A machine learning approach for energy optimization in Mobile Cloud Computing
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Date
2024
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Abstract
In an era marked by rapid technological advancement, smartphones have become integral to
our daily lives, transforming how we engage with the world. Despite their indispensability,
these powerful devices struggle with a variety of performance issues. Among these, limited
battery life stands out as a particularly critical challenge. Mobile Cloud Computing offers a
promising solution by leveraging remote servers to offload resource-intensive tasks. However,
existing approaches grapple to adapt to the dynamic nature of mobile environments, leading
to inefficient energy consumption. To address this and enhance the overall system efficiency,
this work is dedicated to proposing an approach that integrates machine learning techniques
such as Reinforcement Learning and K-means clustering. This approach dynamically partitions
mobile applications into offloadable tasks for remote execution on cloud servers and
unoffloadable tasks for local execution on the mobile device. Through experimentation and
evaluation, we demonstrate the efficacy of our approach in improving energy efficiency and
performance.