Machine Learning-Based Memcached Cluster Auto Scaling
Rahu, Kulvinder Singh, Dipanshu, Rohan S. Kumar · 2024
Machine learning-based autoscaling for Memcached clusters is an evolving and promising field that encompasses dynamic resource allocation, anomaly detection, predictive scaling, and real-time monitoring. This abstract provides a synthesis of insights from an extensive set of 30 references, offering a comprehensive overview of the journey from addressing scalability challenges to the development of predictive models for intelligent scaling decisions. It underscores the importance of real-time management, anomaly detection, and dynamic resource allocation, highlighting significant contributions such as regression models, support vector machines, and time series analysis. The ever-evolving landscape of Memcached optimization continues to drive the pursuit of enhanced efficiency, performance, and adaptability through the integration of machine learning and distributed caching technologies.