To support online index and range queries, the Distributed B-tree is adopted to index the mass and rapidly increasing
data in cloud computing. But current Distributed B-tree has three defects: low degree of concurrency, frequent
node splitting and high cost of updates in clients. For above mentioned defects, this paper presents efficient distribute Btree
index in cloud computing environment, which effectively enhances the performance of the distributed B-tree index.
First, it improves concurrent access by the distributed B-tree high concurrency access method based on node split history.
Second, it reduces the splitting frequency by the method of dynamic changing node size. Finally, it reduces node update
cost in all client buffers by the regional delayed update method. Experimental results show that, this method has high performance
in cloud computing environments.