Cloud Computing

Transfer Learning for Cloud Workload Prediction

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🔷 Introduction Cloud environments often suffer from data scarcity and domain variability, making it difficult to train robust machine learning models. Transfer Learning (TL) addresses this challenge by leveraging knowledge from one domain and applying it to another. 🔷 What is Transfer Learning? Transfer learning allows a model trained on one dataset to be reused … Read more

LSTM vs GRU for Workload Prediction in Cloud Computing

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🔷 Introduction Accurate workload prediction is essential for proactive resource provisioning. Among deep learning models, LSTM (Long Short-Term Memory) and GRU (Gated Recurrent Unit) are widely used. 🔷 LSTM Overview ft=σ(Wf⋅[ht−1,xt]+bf)f_t = \sigma(W_f \cdot [h_{t-1}, x_t] + b_f)ft​=σ(Wf​⋅[ht−1​,xt​]+bf​) LSTM uses: 👉 Suitable for long-term dependencies 🔷 GRU Overview zt=σ(Wz⋅[ht−1,xt])z_t = \sigma(W_z \cdot [h_{t-1}, x_t])zt​=σ(Wz​⋅[ht−1​,xt​]) GRU … Read more