Computer Force Network integrates distributed and ubiquitous computing capabilities in different geographic locations, and its sources include various computing devices such as cloud computing nodes, edge computing nodes, end devices, network devices, etc. The computing tasks in the CFN environment are large in volume and diverse in type, including data analysis, AI reasoning, graphics rendering, and other computing tasks. In this case, the traditional traffic control strategy may not be able to effectively handle the diversity and magnitude of tasks, which may lead to the waste of computing resources, delay of computing tasks, and degradation of service quality. To solve these problems, AI-based traffic control and computing force matching can be used to train AI models using deep learning algorithms by collecting a large amount of network traffic data, device state data, and task demand data. The model can not only learn the pattern of network traffic and computing tasks but also predict future traffic changes and task demands, as well as the computing capacity of devices, and adjust the traffic control strategy and arithmetic matching strategy in real-time based on this information.
With the help of AI, operators can manage traffic and computing power more effectively, reduce network congestion, improve the utilization of computing resources, reduce the latency of computing tasks, and improve the quality of service. For example, when a large number of data analysis tasks are predicted to be coming, AI systems can adjust network configurations in advance to prioritize allocating computing resources to these tasks to meet demand. When the capacity of computing devices is predicted to be insufficient to handle the upcoming tasks, the AI system can adjust the traffic control policy in advance to redirect some tasks to other devices to prevent congestion.
AI-based Computer Force Network traffic control and computer force matching bring significant performance improvements to large-scale CFN, enabling operators to manage computing resources better to meet the demands of various computing tasks.
About GTI
GTI is an international cooperation platform initiated and established by China Mobile, Softbank, Vodafone and other operators in 2011. It currently has 146 operator members and 258 industry partners. In 2023, the new stage of GTI 3.0 was officially launched, and it is committed to continuously deepening the global cooperation of 5G-A+AI and achieving win-win commercial success. For more information, please visit http://gtigroup.org/
About the GTI 5G-A x AI Open Development Program
In Feb 2024 at MWC Barcelona, GTI launched the 5G-A×AI Development Program to promote the integrated innovation of 5G and AI in technology, business, ecology, and commerce, and two-way empowerment. Therefore, 5G is smarter and AI is more ubiquitous, which will support the goals of GTI 3.0. First, Build Open Labs to provide basic environment, equipment facilities, industry application scenarios and other resources for 5G-AxAI integration innovation, and carry out the R&D, testing and demonstration of new technologies and solutions. Second, Build an Open Collaborative Innovation Community, with an online platform for “Communication and Sharing” and “Supply and Demand Matching”, and jointly carry out cutting-edge exploration, technical research, testing and iterative optimization. Third, Explore Innovative 5G-AxAI Integration Use Cases, and condense themreplicable business model templates, so as to provide references for value creation and monetization.
About the China Mobile Research Institution Service Division
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