Observability Frameworks for Cloud-Native Healthcare Analytics
Keywords:
Data observability; Healthcare analytics; Cloud architecture; Data provenance; Distributed tracing; Multi-cloud; Hybrid cloud; Streaming data.Abstract
Cloud computing platforms for big data analytics, such as Amazon Web Services, Google Cloud Platform, or Microsoft Azure, have entered a mainstream growth phase, yet despite the enormous market demand, their effective operation remains a challenging task. Monitoring and observability of big data workloads needing support for dynamic and complex infrastructure represent an important area of research. Healthcare analytics platforms are particularly demanding in this respect, as workloads are commonly performing computations over patient data, which introduces additional requirements. During the last few years, several frameworks have been proposed to address various observability needs for cloud-based platforms, yet information on those approaches remains scattered. This survey work provides a structured overview of definitions, architectural patterns, frameworks, and tools, along with special focus on healthcare observability requirements. Furthermore, the discussion identifies potential areas for future investigation.
The cloud observability area is still evolving, making it necessary to review the state-of-the-art, identify gaps, and propose a research agenda. Foundation work covers an analysis of core concepts and architectural patterns for big data observability in the cloud, followed by the examination of requirements driven by healthcare workloads. These aspects have laid the groundwork for a survey of existing observability frameworks and tools tailored to cloud platforms, with special emphasis placed on monitoring telemetry collection, distributed tracing, and performance management. Finally, additional data management considerations are discussed before the identification of architectural patterns that address the specific observability demands from healthcare data processing workloads.
References
[1] Ahmad, S., & Al-Qutaish, R. E. (2022). Monitoring and observability in cloud-native microservices: A systematic mapping study. Journal of Cloud Computing, 11(1), 1–25.
[2] Al-Hashimi, M., Al-Sayyed, R., & Al-Shraideh, M. (2022). The impact of artificial intelligence on automating administrative and operational processes in healthcare. International Journal of Healthcare Technology and Management, 19(1/2), 45–60.
[3] Ali, O., Shrestha, A., Soar, J., & Wamba, S. F. (2022). Cloud computing-induced digital transformation in healthcare: A review and future research directions. International Journal of Information Management, 65, 102502.
[4] Bhardwaj, A., & Gupta, K. (2022). AIOps for cloud-native healthcare platforms: Enhancing reliability through automated observability. IEEE Transactions on Cloud Computing, 10(4), 2150–2163.
[5] Ciecierski-Holmes, T., Singh, R., Axt, M., & Kozlakidis, Z. (2022). Artificial intelligence for strengthening healthcare systems in low- and middle-income countries: A systematic scoping review. npj Digital Medicine, 5(1), 162.
[6] Dash, S., Shakyawar, S. K., Sharma, M., & Kaushik, S. (2022). Big data in healthcare: Management, analysis and future prospects. Journal of Big Data, 9(1), 1–25.
[7] Florea, R., & Radu, L. D. (2022). AIOps: The future of IT operations in big data environments. Applied Sciences, 12(9), 4567.
[8] Galety, M. G., Sagayam, K. M., & Abraham, A. (2022). Predictive monitoring in healthcare systems using AIOps and machine learning. Health and Technology, 12(4), 789–801.
[9] Haleem, A., Javaid, M., Singh, R. P., & Suman, R. (2022). Medical 4.0 technologies for healthcare: Features, capabilities, and applications. Internet of Things and Cyber-Physical Systems, 2, 12–30.
[10] Javaid, M., Haleem, A., Pratap Singh, R., & Suman, R. (2022). Enabling cloud computing technologies for healthcare: A review. Journal of Industrial Integration and Management, 7(03), 395–417.
[11] Kumar, A., & Singh, P. K. (2022). An observability framework for big data pipelines in cloud-based healthcare analytics. Computing, 104(8), 1821–1845.
[12] Ordibazar, A. H., & Al-Hashimi, M. (2022). Real-time monitoring of epidemic impacts on hospital operations using AIOps observability. Journal of Biomedical Informatics, 128, 104031.
[13] Patel, R., & Samad, A. (2022). Contextualized real-time data for AIOps observability in the healthcare industry. International Journal of Computing and Digital Systems, 11(1), 445–458.
[14] Periasamy, J., & Wang, Y. (2022). Optimizing patient data through AIOps: A framework for precise clinical diagnosis. Frontiers in Public Health, 10, 895123.
[15] Qadri, Y. A., Nauman, A., Zikria, Y. B., & Kim, S. W. (2022). The future of healthcare Internet of Things: A survey of emerging technologies and applications. IEEE Communications Surveys & Tutorials, 22(2), 1121–1167.
[16] Rivera, M., & Florea, R. (2021). Building resilient observability frameworks for big data in cloud ecosystems. Software: Practice and Experience, 51(12), 2410–2432.
[17] Wang, L., & Zhang, Y. (2022). Cloud-native observability for healthcare big data: Challenges and opportunities. Journal of Systems Architecture, 124, 102421.
[18] Zahid, H., & Periasamy, J. (2022). Remote healthcare transformation through digital provider applications and AIOps monitoring. Medical Education Online, 27(1), 204561.
[19] Alhayani, B., Abbas, S. T., & Mohammed, H. J. (2022). Intelligent automation of electronic health records using cloud-based AI frameworks. Journal of Medical Systems, 46(3), 112–125.
Additional Files
Published
Data Availability Statement
None
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.
