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In order to solve problems in present network load balancing approaches including high packet loss rate, low data throughput and excessively long uptime, optimization of the load balancing algorithms is needed. Thus, we propose a load balancing algorithm based on Big Data. By optimizing network structure density, regulating network nodes, dividing task dispatching paths and improving data processing steps, we constructed a data dispatch balancing and load balancing model. By simplifying network data task dispatching according to the model, we can assure our network data dispatching tasks get an ideal timespan, thus avoiding latency problems. Also, through optimizing our load balancing algorithm, we can effectively improve load balancing dual adaptability, search and expand the optimal solution space, increase data throughput and avoid packet loss problems. So, we can better maintain network load balance and efficiently improve the precision and effect in application of our load balancing algorithm. Finally, through experiments, this paper shows that a load balancing algorithm based on Big Data can reduce network latency and data loss, increase network throughput in real world cases with great adaptability. |
