A Task Offloading Algorithm Based on Joint Resource Optimization in Mobile Cloud-Edge Architecture

With the development of the 5G network and the advent of the era of the Internet of Things, the variety of mobile terminal devices has become increasingly abundant, endowed with enhanced computing, perception, storage, and networking communication capabilities. The tasks generated by mobile terminals have become more diverse and intensive. Ordinary mobile devices are unable to handle a large number of tasks generated in a short time. They require computing and storage resources to process complex tasks. Edge computing technology, by processing data closer to terminal devices, can effectively reduce data transmission latency compared to traditional cloud computing models. Mobile edge computing can enhance the speed of service response and provide a more smooth and real-time service experience for mobile device users. This paper primarily employs the genetic algorithm and incorporates the classic Karmarkar-Karp algorithm in partition problems to design a task offloading algorithm. During the execution of the genetic algorithm, the Karmarkar-Karp algorithm is employed to optimize the selected individuals, thereby accelerating the evolutionary process and swiftly overcoming the algorithmic bottleneck that arises from extremely uneven allocation. Ultimately, simulation results demonstrate that the optimized algorithm can find a more optimal task allocation strategy within fewer iterations, reducing the cost of task offloading and balancing the load among edge servers.