Summary:

In this week's episode, we talk with Thaleia Doudali and explore the realm of cloud resource forecasting, focusing on the use of Long Short Term Memory (LSTM) neural networks, a popular machine learning model. Drawing from her research, Thaleia discusses the surprising discovery that, despite the complexity of ML models, accurate predictions often boil down to a simple shift of values by one time step. The discussion explores the nuances of time series data, encompassing resource metrics like CPU, memory, network, and disk I/O across different cloud providers and levels. Thaleia highlights the minimal variations observed in consecutive time steps, prompting a critical question: Do we really need complex machine learning models for effective forecasting? The episode concludes with Thaleia's vision for practical resource management systems, advocating for a thoughtful balance between simple solutions, such as data shifts, and the application of machine learning. Tune in as we unravel the layers of cloud resource forecasting with Thaleia Doudali.

Links:

• SoCC'23 Paper
• Thaleia's Homepage
• IMDEA Software Homepage
• GitHub Repo

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