Developing Predictive Autoscaling Algorithms for Variable Traffic Patterns

Abstract

Cloud-based applications increasingly face the challenge of managing unpredictable traffic patterns while maintaining performance and cost efficiency. Predictive autoscaling has emerged as a critical solution to this problem, enabling dynamic adjustment of computational resources in response to traffic demands. This study focuses on developing intelligent algorithms that anticipate traffic variations and optimize resource allocation in real time. By analyzing historical traffic data and leveraging machine learning techniques, the proposed approach forecasts workload patterns to proactively scale resources. Unlike reactive autoscaling, which adjusts resources only after demand changes, predictive autoscaling minimizes latency and prevents preemptive resource over-provisioning. The research addresses the complexities of diverse traffic behaviours, such as sudden spikes or gradual fluctuations, and incorporates strategies to handle uncertainties in predictions. To validate the effectiveness of the proposed algorithms, we conducted simulations on real-world traffic datasets, benchmarking their performance against conventional scaling methods. The results demonstrate significant application responsiveness, cost savings, and system reliability improvements. This work highlights the potential of predictive autoscaling to transform cloud resource management, offering a scalable and adaptive solution for applications ranging from e-commerce to streaming services. By bridging the gap between traffic prediction and efficient resource utilization, this research contributes to the growing field of intelligent cloud infrastructure, paving the way for more resilient and cost-effective systems.