AI-Powered Predictive Scaling in Cloud Computing: Enhancing Efficiency through Real-Time Workload Forecasting

@article{1702943,
      author = {Pranav Murthy},
      title = {AI-Powered Predictive Scaling in Cloud Computing: Enhancing Efficiency through Real-Time Workload Forecasting},
      journal = {Iconic Research And Engineering Journals},
      year = {2021},
      volume = {5},
      number = {4},
      pages = {143-152},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/17029432.pdf},
      abstract = {AI-powered predictive scaling in cloud computing leverages machine learning algorithms to anticipate future workload demands and optimize resource allocation accordingly. Unlike traditional scaling methods that react to changes in demand, predictive scaling proactively adjusts resources based on predictions derived from historical and real-time data. This approach offers significant benefits, including improved resource utilization, cost savings, and enhanced system performance. However, it also faces challenges such as data quality issues, algorithm limitations, and integration complexities. As AI and machine learning technologies continue to advance, predictive scaling is expected to evolve, integrating with emerging technologies and adapting to diverse cloud environments. This paper explores the mechanisms of predictive scaling, its benefits and challenges, and future trends in its development.},
      keywords = {AI-Powered Predictive Scaling, Cloud Computing, Machine Learning, Resource Management, Predictive Analytics, Cloud Resource Optimization, Real-Time Data Forecasting},
      month = {October},
  }