Welcome to this episode, where we explore the critical domain of cloud workload forecasting and intelligent resource scaling. Efficient management of cloud resources is paramount for cost-effectiveness and optimal performance in today's data-driven environment. We will discuss cutting-edge research addressing the challenges of predicting cloud workloads, encompassing short-term fluctuations and long-term capacity planning.
This podcast synthesizes findings from several pivotal research papers, which we cite as follows:
• We will begin with the "Prophet" forecasting model, a modular regression approach for time series analysis that is designed to be configurable by analysts with domain knowledge, as described in Taylor, S.J. & Letham, B. (2018). Forecasting at Scale.
• Next, we will examine the "TempoScale" approach to cloud workload prediction, which integrates both short-term and long-term information through a decomposition algorithm and deep learning techniques. This is detailed in Wen, L., Xu, M., Toosi, A.N., & Ye, K. (2024). TempoScale: A Cloud Workloads Prediction Approach Integrating Short-Term and Long-Term Information.
• Finally, we will explore a comprehensive analysis of various forecasting algorithms for real-world cloud query workloads, as presented in Diao, Y., Horn, D., Kipf, A., Shchur, O., Benito, I., Dong, W., Pagano, D., Pfeil, P., Nathan, V., Narayanaswamy, B., & Kraska, T. (2024). Forecasting Algorithms for Intelligent Resource Scaling: An Experimental Analysis.
Our discussion will cover the following key areas:
• The challenges inherent in forecasting at scale, addressing the complexities of diverse time series and the need for analysts with domain expertise.
• The significance of interpretable model parameters that can be adjusted by analysts without deep statistical expertise.
• Methods for automated evaluation of forecast quality and effective integration of human feedback.
• The crucial requirement to capture both long-term trends and short-term fluctuations in cloud workloads for effective scaling.
• An in-depth analysis of spikiness and seasonality in production cluster workloads and why traditional forecasting methods may not be sufficient.
• The development and analysis of custom ensemble models that combine multiple machine learning algorithms, leading to improved predictive performance.
Join us as we explore the latest techniques and insights shaping the future of cloud resource management, informed by these significant contributions to the field.
Disclaimer: Please be advised that all or parts of this podcast are generated by AI. While we strive for accuracy, the information presented may contain some errors. Please refer to the original research papers for complete and verified details.
