The rising popularity of big data processing for semantically rich applications such as social networks and IoT (Internet of Things) has made the range queriable cloud storage increasingly important. To support range queries, the data locality is preserved strictly, which makes the load balancing among nodes a challenging task. Currently, most of the range queriable cloud storage systems adopt the combination of neighbor item exchange and neighbor migration methods, which incurs large overhead, and suffers from slow convergence. In this work, we present a novel virtual node based distributed load balancing method for range queriable cloud systems. In our method, each physical node is partitioned into multiple virtual nodes, and all the virtual nodes are organized with range queriable P2P network. Load balancing is conducted in both overlay level (betweenneighboring virtual nodes) without global knowledge and physical level (among physical nodes) with limited global knowledge. Both theoretical analysis and simulations show that our method can significantly reduce the overhead and shorten the convergence time.