Journal of Data Processing Chen Qing, Yong Zhong, Liuming Xiang 5 4 2015 http://www.dline.info/jdp/fulltext/v5n4/jdpv5n4_1.pdf Due to the two-phase commit protocol, all transactions of DDBS (distributed database system) will roll back if one of distributed nodes was overload, finally make the DDBS difficultly adapting to the Big Data’s environment, whose data has the characters of dynamic and randomness. In order to solve this problem, Shannon entropy is proposed to evaluate system’s load, using the maximum entropy principle of entropy with the objective function and constraints to balance the load and maximize resource’s utilization on the demand of user’s QoS. Overloaded node’s data will be migrated to other suitable nodes under the guidance of algorithm based on Shannon entropy, and make a step to the further design of Cloud database system. Experimental results show that the load evolution algorithm based on Shannon entropy can evaluate the load in Big Data’s environment, avoid single-node bottlenecks, and improve system’s performance.