AWS Cost Optimization for Machine Learning Platform
DOI:
https://doi.org/10.53555/ephijse.v10i3.296Keywords:
AWS Cost Optimization, Machine Learning, Cloud Cost ManagementAbstract
Operating on Amazon Web Services (AWS), ML systems offer major scalability & the adaptability, which attracts companies & also academics. Still, inadequate resource allocation, unused instances & irregular demand can quickly drive up the costs of running ML workloads on their cloud. Maintaining sustainable & the efficient cloud-based ML operations depends on their well managing & reducing these prices. Enterprises trying to maximize their value of their cloud investments while keeping performance standards depends on the price minimizing in ML processes. By means of clever cost-reducing programs, businesses can significantly lower their AWS costs while maintaining these necessary high availability & compute power for ML applications. Optimizing instance sizes, using spot instances, embracing their serverless architectures & running auto-scaling systems are fundamental strategies. Right-sizing ensures that ML workloads use correctly scaled computational resources depending on the actual use patterns, therefore preventing over-provisioning & lowering waste. Spot instances are appropriate for the non-essential & fault-tolerant ML operations since they provide a reasonably affordable option by using extra AWS capacity at significantly reduced rates. By eliminating the need for always running infrastructure & billing just for actual computing time, serverless systems—including AWS Lambda & AWS Fargate—save prices. Moreover, auto-scaling continuously changes resource allocation in their response to demand, therefore ensuring effective use & the cost savings. Different cost-reducing strategies are shown in a useful case study. By means of right-sizing, the use of the spot instances & the auto-scaling, a corporation doing huge scale ML training activities on AWS successfully dropped cloud costs by 40%. This example shows the possibility for the significant financial benefits when best cost control techniques are followed. All things considered, cost effectiveness is absolutely essential for the cloud-based ML systems—especially in AWS environments where resource use could vary randomly. By means of the effective cost control strategies, companies may strike a balance between performance & their consumption, therefore ensuring long-term sustainability & profitability in their machine learning activities.
References
Nama, P. R. A. T. H. Y. U. S. H. A. "Cost management and optimization in automation infrastructure." Iconic Research and Engineering Journals 5.12 (2022): 276-285.
Osypanka, Patryk, and Piotr Nawrocki. "Resource usage cost optimization in cloud computing using machine learning." IEEE Transactions on Cloud Computing 10.3 (2020): 2079-2089.
Naseer, Iqra. "AWS cloud computing solutions: optimizing implementation for businesses." Statistics, computing and interdisciplinary research 5.2 (2023): 121-132.
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.
Chaganti, Krishna Chaitanya. "The Role of AI in Secure DevOps: Preventing Vulnerabilities in CI/CD Pipelines." International Journal of Science And Engineering 9.4 (2023): 19-29.
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
Anand, Sangeeta, and Sumeet Sharma. “Hybrid Cloud Approaches for Large-Scale Medicaid Data Engineering Using AWS and Hadoop”. International Journal of Emerging Trends in Computer Science and Information Technology, vol. 3, no. 1, Mar. 2022, pp. 20-28
Vasanta Kumar Tarra, and Arun Kumar Mittapelly. “AI-Powered Workflow Automation in Salesforce: How Machine Learning Optimizes Internal Business Processes and Reduces Manual Effort”. Los Angeles Journal of Intelligent Systems and Pattern Recognition, vol. 3, Apr. 2023, pp. 149-71
Jamal, Suhaima, and Hayden Wimmer. "Performance analysis of machine learning algorithm on cloud platforms: AWS vs Azure vs GCP." International Scientific and Practical Conference on Information Technologies and Intelligent Decision Making Systems. Cham: Springer Nature Switzerland, 2022.
Chahal, Dheeraj, et al. "Performance and cost comparison of cloud services for deep learning workload." Companion of the ACM/SPEC International Conference on Performance Engineering. 2021.
Sangaraju, Varun Varma, and Senthilkumar Rajagopal. "Applications of Computational Models in OCD." Nutrition and Obsessive-Compulsive Disorder. CRC Press 26-35.
Thota, Ravi Chandra. "Cost optimization strategies for micro services in AWS: Managing resource consumption and scaling efficiently." (2023).
Kaplunovich, Alex, and Yelena Yesha. "Cloud big data decision support system for machine learning on AWS: Analytics of analytics." 2017 IEEE International Conference on Big Data (Big Data). IEEE, 2017.
Selvarajan, Guru Prasad. "OPTIMISING MACHINE LEARNING WORKFLOWS IN SNOWFLAKEDB: A COMPREHENSIVE FRAMEWORK SCALABLE CLOUD-BASED DATA ANALYTICS." Technix International Journal for Engineering Research 8 (2021): a44-a52.
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
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
Vasanta Kumar Tarra, and Arun Kumar Mittapelly. “AI-Driven Fraud Detection in Salesforce CRM: How ML Algorithms Can Detect Fraudulent Activities in Customer Transactions and Interactions”. American Journal of Data Science and Artificial Intelligence Innovations, vol. 2, Oct. 2022, pp. 264-85
Chaganti, Krishna Chaitanya. "AI-Powered Threat Detection: Enhancing Cybersecurity with Machine Learning." International Journal of Science And Engineering 9.4 (2023): 10-18.
Sangaraju, Varun Varma. "AI-Augmented Test Automation: Leveraging Selenium, Cucumber, and Cypress for Scalable Testing." International Journal of Science And Engineering 7.2 (2021): 59-68.
Maurya, Sudhanshu, et al. "Cost analysis of amazon web services–From an eye of architect and developer." Materials Today: Proceedings 46 (2021): 10757-10760.
Kupanarapu, Sujith Kumar. "AI-POWERED SMART GRIDS: REVOLUTIONIZING ENERGY EFFICIENCY IN RAILROAD OPERATIONS." INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING AND TECHNOLOGY (IJCET) 15.5 (2024): 981-991.
Horovitz, Shay, et al. "Faastest-machine learning based cost and performance faas optimization." International conference on the economics of grids, clouds, systems, and services. Cham: Springer International Publishing, 2018.
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.
Masood, Adnan. Automated Machine Learning: Hyperparameter optimization, neural architecture search, and algorithm selection with cloud platforms. Packt Publishing Ltd, 2021.
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. “Automating Prior Authorization Decisions Using Machine Learning and Health Claim Data”. International Journal of Artificial Intelligence, Data Science, and Machine Learning, vol. 3, no. 3, Oct. 2022, pp. 35-44
Elger, Peter, and Eóin Shanaghy. AI as a Service: Serverless machine learning with AWS. Manning, 2020.
Chinamanagonda, Sandeep. "Cost Optimization in Cloud Computing-Businesses focusing on optimizing cloud spend." Journal of Innovative Technologies 3.1 (2020).
Chaganti, Krishna C. "Advancing AI-Driven Threat Detection in IoT Ecosystems: Addressing Scalability, Resource Constraints, and Real-Time Adaptability."
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
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
Vasanta Kumar Tarra, and Arun Kumar Mittapelly. “The Role of Generative AI in Salesforce CRM: Exploring How Tools Like ChatGPT and Einstein GPT Transform Customer Engagement”. JOURNAL OF RECENT TRENDS IN COMPUTER SCIENCE AND ENGINEERING ( JRTCSE), vol. 12, no. 1, May 2024, pp. 50-66
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.
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.
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.
Chaganti, Krishna C. "Leveraging Generative AI for Proactive Threat Intelligence: Opportunities and Risks." Authorea Preprints.
Mehdi Syed, Ali Asghar. “Disaster Recovery and Data Backup Optimization: Exploring Next-Gen Storage and Backup Strategies in Multi-Cloud Architectures”. International Journal of Emerging Research in Engineering and Technology, vol. 5, no. 3, Oct. 2024, pp. 32-42
Kurniawan, Agus. Learning AWS IoT: Effectively manage connected devices on the AWS cloud using services such as AWS Greengrass, AWS button, predictive analytics and machine learning. Packt Publishing Ltd, 2018.
Chaganti, Krishna. "Adversarial Attacks on AI-driven Cybersecurity Systems: A Taxonomy and Defense Strategies." Authorea Preprints.
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
Sangaraju, Varun Varma. "Ranking Of XML Documents by Using Adaptive Keyword Search." (2014): 1619-1621.
Anand, Sangeeta, and Sumeet Sharma. “Self-Healing Data Pipelines for Handling Anomalies in Medicaid and CHIP Data Processing”. International Journal of AI, BigData, Computational and Management Studies, vol. 5, no. 2, June 2024, pp. 27-37
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.
Vasanta Kumar Tarra, and Arun Kumar Mittapelly. “Voice AI in Salesforce CRM: The Impact of Speech Recognition and NLP in Customer Interaction Within Salesforce’s Voice Cloud”. Newark Journal of Human-Centric AI and Robotics Interaction, vol. 3, Aug. 2023, pp. 264-82
Marino, Carlos Antonio, Flavia Chinelato, and Mohammad Marufuzzaman. "AWS IoT analytics platform for microgrid operation management." Computers & Industrial Engineering 170 (2022): 108331.
