cloud data centers. IEEE Transactions on Automation Science and Engineering, 17(4), 1725–1735.
Mughal, A. A. (2021). Cybersecurity Architecture for the Cloud: Protecting Network in a Virtual Environment. International Journal of Intelligent Automation and Computing, 4(1), 35–48.
Masdari, M., Gharehpasha, S., Ghobaei-Arani, M., and Ghasemi, V. (2020). Bio-inspired virtual machine placement schemes in cloud computing environment: taxonomy, review, and future research directions. Cluster Computing, 23(4), 2533–2563.
Mishra, S. K., Sahoo, B., and Parida, P. P. (2020). Load balancing in cloud computing: a big picture. Journal of King Saud University-Computer and Information Sciences, 32(2), 149–158.
Bharany, S., Sharma, S., Khalaf, O. I., Abdulsahib, G. M., Al Humaimeedy, A. S., Aldhyani, T. H., … and Alkahtani, H. (2022). A systematic survey on energy-efficient techniques in sustainable cloud computing. Sustainability, 14(10), 6256–6264.
Alam, T. (2020). Cloud Computing and its role in the Information Technology. IAIC Transactions on Sustainable Digital Innovation (ITSDI), 1(2), 108–115.
Tang, X. (2021). Reliability-aware cost-efficient scientific workflows scheduling strategy on multi-cloud systems. IEEE Transactions on Cloud Computing, 10(4), 2909–2919.
Li, W., Wu, J., Cao, J., Chen, N., Zhang, Q., and Buyya, R. (2021). Blockchain-based trust management in cloud computing systems: a taxonomy, review and future directions. Journal of Cloud Computing, 10(1), 35–44.
Alsaidy, S. A., Abbood, A. D., and Sahib, M. A. (2022). Heuristic initialization of PSO task scheduling algorithm in cloud computing. Journal of King Saud University-Computer and Information Sciences, 34(6), 2370–2382.
Ibrahim, I. M. (2021). Task scheduling algorithms in cloud computing: A review. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12(4), 1041–1053.
Rjoub, G., Bentahar, J., Abdel Wahab, O., and Saleh Bataineh, A. (2021). Deep and reinforcement learning for automated task scheduling in large-scale cloud computing systems. Concurrency and Computation: Practice and Experience, 33(23), e5919–e5930.
Zheng, W., Muthu, B., and Kadry, S. N. (2021). Research on the design of analytical communication and information model for teaching resources with cloud-sharing platform. Computer Applications in Engineering Education, 29(2), 359–369.
Wei, W., Yang, R., Gu, H., Zhao, W., Chen, C., and Wan, S. (2021). Multi-objective optimization for resource allocation in vehicular cloud computing networks. IEEE Transactions on Intelligent Transportation Systems, 23(12), 25536–25545.
Shu, W., Cai, K., and Xiong, N. N. (2021). Research on strong agile response task scheduling optimization enhancement with optimal resource usage in green cloud computing. Future Generation Computer Systems, 124(1), 12–20.
Ahmad, W., Rasool, A., Javed, A. R., Baker, T., and Jalil, Z. (2021). Cyber security in iot-based cloud computing: A comprehensive survey. Electronics, 11(1), 16–25.
Sriram, G. S. (2022). Edge computing vs. Cloud computing: an overview of big data challenges and opportunities for large enterprises. International Research Journal of Modernization in Engineering Technology and Science, 4(1), 1331–1337.
Murad, S. A., Muzahid, A. J. M., Azmi, Z. R. M., Hoque, M. I., and Kowsher, M. (2022). A review on job scheduling technique in cloud computing and priority rule based intelligent framework. Journal of King Saud University-Computer and Information Sciences, 34(6), 2309–2331.
Masdari, M., and Zangakani, M. (2020). Green cloud computing using proactive virtual machine placement: challenges and issues. Journal of Grid Computing, 18(4), 727–759.
Hsieh, S. Y., Liu, C. S., Buyya, R., and Zomaya, A. Y. (2020). Utilization-prediction-aware virtual machine consolidation approach for energy-efficient cloud data centers. Journal of Parallel and Distributed Computing, 139(1), 99–109.
