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Multimedia Network Big Data Balanced Scheduling Based On Fibonacci Tree Optimization Algorithm

With the popularization of Internet technology, network big data has penetrated into every household. Various multimedia networks have enriched everyone's lives. At the same time, the scale of multimedia network data has become larger and larger, making the balanced scheduling of big data networks The problem is becoming more and more obvious. The problem of the unbalanced load of the multimedia network big data network has always been an object of attention. Therefore, for the problem of resource balance scheduling in multimedia network data, finding an effective network optimization algorithm plays an important role in improving the load balance of large-scale multimedia network resources. The main research content of this paper is based on the Fibonacci tree optimization algorithm. For the research on the balanced scheduling of media network big data, the Fibonacci tree optimization algorithm is a new intelligent optimization algorithm based on the Fibonacci method and the golden section method. The algorithm solves the optimal solution of the problem through alternate iterations of global exploration and local optimization. The final results of the study show that the throughput and delay of different algorithms are quite different when the number of tasks is the same. The Fibonacci tree optimization algorithm method in this paper has a throughput rate of 13.26mb/s and a delay of 16.28ms, which is similar. It has the highest throughput rate and lower latency than other algorithms, which shows that the Fibonacci tree optimization algorithm can adjust resources adaptively to the big data network environment, and the advantages of big data balanced scheduling are fully demonstrated.