Resource Allocation Optimization Study of Cloud-Edge-End Collaboration Based on QUBO Model

The core of this article is to construct an effective QUBO model to solve the problem of cloud edge end computing resource allocation, including two key scenarios: optimization of server maximum computing demand coverage and optimization of cloud edge server computing network layout. Each scenario provides a detailed description of the model's process from analysis, establishment to solution, overcoming several major difficulties in establishing a QUBO model, including using binary variables to express the objective function, transforming the objective function into QUBO expressions, and transforming inequality constraints. A scheme for the number of constraint relaxation factors is introduced, which effectively reduces the number of quantum bits and computational complexity. Finally, statistical analysis of the solution results, non parametric testing based on data characteristics, and detailed analysis are conducted, demonstrating the potential application of the QUBO model and quantum computing in dealing with complex combinatorial optimization problems. During the solving process, China's first optical quantum computing power cloud platform and corresponding quantum computing library Kaiwu SDK were utilized for research. Multiple solvers were used and their performance was compared to select the best performing taboo search solver, and then detailed results were calculated. The final results were fully visualized to make them more intuitive. The results indicate that quantum computing can provide an efficient, accurate, and stable solution for cloud edge end computing resource allocation. Through these studies, this article not only confirms the efficiency of quantum computing in solving specific optimization problems, but also provides some experience and reference for exploring the application of quantum computing in a wider field.