Introduction to Sensors in IoT: Types, Working, and Real Examples 1. What is a Sensor? A sensor is a device that detects physical or environmental changes and converts them into electrical signals. Examples of physical parameters: 2. How Sensors Work Sensors follow this process: Environment β Detection β Signal Conversion β Output For example: 3. … Read more
π· Introduction Machine learning models for cloud resource management are traditionally trained in a centralized manner, where all data is aggregated into a single server. However, with growing concerns over privacy, scalability, and data ownership, Federated Learning (FL) has emerged as a viable alternative. This article presents a detailed comparison of Centralized Learning vs Federated … Read more
π· 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. π· What is Transfer Learning? Transfer learning allows a model trained on one dataset to be reused … Read more
π· Introduction Traditional auto-scaling mechanisms rely on static thresholds, which fail under dynamic workloads. AI-based scaling introduces predictive intelligence. π· Types of Scaling Approaches 1. Static Scaling 2. Reactive Scaling 3. Predictive Scaling (AI-Based) π· AI-Based Scaling Workflow π· Scaling Decision Logic Example: Enhanced with prediction: π· Benefits of AI Scaling β Reduced SLA violationsβ … Read more
π· 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. π· LSTM Overview ft=Ο(Wfβ [htβ1,xt]+bf)f_t = \sigma(W_f \cdot [h_{t-1}, x_t] + b_f)ftβ=Ο(Wfββ [htβ1β,xtβ]+bfβ) LSTM uses: π Suitable for long-term dependencies π· GRU Overview zt=Ο(Wzβ [htβ1,xt])z_t = \sigma(W_z \cdot [h_{t-1}, x_t])ztβ=Ο(Wzββ [htβ1β,xtβ]) GRU … Read more
π· Introduction Cloud computing environments face dynamic and unpredictable workloads, making efficient resource allocation a critical challenge. Traditional centralized approaches often suffer from scalability, privacy, and latency issues. Federated Learning (FL) emerges as a promising paradigm where multiple distributed clients collaboratively train a model without sharing raw data. π· Why Federated Learning in Cloud? Key … Read more
