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CloudAIBus: a testbed for AI based cloud computing environments
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DOI：10.1007/s10586-024-04562-9.png)
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Abstract
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Smart resource allocation is essential for optimising cloud computing efficiency and utilisation, but it is also very challenging as traditional approaches often overprovision CPU resources, leading to financial inefficiencies. Recently developed Artificial Intelligence (AI) techniques have the potential to solve this problem efficiently; for example, deep learning models can accurately forecast how resources will be used, allowing for more efficient distribution of those resources. Despite these encouraging breakthroughs, researchers have not thoroughly investigated these AI models' dynamic scaling potential. To address this gap, we developed a new testbed for an AI-driven cloud computing environment called CloudAIBus for effective resource allocation. CloudAIBus employs a deep learning model named DeepAR to provide a robust solution for forecasting CPU usage in order to make cost-effective resource allocation decisions. Furthermore, we implement the DeepAR model using Amazon SageMaker, a robust platform that provides the infrastructure for scalable and efficient training. We evaluated the performance of the DeepAR-based resource management approach (CloudAIBus) using Google Colab, and results show that the proposed approach offers better performance than baselines (LSTM and ARIMA-based resource management) in terms of Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Mean Squared Error (MSE). The proposed approach cut the percentage of unused CPUs from 98.65 to 32.35% compared to the GWA-T-12 dataset. This showed that it was effective at reducing over-provisioning by making accurate predictions.
Keywords:
Cloud computing
Artificial intelligence
Deep learning
Resource allocation
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