2692-2967ISSN (Online)
2770-0216ISSN (Print)
Volume 12 , Issue 1 , PP: 01-14, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Anil Audumbar Pise 1 *
Doi: https://doi.org/10.54216/AJBOR.120101
The fault tolerance study carried out in this research explores Bidirectional Long Short-Term Memory (LSTM) and Generative Adversarial Networks (GAN) to improve cloud computing dependability and functionality. Being an integral part of the rage for business operations, cloud-computing fundamentals of resource provisioning and fault tolerance have a bearing on the overall cost-dynamics, ROI and OpEx. Reliability covers such issues as hardware failures, configuration problems and other network issues that may have financial implications and even lead to revenue loss, and failure to meet service level agreement (SLA). The work develops a novel GAN-BiLSTM model for the accurate prediction of faults and the enhancement of recovery management, resulting in resource efficiency and cost of capital reduction (CapEx). Evaluation criteria involve deadline guarantee ratio, average task delay, and system scalability, confirming that the proposed model has better financial performance than DPSO and ANFIS. Cutting wastage of resources and increasing energy capacity in a system, the model displays attractive cost reduction and operating effectiveness for cloud service providers. In the simulation, important results of the model are demonstrated in the business continuity, financial risk reduction as well as maintaining accurate and resourceful service in high demand situations. All these developments have placed the fault tolerant systems powered by machine learning as indispensable instruments that can also enhance profitability, resources utilisation and sustainable competitiveness in the cloud computing business.
Virtual Machines , GAN , BiLSTM , Cost reduction , Return on investment , Operational expenditure , Risk mitigation , Service-level agreement , Deep learning , Virtual machine migration
[1] X. Zhou, G. Zhang, J. Sun, J. Zhou, T. Wei, and S. Hu, ‘‘Minimizing cost and makespan for workflow scheduling in cloud using fuzzy dominance sort-based HEFT,’’ Future Gener. Comput. Syst., vol. 93, pp. 278–289, Apr. 2019, doi: 10.1016/j.future.2018.10.046.
[2] M. Zhao, Z. Han, and X. Du, ‘‘A survey of data center network topology structure,’’ in Proc. 25th Int. Conf. Adv. Commun. Technol. (ICACT), Feb. 2023, pp. 303–309.
[3] C. Acevedo, P. Hernández, A. Espinosa, and V. Mendez, ‘‘a data aware MultiWorkflow scheduler for clusters on WorkflowSim,’’ in Proc. 2nd Int. Conf. Complex., Future Inf. Syst. Risk, 2017, pp. 79–86, doi: 10.5220/0006303500790086. [4] V. Roy, S. Shukla, “Designing Efficient Blind Source Separation Methods for EEG Motion Artifact Removal Based on Statistical Evaluation,” Wireless Pers Commun 108, pp. 1311–1327 (2019). https://doi.org/10.1007/s11277-019-06470-3.
[5] A. Sariga , J. Uthayakumar, Type 2 Fuzzy Logic based Unequal Clustering algorithm for multi-hop wireless sensor networks, International Journal of Wireless and Ad Hoc Communication, Vol. 1 , No. 1 , (2020) : 33-46 (Doi : https://doi.org/10.54216/IJWAC.010102)
[6] Irina V. Pustokhina, Blockchain technology in the international supply chains, International Journal of Wireless and Ad Hoc Communication, Vol. 1 , No. 1 , (2020) : 16-25 (Doi : https://doi.org/10.54216/IJWAC.010103)
[7] I. Casas, J. Taheri, R. Ranjan, L. Wang, and A. Y. Zomaya, ‘‘A balanced scheduler with data reuse and replication for scientific workflows in cloud computing systems,’’ Future Gener. Comput. Syst., vol. 74, pp. 168–178, Sep. 2017, doi: 10.1016/j.future.2015.12.005.
[8] U. K. Jena, P. K. Das, and M. R. Kabat, ‘‘Hybridization of meta-heuristic algorithm for load balancing in cloud computing environment,’’ J. King Saud Univ., Comput. Inf. Sci., vol. 34, no. 6, pp. 2332–2342, Jun. 2022.
[9] S. Sekigawa, C. Sasaki, and A. Tagami, ‘‘toward a cloud-native telecom infrastructure: Analysis and evaluations of kubernetes networking,’’ in Proc. IEEE Globecom Workshops (GC Wkshps), Dec. 2022, pp. 838–843.
[10] Mrs.K.Kiruthika , Ms.S.Gayathri , Ms.R.Hemalatha , Ms.P.Menaga, Design and Development of Mobile Healthcare Application for “Ayurvedic” based Clinical Documents, Journal of Cognitive Human-Computer Interaction, Vol. 1 , No. 1 , (2021) : 18-27 (Doi : https://doi.org/10.54216/JCHCI.010103)
[11] Ajay G , Abhishek Kumar , Venkatesan R, Query-Based Image Retrieval using Support Vector Machine (SVM), Journal of Cognitive Human-Computer Interaction, Vol. 1 , No. 1 , (2021) : 28-36 (Doi : https://doi.org/10.54216/JCHCI.010104)
[12] M. Ghasemzadeh, H. Arabnejad, and J. G. Barbosa, ‘‘Deadline-budget constrained scheduling algorithm for scientific workflows in a cloud environment,’’ in Proc. Leibniz Int. Informat., 2017, vol. 70, no. 19, pp. 19.1–19.16, doi: 10.4230/LIPIcs.OPODIS.2016.19. [13] V. Roy, S. Shukla. “Effective EEG Motion Artifacts Elimination Based on Comparative Interpolation Analysis.” Wireless Pers Commun 97, 6441–6451 (2017). https://doi.org/10.1007/s11277-017-4846-3.
[14] Hisham Elhoseny , Hazem EL-Bakry, Utilizing Service Oriented Architecture (SOA) in IoT Smart Applications, Journal of Cybersecurity and Information Management, Vol. 0 , No. 1 , (2019) : 15-31 (Doi : https://doi.org/10.54216/JCIM.000102)
[15] Andino Maseleno, Design of Optimal Machine Learning based Cybersecurity Intrusion Detection Systems, Journal of Cybersecurity and Information Management, Vol. 0 , No. 1 , (2019) : 32-43 (Doi : https://doi.org/10.54216/JCIM.000103)
[16] Z. Ahmad, B. Nazir, and A. Umer, ‘‘A fault-tolerant workflow management system with quality-of-service-aware scheduling for scientific workflows in cloud computing,’’ Int. J. Commun. Syst., vol. 34, no. 1, pp. 1–23, 2021, doi: 10.1002/dac.4649.
[17] P. Banerjee, S. Roy, A. Sinha, M. M. Hassan, S. Burje, A. Agrawal, A. K. Bairagi, S. Alshathri, and W. El-Shafai, ‘‘MTD-DHJS: Makespan-optimized task scheduling algorithm for cloud computing with dynamic computational time prediction,’’ IEEE Access, vol. 11, pp. 105578–105618, 2023
[18] S. Basu, M. Karuppiah, K. Selvakumar, K.-C. Li, S. K. H. Islam, M. M. Hassan, and M. Z. A. Bhuiyan, ‘‘an intelligent/cognitive model of task scheduling for IoT applications in cloud computing environment,’’ Future Gener. Comput. Syst., vol. 88, pp. 254–261, Nov. 2018, doi: 10.1016/j.future.2018.05.056.
[19] N. Singh, Y. Hamid, S. Juneja, G. Srivastava, G. Dhiman, T. R. Gadekallu, and M. A. Shah, ‘‘Load balancing and service discovery using Docker swarm for microservice based big data applications,’’ J. Cloud Comput., vol. 12, no. 1, pp. 1–9, Jan. 2023.
[20] V. Roy et al., “Network Physical Address Based Encryption Technique Using Digital Logic”, International Journal of Scientific & Technology Research, Vol. 9, No. 4, 2020, Pp no.- 3119-3122.
[21] K. Vinay, S. M. D. Kumar, S. Raghavendra, and K. R. Venugopal, ‘‘Cost and fault-tolerant aware resource management for scientific workflows using hybrid instances on clouds,’’ Multimedia Tools Appl., vol. 77, no. 8, pp. 10171–10193, Apr. 2018, doi: 10.1007/s11042-017-5304-7.
[22] H. Arabnejad, C. Pahl, G. Estrada, A. Samir, and F. Fowley, ‘‘A fuzzy load balancer for adaptive fault tolerance management in cloud platforms,’’ in Proc. Eur. Conf. Service-Oriented Cloud Comput. Cham, Switzerland: Springer, 2017, pp. 109–124. [23] S. Shukla, V. Roy and A. Prakash, "Wavelet Based Empirical Approach to Mitigate the Effect of Motion Artifacts from EEG Signal," 2020 IEEE 9th International Conference on Communication Systems and Network Technologies (CSNT), Gwalior, India, 2020, pp. 323-326, doi: 10.1109/CSNT48778.2020.9115761.
[24] J. Shah and D. Dubaria, ‘‘Building modern clouds: Using Docker, Kubernetes & Google cloud platform,’’ in Proc. IEEE 9th Annu. Comput. Commun. Workshop Conf. (CCWC), Jan. 2019, pp. 184–189.
[25] B. CAI, B. Wang, M. Yang, and Q. Guo, ‘‘AutoMan: Resource-efficient provisioning with tail latency guarantees for microservices,’’ Future Gener. Comput. Syst., vol. 143, pp. 61–75, Jun. 2023.
[26] Lobna Osman, Olutosin Taiwo, Ahmed Elashry, Absalom E. Ezugwu, Intelligent Edge Computing for IoT: Enhancing Security and Privacy, Journal of Intelligent Systems and Internet of Things, Vol. 8 , No. 1 , (2023) : 55-65 (Doi : https://doi.org/10.54216/JISIoT.080105)
[27] Ossama H. Embarak, Raed Abu Zitar, Securing Wireless Sensor Networks Against DoS attacks in Industrial 4.0, Journal of Intelligent Systems and Internet of Things, Vol. 8 , No. 1 , (2023) : 66-74 (Doi : https://doi.org/10.54216/JISIoT.080106)
