An In-Depth Review to Explore Cost Optimization Strategies for Healthcare Domain in Cloud Computing
Keywords:
healthcare, categorization, Internet of Things, innovative, Information TechnologyAbstract
Cloud Computing (CC) is a technological innovation that enables the provision of computing ability and data storage in a dynamic and flexible manner via pay-as-you-go services using the Internet. This technology has significantly advanced the field of Information Technology (IT). Over the recent years, the progression of cloud computing is increased to the emergence of novel technologies, including fog computing, edge computing, and cloud federation. However, the advent of the Internet of Things (IoT) has introduced various challenges associated with these innovative technologies. Hence, this manuscript delves into an examination of each of these evolving cloud-oriented technologies, encompassing their architectures, prospects, and challenges. The objective of this study is to assess the issue of cost optimization in healthcare (HC) by conducting a thorough survey of existing approaches in cloud computing. The paper aims to present a comprehensive classification of the aspects and parameters related to cost optimization in HC. Additionally, it offers a categorization of cost-based metrics, distinguishing between monetary and temporal cost parameters across various scheduling stages. The intention is to provide valuable insights for researchers and practitioners, aiding them in the selection of the most suitable cost optimization approach based on identified aspects and parameters. Furthermore, the paper outlines potential avenues for future research in this ongoing and evolving research domain.
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Channi, H. K., Sandhu, R., Faiz, M., & Islam, S. M. (2023, August). Multi-Criteria Decision-Making Approach for Laptop Selection: A Case Study. In 2023 3rd Asian Conference on Innovation in Technology (ASIANCON) (pp. 1-5). IEEE.
Faiz, M., & Daniel, A. K. (2023). A hybrid WSN based two-stage model for data collection and forecasting water consumption in metropolitan areas. International Journal of Nanotechnology, 20(5-10), 851-879.
Narayan, V., Faiz, M., Mall, P. K., & Srivastava, S. (2023). A Comprehensive Review of Various Approach for Medical Image Segmentation and Disease Prediction. Wireless Personal Communications, 132(3), 1819-1848.
Saxena, A., Chauhan, R., Chauhan, D., Sharma, S., Sharma, D., & Narayan, V. (2022). Comparative Analysis Of AI Regression And Classification Models For Predicting House Damages İn Nepal: Proposed Architectures And Techniques. Journal of Pharmaceutical Negative Results, 6203-6215.
Narayan, V., Awasthi, S., Fatima, N., Faiz, M., Bordoloi, D., Sandhu, R., & Srivastava, S. (2023, May). Severity of Lumpy Disease detection based on Deep Learning Technique. In 2023 International Conference on Disruptive Technologies (ICDT) (pp. 507-512). IEEE.
Mall, P. K., Narayan, V., Srivastava, S., Sabarwal, M., Kumar, V., Awasthi, S., & Tyagi, L. (2023). Rank Based Two Stage Semi-Supervised Deep Learning Model for X-Ray Images Classification.
Chaturvedi, P., Daniel, A. K., & Narayan, V. (2021). Coverage Prediction for Target Coverage in WSN Using Machine Learning Approaches.
Narayan, V., Daniel, A. K., & Chaturvedi, P. (2023). E-FEERP: Enhanced Fuzzy based Energy Efficient Routing Protocol for Wireless Sensor Network. Wireless Personal Communications, 1-28.
Danthuluri, S., & Chitnis, S. (2023). Energy and cost optimization mechanism for workflow scheduling in the cloud. Materials Today: Proceedings, 80, 3069-3074.
Kamanga, C. T., Bugingo, E., Badibanga, S. N., & Mukendi, E. M. (2023). A multi-criteria decision making heuristic for workflow scheduling in cloud computing environment. The Journal of Supercomputing, 79(1), 243-264.
Godhrawala, H., & Sridaran, R. (2023). A dynamic Stackelberg game based multi-objective approach for effective resource allocation in cloud computing. International Journal of Information Technology, 15(2), 803-818.
Malathi, K., & Priyadarsini, K. (2023). Hybrid lion–GA optimization algorithm-based task scheduling approach in cloud computing. Applied Nanoscience, 13(3), 2601-2610.
Mohamed, A. A., Abdellatif, A. D., Alburaikan, A., Khalifa, H. A. E. W., Elaziz, M. A., Abualigah, L., & AbdelMouty, A. M. (2023). A novel hybrid arithmetic optimization algorithm and salp swarm algorithm for data placement in cloud computing. Soft Computing, 27(9), 5769-5780.
Tuli, S., Casale, G., & Jennings, N. R. (2023). Learning to Dynamically Select Cost Optimal Schedulers in Cloud Computing Environments. ACM SIGMETRICS Performance Evaluation Review, 50(4), 29-31.
Asghari, S., & Jafari Navimipour, N. (2023). The role of an ant colony optimisation algorithm in solving the major issues of the cloud computing. Journal of Experimental & Theoretical Artificial Intelligence, 35(6), 755-790.
Jeyaraj, R., Balasubramaniam, A., MA, A. K., Guizani, N., & Paul, A. (2023). Resource management in cloud and cloud-influenced technologies for internet of things applications. ACM Computing Surveys, 55(12), 1-37.
Xu, M., Song, C., Wu, H., Gill, S. S., Ye, K., & Xu, C. (2022). esDNN: deep neural network based multivariate workload prediction in cloud computing environments. ACM Transactions on Internet Technology (TOIT), 22(3), 1-24.
Kruekaew, B., & Kimpan, W. (2022). Multi-objective task scheduling optimization for load balancing in cloud computing environment using hybrid artificial bee colony algorithm with reinforcement learning. IEEE Access, 10, 17803-17818.
Li, C., Bai, J., Chen, Y., & Luo, Y. (2020). Resource and replica management strategy for optimizing financial cost and user experience in edge cloud computing system. Information Sciences, 516, 33-55.
Faiz, M., & Daniel, A. K. (2022). A Multi-Criteria Dual Membership Cloud Selection Model based on Fuzzy Logic for QoS. International Journal of Computing and Digital Systems, 12(1), 453-467.
Narayan, Vipul, et al. "7 Extracting business methodology: using artificial intelligence-based method." Semantic Intelligent Computing and Applications 16 (2023): 123
Narayan, Vipul, et al. "A Comprehensive Review of Various Approach for Medical Image Segmentation and Disease Prediction." Wireless Personal Communications 132.3 (2023): 1819-1848.
Mall, Pawan Kumar, et al. "Rank Based Two Stage Semi-Supervised Deep Learning Model for X-Ray Images Classification: AN APPROACH TOWARD TAGGING UNLABELED MEDICAL DATASET." Journal of Scientific & Industrial Research (JSIR) 82.08 (2023): 818-830.
Narayan, Vipul, et al. "Severity of Lumpy Disease detection based on Deep Learning Technique." 2023 International Conference on Disruptive Technologies (ICDT). IEEE, 2023.
Saxena, Aditya, et al. "Comparative Analysis Of AI Regression And Classification Models For Predicting House Damages İn Nepal: Proposed Architectures And Techniques." Journal of Pharmaceutical Negative Results (2022): 6203-6215.
Kumar, Vaibhav, et al. "A Machine Learning Approach For Predicting Onset And Progression"“Towards Early Detection Of Chronic Diseases “." Journal of Pharmaceutical Negative Results (2022): 6195-6202.
Chaturvedi, Pooja, Ajai Kumar Daniel, and Vipul Narayan. "Coverage Prediction for Target Coverage in WSN Using Machine Learning Approaches." (2021).
Chaturvedi, Pooja, A. K. Daniel, and Vipul Narayan. "A Novel Heuristic for Maximizing Lifetime of Target Coverage in Wireless Sensor Networks." Advanced Wireless Communication and Sensor Networks. Chapman and Hall/CRC 227-242.
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