Making phones and servers work together more efficiently with AI

What is it about?

Mobile devices like phones and sensors often need to run demanding apps, but they don’t always have enough power or speed. One solution is to offload tasks to nearby “edge servers,” which can process them faster. The challenge is deciding which tasks to offload, when, and where, and more specifically, when many devices are competing for resources. In this work, we developed a new method where devices and a central “master” agent at the server learn together, using artificial intelligence, to make smarter offloading decisions. Our approach takes into account deadlines, energy use, network capacity, and server storage. The results show that our method reduces delays and saves energy compared to existing approaches. This means smoother app performance for users, more efficient use of networks, and longer battery life for mobile devices.

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Why is it important?

As mobile devices become more powerful and widely used, from smartphones to Internet of Things sensors, they increasingly rely on cloud and edge servers to handle heavy tasks like AI, video, or data processing. The problem is that current methods for deciding when and how to offload tasks often waste energy, cause delays, or cannot scale when many devices are connected at once. Our work is important because it introduces a new way for devices to make coordinated, intelligent offloading decisions in real time. By combining individual learning at the device level with guidance from a central “master” agent, our method improves both efficiency and scalability. This leads to faster response times, lower energy use, and smoother experiences for users, which are critical as society moves toward more AI-driven applications, smart cities, and connected devices.

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