AI-Based Workload Prediction Models for Optimizing Serverless Resource Allocation
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Abstract
Serverless computing has become a popular architecture in cloud computing due to its scalability and cost-efficiency. However, unpredictable workload variations can lead to either over-provisioning or under-provisioning of resources, impacting performance and cost. This study explores the use of AI-based workload prediction models to forecast serverless workloads accurately and optimize resource allocation. The proposed model leverages machine learning techniques, including Long Short-Term Memory (LSTM) networks, to enhance predictive capabilities. Results indicate that AI-based workload forecasting reduces latency and optimizes costs compared to traditional static approaches. This paper offers a practical roadmap for serverless providers to adopt AI models for dynamic and efficient resource management.
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• "Optimizing Data Processing for Financial Services Platforms
• Author : Harshita Cherukuri1, Villa 188, My Home Ankura, Sector B, Radial Road-7, Exit No 2, Tellapur, Cyberabad-sangareddy, 502032, Telangana, India , Dr. Bhawna Goel , Dr. Poornima Tyagi
• DOI LINK : 10.56726/IRJMETS60903 doi 10.56726/IRJMETS60903"
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• Vijayabaskar, Santhosh, Amit Mangal, Swetha Singiri, A. Renuka, and Akshun Chhapola. 2023. "Leveraging Blue Prism for Scalable Process Automation in Stock Plan Services." Innovative Research Thoughts 9(5):216. doi: https://doi.org/10.36676/irt.v9.i5.1484.
• Mahadik, Siddhey, Amit Mangal, Swetha Singiri, Akshun Chhapola, and Shalu Jain. 2022. "Risk Mitigation Strategies in Product Management." International Journal of Creative Research Thoughts (IJCRT) 10(12):665.