Theres so much interference in the network information and the use of data nodes have strong randomness

the current data scheduling model is easy to cause conflict data node

results in the decrease of scheduling efficiency

therefore

put forward a new network more interference of huge amounts of data optimization scheduling model

build the network information to be processed characteristic similarity model

the characteristics of the item to be processed to obtain network neighbors

the collaborative filtering algorithm was adopted to realize the network interference filter. Based on the partition for the amount of mass data scheduling tasks is calculated

to obtain huge amounts of data scheduling tasks of subtasks

so as to build huge amounts of data scheduling tasks and data mapping relationships between the nodes. According to the target data transmission in the servers status

the quota of huge amounts of data communication

build huge amounts of data optimization scheduling model. The experimental results show that the proposed model to the scheduling of huge amounts of data

not only high throughput

and transmission rate is fast

has a high operation performance.