Monday 07 April 2025
Cloud computing is a critical component of modern life, powering everything from our favorite streaming services to our online banking apps. But as more and more data moves into the cloud, it’s becoming increasingly important to ensure that all this information stays safe and secure.
One major challenge in achieving this goal is predicting energy consumption in cloud data centers. As you might expect, running a large number of servers 24/7 can be quite power-hungry – and that’s not just bad for the environment, but also for your wallet. If a cloud provider can’t accurately predict how much juice its servers will guzzle, it may end up over- or under-provisioning resources, leading to inefficiencies and higher costs.
That’s where a new paper comes in, proposing an innovative approach to predicting energy consumption in cloud data centers. The researchers behind this work have developed a machine learning model that combines two existing techniques – kernel extreme learning machines (KELM) and vector weighted average algorithms (VWA) – to create a more accurate and efficient prediction system.
The KELM part of the equation is all about capturing complex relationships between different data points. By using a type of neural network called an extreme learning machine, the researchers can learn to identify patterns in large datasets that might not be immediately apparent. This is especially useful when it comes to predicting energy consumption, since there are so many variables at play (think server usage, temperature, humidity, and more).
The VWA part of the equation, on the other hand, is all about prioritizing the most important features in a dataset. By assigning weights to different data points based on their relevance, the researchers can ensure that the model is focusing on the right things when making predictions.
When combined, these two techniques create a powerful prediction system that’s capable of accurately forecasting energy consumption in cloud data centers. In testing, this system outperformed existing methods by as much as 12-28%, and was able to reduce prediction errors by up to 53%.
But what does this mean for you, the average user? For one thing, it could lead to more efficient and cost-effective cloud computing services. By accurately predicting energy consumption, cloud providers can optimize their resources and reduce waste – which is good for both the environment and your wallet.
It’s also worth noting that this research has broader implications for the field of artificial intelligence as a whole.
Cite this article: “Revolutionizing Cloud Computing Energy Efficiency: A Novel Hybrid Approach Combines Predictive Power with Adaptive Feature Engineering”, The Science Archive, 2025.
Cloud Computing, Energy Consumption, Machine Learning, Prediction, Data Centers, Server Usage, Temperature, Humidity, Kernel Extreme Learning Machines, Vector Weighted Average Algorithms
