AI-Powered Cloud Resource Optimization for Large-Scale Enterprise Applications
DOI:
https://doi.org/10.15662/IJEETR.2023.0506025Keywords:
Artificial Intelligence in Cloud Computing, Cloud Resource Optimization, Machine Learning for Resource Allocation, Large-Scale Enterprise Applications, Cloud Infrastructure Management, Intelligent Workload Scheduling, Auto-Scaling and Resource Provisioning, Cloud Performance Optimization, Predictive Analytics in Cloud Systems, Distributed Cloud Computing SystemsAbstract
Cloud resource consumption is vital to the operational costs of enterprise applications, and it is often a main explanatory variable driving the performance experience. Hyper-scale public cloud providers such as Amazon, Microsoft, and Google have been investing in making resource consumption simple and cost-effective across different resource layers: virtual machines, containers, serverless, and database services. However, the rich functionality, flexibility, and cloud-native advantages of enterprise applications come with complexity that transforms any cost benefits into a burden and can negatively affect resource consumption efficiency, especially at very high scale. Empirical studies have shown that a single large-scale business application deployed and operating in the cloud costs many millions of dollars every month. Addressing these cost issues and improving efficiency at such scale require AI, ML, and Data products at their core. Optimization of resource consumption under cost and performance objectives becomes essential. Solutions using AI for data-driven resource optimization, prediction, and forecasting can be utilized on their own whenever a given cost-resource dimension is analysed or combined into a single cost-performance optimization through a case study on a real-world enterprise cloud application.
AI and ML solutions for cloud resource optimization have already been proposed and evaluated in situations such as demand prediction and capacity planning, autoscaling policies, performance-aware workload scheduling, workload-specific service selection, anomaly detection, predictive maintenance, and cost optimization. Promising results have been obtained in terms of cost, performance, and application experience. However, a single solution at a time is not sufficient for producing world-class cost-performance efficiency. A comprehensive AI-powered resource optimization framework leveraging internal or external data sources for the given task is needed to be analysed and deployed with each deployment at every stage of the data-life-cycle process while also considering specific planning needs.
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