Intelligent Cost Optimization for EMR Workloads using Custom APIs
DOI:
https://doi.org/10.53555/ephijse.v10i2.301Keywords:
Intelligent Cost Optimization, EMR Workloads, Custom APIsAbstract
Given the rising demand for big data processing and the current dynamic economic environment, which presents challenges for businesses, especially in terms of managing cloud computing costs has become a large issue. Although cloud infrastructure provides scalability and adaptability, poor management of it could cause significant expenses. Large data set processing inside distributed computing systems largely relies on Amazon Elastic MapReduce (EMR). Although Electronic Medical Records in the healthcare sector and other businesses managing large amounts of data might find EMS suitable, its dynamic and scalable capabilities could potentially lead to cost inefficiencies. Inappropriate scaling or too generous resource allocation can lead to resource waste and higher running expenses. This white paper examines cutting-edge cost-optimal methods designed especially for E MR workload management. Electronic Medical Records (EMRs) are confidential patient records requiring efficient, safe, reliable processing—qualities lacking in which case significant charges could arise. The study stresses on a simple approach the usage of tailored application programming interfaces (APIs). These APIs let one automate important chores such dynamic job scheduling, real-time instance selection, autonomous scaling, and ongoing cost monitoring. By means of automation, businesses may guarantee that computing resources are distributed precisely where and when needed, therefore avoiding the inefficiencies connected with set configurations. Dynamic work scheduling distributes tasks depending on real-time data, therefore optimizing resource use all day. Companies can identify the most reasonably priced and task-appropriate computer instances by means of selective instance selection, therefore avoiding a homogeneous approach that might result in inefficiencies or insufficient performance. By allowing systems to dynamically change resources in response to various demands, autonomous scaling guarantees performance while eliminating needless resource allocation and so maximizes efficiency.
References
Mishra, Sakti. Simplify Big Data Analytics with Amazon EMR: A beginner's guide to learning and implementing Amazon EMR for building data analytics solutions. Packt Publishing Ltd, 2022.
Cohen, Raphael Y., and Vesela P. Kovacheva. "A methodology for a scalable, collaborative, and resource-efficient platform, MERLIN, to facilitate healthcare AI research." IEEE journal of biomedical and health informatics 27.6 (2023): 3014-3025.
Kupunarapu, Sujith Kumar. "AI-Enhanced Rail Network Optimization: Dynamic Route Planning and Traffic Flow Management." International Journal of Science And Engineering 7.3 (2021): 87-95.
Ta-Shma, Paula, et al. "An ingestion and analytics architecture for iot applied to smart city use cases." IEEE Internet of Things Journal 5.2 (2017): 765-774.
Mehdi Syed, Ali Asghar, and Erik Anazagasty. “AI-Driven Infrastructure Automation: Leveraging AI and ML for Self-Healing and Auto-Scaling Cloud Environments”. International Journal of Artificial Intelligence, Data Science, and Machine Learning, vol. 5, no. 1, Mar. 2024, pp. 32-43
Kiran, Mariam, et al. "Lambda architecture for cost-effective batch and speed big data processing." 2015 IEEE international conference on big data (big data). IEEE, 2015.
Kaur, Jagreet, and Kulwinder Singh Mann. "AI based healthcare platform for real time, predictive and prescriptive analytics using reactive programming." Journal of Physics: Conference Series. Vol. 933. No. 1. IOP Publishing, 2017.
Kupunarapu, Sujith Kumar. "Data Fusion and Real-Time Analytics: Elevating Signal Integrity and Rail System Resilience." International Journal of Science And Engineering 9.1 (2023): 53-61.
Vasanta Kumar Tarra. “Policyholder Retention and Churn Prediction”. JOURNAL OF RECENT TRENDS IN COMPUTER SCIENCE AND ENGINEERING ( JRTCSE), vol. 10, no. 1, May 2022, pp. 89-103
Mehdi Syed, Ali Asghar, and Erik Anazagasty. “Ansible Vs. Terraform: A Comparative Study on Infrastructure As Code (IaC) Efficiency in Enterprise IT”. International Journal of Emerging Trends in Computer Science and Information Technology, vol. 4, no. 2, June 2023, pp. 37-48
Evans, R. Scott. "Electronic health records: then, now, and in the future." Yearbook of medical informatics 25.S 01 (2016): S48-S61.
Kupunarapu, Sujith Kumar. "AI-Driven Crew Scheduling and Workforce Management for Improved Railroad Efficiency." International Journal of Science And Engineering 8.3 (2022): 30-37.
Payne, Thomas H., et al. "Report of the AMIA EHR-2020 Task Force on the status and future direction of EHRs." Journal of the American Medical Informatics Association 22.5 (2015): 1102-1110.
Sangeeta Anand, and Sumeet Sharma. “Automating ETL Pipelines for Real-Time Eligibility Verification in Health Insurance”. Essex Journal of AI Ethics and Responsible Innovation, vol. 1, Mar. 2021, pp. 129-50
Kumar, Yogesh, Surabhi Kaul, and Yu-Chen Hu. "Machine learning for energy-resource allocation, workflow scheduling and live migration in cloud computing: State-of-the-art survey." Sustainable Computing: Informatics and Systems 36 (2022): 100780.
Ugajin, Atsushi. "Automation in hospitals and health care." Springer Handbook of Automation. Cham: Springer International Publishing, 2023. 1209-1233.
Chaganti, Krishna. "Adversarial Attacks on AI-driven Cybersecurity Systems: A Taxonomy and Defense Strategies." Authorea Preprints.
Vasanta Kumar Tarra, and Arun Kumar Mittapelly. “Data Privacy and Compliance in AI-Powered CRM Systems: Ensuring GDPR, CCPA, and Other Regulations Are Met While Leveraging AI in Salesforce”. Essex Journal of AI Ethics and Responsible Innovation, vol. 4, Mar. 2024, pp. 102-28
Anand, Sangeeta. “Designing Event-Driven Data Pipelines for Monitoring CHIP Eligibility in Real-Time”. International Journal of Emerging Research in Engineering and Technology, vol. 4, no. 3, Oct. 2023, pp. 17-26
Deyhim, Parviz. Best practices for amazon emr. Technical report, Amazon Web Services Inc, 2013.
Sangeeta Anand, and Sumeet Sharma. “Big Data Security Challenges in Government-Sponsored Health Programs: A Case Study of CHIP”. American Journal of Data Science and Artificial Intelligence Innovations, vol. 1, Apr. 2021, pp. 327-49
Mathew, Sajee, and J. Varia. "Overview of amazon web services." Amazon Whitepapers 105.1 (2014): 22.
Vasanta Kumar Tarra, and Arun Kumar Mittapelly. “Predictive Analytics for Risk Assessment & Underwriting”. JOURNAL OF RECENT TRENDS IN COMPUTER SCIENCE AND ENGINEERING ( JRTCSE), vol. 10, no. 2, Oct. 2022, pp. 51-70
Kodete, Chandra Shikhi, et al. "Robust Heart Disease Prediction: A Hybrid Approach to Feature Selection and Model Building." 2024 4th International Conference on Ubiquitous Computing and Intelligent Information Systems (ICUIS). IEEE, 2024.
Sangaraju, Varun Varma, and Senthilkumar Rajagopal. "Applications of Computational Models in OCD." Nutrition and Obsessive-Compulsive Disorder. CRC Press 26-35.
Anand, Sangeeta. “Quantum Computing for Large-Scale Healthcare Data Processing: Potential and Challenges”. International Journal of Emerging Trends in Computer Science and Information Technology, vol. 4, no. 4, Dec. 2023, pp. 49-59
Chaganti, Krishna C. "Advancing AI-Driven Threat Detection in IoT Ecosystems: Addressing Scalability, Resource Constraints, and Real-Time Adaptability."
Sangaraju, Varun Varma. "Optimizing Enterprise Growth with Salesforce: A Scalable Approach to Cloud-Based Project Management." International Journal of Science And Engineering 8.2 (2022): 40-48.
Mahammad, Abeda Begum, and Rajeev Kumar. "Scalable and security framework to secure and maintain healthcare data using blockchain technology." 2023 International Conference on Computational Intelligence and Sustainable Engineering Solutions (CISES). IEEE, 2023.
Sreedhar, C., and Varun Verma Sangaraju. "A Survey On Security Issues In Routing In MANETS." International Journal of Computer Organization Trends 3.9 (2013): 399-406.
Vasanta Kumar Tarra, and Arun Kumar Mittapelly. “Future of AI & Blockchain in Insurance CRM”. JOURNAL OF RECENT TRENDS IN COMPUTER SCIENCE AND ENGINEERING ( JRTCSE), vol. 10, no. 1, Mar. 2022, pp. 60-77
Kupunarapu, Sujith Kumar. "AI-Enabled Remote Monitoring and Telemedicine: Redefining Patient Engagement and Care Delivery." International Journal of Science And Engineering 2.4 (2016): 41-48.4
Mehdi Syed, Ali Asghar. “Zero Trust Security in Hybrid Cloud Environments: Implementing and Evaluating Zero Trust Architectures in AWS and On-Premise Data Centers”. International Journal of Emerging Trends in Computer Science and Information Technology, vol. 5, no. 2, Mar. 2024, pp. 42-52
Chaganti, Krishna C. "Leveraging Generative AI for Proactive Threat Intelligence: Opportunities and Risks." Authorea Preprints.
. Kulkarni, Amol. "Amazon Redshift: Performance Tuning and Optimization." International Journal of Computer Trends and Technology 71.2 (2023): 40-44.
Mehdi Syed, Ali Asghar. “Hyperconverged Infrastructure (HCI) for Enterprise Data Centers: Performance and Scalability Analysis”. International Journal of AI, BigData, Computational and Management Studies, vol. 4, no. 4, Dec. 2023, pp. 29-38
Chaganti, Krishna Chiatanya. "Securing Enterprise Java Applications: A Comprehensive Approach." International Journal of Science And Engineering 10.2 (2024): 18-27.
Mungoli, Neelesh. "Scalable, distributed AI frameworks: leveraging cloud computing for enhanced deep learning performance and efficiency." arXiv preprint arXiv:2304.13738 (2023).
Yang, Zongheng, et al. "{SkyPilot}: An intercloud broker for sky computing." 20th USENIX Symposium on Networked Systems Design and Implementation (NSDI 23). 2023.
