LAUSR

Abstract Because of the limited memory of the increasing amount of information in current wearable devices, the processing capacity of the servers in the storage system can not keep up with the speed of information growth, resulting in low load balancing, long load balancing time and data processing delay. Therefore, a data load balancing technology is applied to the massive storage systems of wearable devices in this paper. We first analyze the object-oriented load balancing method and formally describe the dynamic load balancing issues, taking the load balancing as a mapping problem. Then the task of assigning each data node and the corresponding request task of the actual processing capability for the data node are completed. Different data is allocated to the corresponding data storage node to complete the calculation of the comprehensive weight of the data storage node. The load information of each data storage node is collected according to the scheduler in the storage system to calculate the load weight of the current data storage node and assign it. The data load balancing of the massive storage system for wearable devices is realized. The experimental results show that the average time of load balancing using this method is 1.75h, which is much lower than the traditional methods. It is shown that the data load balancing technology of the massive storage system of wearable devices has less time for data load balancing, higher load balancing, stronger data processing capability, shorter processing time and obvious application advantages.