Abstract
Cloud enterprises are currently facing difficulties managing the enormous amount of data and varied resources in the cloud because of the explosive expansion of the cloud computing system with numerous clients, ranging from small business owners to large corporations. Cloud computing’s performance may need more effective resource planning. Resources must be distributed equally among all relevant stakeholders to maintain the group’s profit and the satisfaction of its consumers. Since these essential resources are unavailable on the board, a client request cannot be put on hold forever. To address these issues, a hybrid machine learning technique for resource allocation security with effective task scheduling in cloud computing is proposed in this study. Initially, a short scheduler for tasks built around the enhanced Particle Swarm Optimization algorithm (IPSO-TS) reduces make-span time and increases throughput. Next, bandwidth and resource load are included in a Graph Attention Neural Network (GANN) for effective resource allocation under various design limitations. Finally, NSUPREME, a simple identification technique, is suggested for the encryption process to secure data storage. The proposed method is finally simulated using various simulation settings to demonstrate its effectiveness, and the outcomes are contrasted with those of cutting-edge approaches. The findings indicate that the suggested plan is more efficient than the current one regarding resource use, power usage, responsiveness, etc.
Similar content being viewed by others
Data Availability
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
References
Adil, M., Nabi, S., Aleem, M., Diaz, V.G., Lin, J.C.-W.: Ca-mlbs: content-aware machine learning based load balancing scheduler in the cloud environment. Expert. Syst. 40(4), e13150 (2023)
Al-Rahayfeh, A., Atiewi, S., Abuhussein, A., Almiani, M.: Novel approach to task scheduling and load balancing using the dominant sequence and mean shift clustering algorithms. Future Internet 11, 5 (2019)
Buyya, R., Yeo, C.S., Venugopal, S., Broberg, J., Brandic, I.: Cloud computing and emerging it platforms: Vision, hype, and reality for delivering computing as the 5th utility. Futur. Gener. Comput. Syst. 25(6), 599–616 (2022)
Li, B., Zhou, X., Ning, Z., Guan, X., Yiu, K.: C, Dynamic event-triggered security control for networked control systems with cyber-attacks: A model predictive control approach. Inf. Sci. 612, 384–398 (2022)
Chaisiri, S., Lee, B.-S., Niyato, D.: Optimizing resource provisioning cost in cloud computing. IEEE Trans. Serv. Comput. 5(2), 164–177 (2020)
Chandakkar, P.S., Li, Y., Ding, P.L.K., Li, B.: Strategies for re-training a pruned neural network in an edge computing paradigm. In 2017 IEEE International Conference on Edge Computing (EDGE) 244–247 (2017)
Liu, C., Wu, T., Li, Z., Ma, T., Huang, J.: Robust Online Tensor Completion for IoT Streaming Data Recovery. IEEE Transactions on Neural Networks and Learning Systems (2022)
Endo, P.T., de Almeida Palhares, A.V., Pereira, N.N., Goncalves, G.E., Sadok, D., Kelner, J., Melander, B., Mangs, J.-E.: Resource allocation for the distributed cloud: concepts and research challenges. IEEE Network 25(4), 42–46 (2021)
Hasan Shuvo, M.N., Shahriar Maswood, M.M., Alharbi, A.G.: Lsru: A novel deep learning based hybrid method to predict the workload of virtual machines in the cloud data center. In 2020 IEEE Region 10 Symposium (TENSYMP) 1604–1607 (2020)
Zhao, Z., Xu, G., Zhang, N., Zhang, Q.: Performance analysis of the hybrid satellite-terrestrial relay network with opportunistic scheduling over generalized fading channels. IEEE Trans. Veh. Technol. 71(3), 2914–2924 (2022)
Zhou, X., Zhang, L.: SA-FPN: An effective feature pyramid network for crowded human detection. Appl. Intell. 52(11), 12556–12568 (2022)
Khan, W., Ahmed, E., Hakak, S., Yaqoob, I., Ahmed, A.: Edge computing: A survey. Future Generation Computer Systems 97 (02 2019)
Linthicum, D.S.: Connecting fog and cloud computing. IEEE Cloud Computing 4(2), 18–20 (2023)
Liang, X., Huang, Z., Yang, S., Qiu, L.: Device-Free Motion & Trajectory Detection via RFID. ACM Trans. Embed. Comput. Syst. 17(4), 78 (2018)
Lu, H., Zhu, Y., Yin, M., Yin, G., Xie, L.: Multimodal Fusion Convolutional Neural Network With Cross-Attention Mechanism for Internal Defect Detection of Magnetic Tile. IEEE Access 10, 60876–60886 (2022)
Muniswamy, S., Vignesh, R.: Dsts: A hybrid optimal and deep learning for dynamic scalable task scheduling on container cloud environment. Journal of Cloud Computing 11(1), 33 (2022)
Nanjappan, M., Albert, P.: Hybrid-based novel approach for resource scheduling using mcfcm and pso in a cloud computing environment. Concurrency and Computation: Practice and Experience 34 (09 2019)
Paul, P.K., Ghose, M.K.: Cloud computing: Possibilities, challenges, and opportunities with particular reference to its emerging need in the academic and working area of information science. Procedia Engineering 38 (2020), 2222–2227. International Conference on Modelling Optimization and Computing
Zhang, X., Huang, D., Li, H., Zhang, Y., Xia, Y., Liu, J.: Self-training maximum classifier discrepancy for EEG emotion recognition. CAAI Transactions on Intelligence Technology, (2023)
Praveen, S.P., Ghasempoor, H., Shahabi, N., Izanloo, F.: A hybrid gravitational emulation local search-based algorithm for task scheduling in cloud computing. Math. Probl. Eng. 2023, 6516482 (2023)
Li, Q., Lin, H., Tan, X., Du, S.: Consensus for Multiagent-Based Supply Chain Systems Under Switching Topology and Uncertain Demands. IEEE Transactions on Systems, Man, and Cybernetics: Systems 50(12), 4905–4918 (2020)
Li, T., Fan, Y., Li, Y., Tarkoma, S., Hui, P.: Understanding the Long-Term Evolution of Mobile App Usage. IEEE Trans. Mob. Comput. 22(2), 1213–1230 (2023)
Xie, Y., Wang, X., Shen, Z., Sheng, Y., Wu, G.A.: Two-stage Estimation of Distribution Algorithm with Heuristics for Energy-aware Cloud Workflow Scheduling. IEEE Transactions on Services Computing, (2023)
Li, X., Sun, Y.: Stock intelligent investment strategy based on support vector machine parameter optimization algorithm. Neural Computing and Applications 32(6), 1765–1775 (2020)
Yan, H., Diao, X.-c., Li, G.: Design and implementation of the uncertain resource objects in the network resource management. In 2008 International Seminar on Future Information Technology and Management Engineering 582–585 (2020)
Cao, B., Sun, Z., Zhang, J., Gu, Y.: Resource Allocation in 5G IoV Architecture Based on SDN and Fog-Cloud Computing. IEEE transactions on intelligent transportation systems 22(6), 3832–3840 (2021)
Dai, X., Xiao, Z., Jiang, H., Alazab, M., Lui, J.C.S., Min, G., Liu, J.: Task Offloading for Cloud-Assisted Fog Computing With Dynamic Service Caching in Enterprise Management Systems. IEEE Transactions on Industrial Informatics 19(1), 662–672 (2023)
Jiang, H., Dai, X., Xiao, Z., Iyengar, A.K.: Joint Task Offloading and Resource Allocation for Energy-Constrained Mobile Edge Computing. IEEE Transactions on Mobile Computing, (2022)
Li, J., Deng, Y., Sun, W., Li, W., Li, R., Li, Q.,... Liu, Z.: Resource Orchestration of Cloud-Edge–Based Smart Grid Fault Detection. ACM Trans. Sen. Netw., 18(3), 2022
Cao, B., Zhang, J., Liu, X., Sun, Z., Cao, W., Nowak, R.M., Lv, Z.: Edge-Cloud Resource Scheduling in Space–Air–Ground-Integrated Networks for Internet of Vehicles. IEEE Internet of Things Journal 9(8), 5765–5772 (2022)
Wu, Z., Cao, J., Wang, Y., Wang, Y., Zhang, L.,... Wu, J.: hPSD: A Hybrid PU-Learning-Based Spammer Detection Model for Product Reviews. IEEE transactions on cybernetics, 50(4), 1595–1606 (2020)
Zhang, J., Liu, Y., Li, Z., Lu, Y.: Forecast-Assisted Service Function Chain Dynamic Deployment for SDN/NFV-Enabled Cloud Management Systems. IEEE Systems Journal, (2023)
Ni, Q., Guo, J., Wu, W., Wang, H., Wu, J.: Continuous Influence-Based Community Partition for Social Networks. IEEE Transactions on Network Science and Engineering 9(3), 1187–1197 (2022)
Liao, Q., Chai, H., Han, H., Zhang, X., Wang, X., Xia, W., Ding, Y.: An Integrated Multi-Task Model for Fake News Detection. IEEE Transactions on Knowledge and Data Engineering 34(11), 5154–5165 (2022)
Chen, P., Liu, H., Xin, R., Carval, T., Zhao, J., Xia, Y., Zhao, Z.: Effectively Detecting Operational Anomalies In Large-Scale IoT Data Infrastructures By Using A GAN-Based Predictive Model. The Computer Journal 65(11), 2909–2925 (2022)
Li, C., Dong, M., Xin, X., Li, J., Chen, X.,... Ota, K.: Efficient Privacy-preserving in IoMT with Blockchain and Lightweight Secret Sharing. IEEE Internet of Things Journal, (2023)
Zhang, H., Mi, Y., Fu, Y., Liu, X., Zhang, Y., Wang, J.,... Tan, J.: Security defense decision method based on potential differential game for complex networks. Computers & Security, 129, 103187 (2023)
Cheng, B., Wang, M., Zhao, S., Zhai, Z., Zhu, D., Chen, J.: Situation-Aware Dynamic Service Coordination in an IoT Environment. IEEE/ACM Transactions on Networking 25(4), 2082–2095 (2017)
Min, C., Pan, Y., Dai, W., Kawsar, I., Li, Z.,... Wang, G.: Trajectory optimization of an electric vehicle with minimum energy consumption using inverse dynamics model and servo constraints. Mechanism and Machine Theory, 181, 105185 (2023)
Funding
This research received no specific grant from any funding agency.
Author information
Authors and Affiliations
Contributions
Conceptualization, Methodology, Formal analysis, Investigation, Data Curation, Validation, Resources, Supervision, Writing—original draft, Writing—review & editing.
Corresponding author
Ethics declarations
Ethics Approval
Not applicable.
Consent for Publication
Not applicable.
Competing Interests
The authors declare no competing interests.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Zhou, H. A Novel Approach to Cloud Resource Management: Hybrid Machine Learning and Task Scheduling. J Grid Computing 21, 68 (2023). https://doi.org/10.1007/s10723-023-09702-w
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s10723-023-09702-w