Machine Learning for Performance Optimization in Resource-Constrained Environments

The rapid growth of digital technology has significantly increased reliance on computer systems across many different fields. This widespread adoption has also led to the deployment of devices in challenging scenarios characterized by limited resources, such as constrained computational capabilities or unreliable communication. These environments require alternative, innovative solutions, often specifically tailored to individual use cases. However, if operational conditions change at runtime, causing systems to function in unforeseen scenarios, there is a considerable risk of performance degradation or inefficient use of already limited and valuable resources. To address these complexities, Machine Learning (ML) methods offer significant potential by enabling system to autonomously adapt to evolving conditions. Techniques including predictive analytics and self-optimization allow systems to intelligently adjust at runtime, dynamically optimizing resource usage and performance. However, the effectiveness of ML-driven optimization is significantly limited by the scarcity of realistic data, particularly in scenarios involving limited resources. To mitigate this challenge, generative artificial intelligence emerges as a pivotal tool, facilitating data augmentation, enhancing the effectiveness of the development and testing processes of software solutions.