The increasing popularity of compute-intensive apps for mobile devices has led to a greater need for efficient task delegation and resource distribution in MEC environments. Traditional optimization methods often have a hard time dealing with the dynamic and diversified nature of MEC systems, this leads to high energy expenditure and poor performance. This article proposes a deep-learning-based approach that is intelligent regarding task delegation and resource allocation in order to minimize energy expenditure while still achieving QoS requirements, such as deadlines and latency. The system has a deep learning orchestrator that analyses the task profiles and the current system conditions in order to determine the most effective path for execution: local, edge, or cloud. Through simulation and evaluation, this approach has a demonstrated superior capacity for adaptation, scalability, and efficiency in terms of energy consumption compared to traditional approaches, this provides a viable solution to the next generation of mobile computing.
Keywords
Mobile Cloud ComputingEnergy EfficiencyTask OffloadingEdge-AIResource Allocation
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