Enhancing Healthcare Enterprise Cloud Efficiency with Advanced Balancing and Control Systems
Keywords:
Enterprise Cloud, Efficiency, Balancing Control Systems, Cloud Optimization, Automation, Cloud Management, Data Integrity, Workflow Optimization, Scalability, Resource Utilization, Operational Compliance, Performance Metrics, Cloud Infrastructure, Digital InnovationAbstract
In the modern healthcare enterprise landscape, achieving optimal cloud efficiency is paramount for managing extensive volumes of data and complex operations effortlessly. This study investigates the advancement of an innovative balancing and control system specifically designed to automate and enhance cloud-based healthcare workflows, ensuring smooth operations and optimized resource utilization. By implementing a comprehensive framework that enables proactive monitoring and control, our solution proficiently identifies and rectifies data inconsistencies, bolstering data integrity while significantly reducing latency.
The system leverages strategic automation methodologies to manage and optimize thousands of concurrent healthcare operations, fostering scalability and resilience amid evolving industry demands. Our approach markedly improves critical performance metrics such as operational compliance and data processing speeds, thereby establishing a robust foundation for efficient and reliable cloud management in the healthcare sector. The results indicate that integrating advanced balancing and control systems within existing cloud infrastructures not only enhances efficiency but also aligns operational processes with the agility and growth objectives of healthcare enterprises.
This framework represents a pivotal shift towards more intelligent and automated healthcare cloud management strategies, setting the stage for sustained innovation and efficiency in the digital era of healthcare. By emphasizing the importance of balance and controls, this study underscores a strategic path forward for enterprise efficiency and adaptability amidst rapid technological advancements.
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Fantom, W., Davies, E., Rotsos, C., Veitch, P., Cassidy, S., & Race, N. (2023). Nes: towards lifecycle automation for emulation-based experimentation.https://doi.org/10.1109/noms56928.2023.10154268
Mustafiz, S., Hassane, O., Dupont, G., Khendek, F., & Toeroe, M. (2020). Model-driven process enactment for nfv systems with maple. Software & Systems Modeling, 19(5), 1263-1282. https://doi.org/10.1007/s10270-020-00783-9
Rayaprolu, R. (2024). Intelligent resource management in cloud computing: ai techniques for optimizing devops operations. JAIGS, 6(1), 397-408. https://doi.org/10.60087/jaigs.v6i1.262
Rios, F. and Ly, C. (2021). Implementing and managing a data curation workflow in the cloud. Journal of Escience Librarianship, 10(3). https://doi.org/10.7191/jeslib.2021.1205
Sriperambuduri, V. (2023). A hybrid grey wolf optimization and constriction factor based pso algorithm for workflow scheduling in cloud. International Journal on Recent and Innovation Trends in Computing and Communication, 11(9s), 718-726. https://doi.org/10.17762/ijritcc.v11i9s.7744
Ahmad, Z., Nazir, B., & Umer, A. (2020). A fault‐tolerant workflow management system with quality‐of‐service‐aware scheduling for scientific workflows in cloud computing. International Journal of Communication Systems, 34(1). https://doi.org/10.1002/dac.4649
Al-Haboobi, A. and Kecskemeti, G. (2021). Execution time reduction in function oriented scientific workflows. Acta Cybernetica, 25(2), 131-150. https://doi.org/10.14232/actacyb.288489
Giustizia, J. (2024). Maloja: simple and scalable snakemake workflow orchestration in the cloud.. https://doi.org/10.1101/2024.06.28.601236
Kassabi, H., Serhani, M., Dssouli, R., & Navaz, A. (2019). Trust enforcement through self-adapting cloud workflow orchestration. Future Generation Computer Systems, 97, 462-481. https://doi.org/10.1016/j.future.2019.03.004
Kassabi, H., Serhani, M., Masud, M., Shuaib, K., & Khalil, K. (2023). Deep learning approach to security enforcement in cloud workflow orchestration. Journal of Cloud Computing Advances Systems and Applications, 12(1). https://doi.org/10.1186/s13677-022-00387-2
Medeiros, D. (2023). A gpu-accelerated molecular docking workflow with kubernetes and apache airflow., 193-206. https://doi.org/10.1007/978-3-031-40843-4_15
Rayaprolu, R. (2024). Intelligent resource management in cloud computing: ai techniques for optimizing devops operations. JAIGS, 6(1), 397-408. https://doi.org/10.60087/jaigs.v6i1.262
Rios, F. and Ly, C. (2021). Implementing and managing a data curation workflow in the cloud. Journal of Escience Librarianship, 10(3). https://doi.org/10.7191/jeslib.2021.1205
Sangani, S. and Patil, R. (2023). Reliable and efficient webserver management for task scheduling in edge-cloud platform. International Journal of Electrical and Computer Engineering (Ijece), 13(5), 5922. https://doi.org/10.11591/ijece.v13i5.pp5922-5931
Zhou, X., Zhang, G., Sun, J., Zhou, J., Wei, T., & Hu, S. (2019). Minimizing cost and makespan for workflow scheduling in cloud using fuzzy dominance sort based heft. Future Generation Computer Systems, 93, 278-289. https://doi.org/10.1016/j.future.2018.10.046
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