To solve the problem of low query efficiency and high resource occupancy of the existing provenance graph query system, and consider the internal structure characteristics of provenance information, the relationship between the provenance of information and the data itself, a provenance graph query method based on double layer index structure was proposed. Firstly, for provenance graph query, a double layer index structure including global index based on dictionary table and local index based on bitmap was established. Global index was used to query the server nodes stored in provenance graph, and local index was for refining the query inside one server node. Secondly, based on the dual index structure, a provenance graph query method was designed, in view of the six kinds of selection index and three kinds of join link index. The experimental results show that the proposed method not only improves the query efficiency, but also reduces the waste of memory resources.

Concerning the high energy consumption, low acceptance rate and unbalanced load in virtual network embedding, a comprehensive energy-aware virtual network reconfiguration algorithm based on resource consolidation, namely HEAR algorithm, was proposed, which consists of two stages including node reconfiguration and link reconfiguration. In node reconfiguration stage, the virtual nodes on the physical node with least mapping virtual nodes and their relevant virtual links were moved to other physical nodes except congested nodes to improve acceptance rate and load balance, as well as suspending or closing the physical nodes with empty load to save energy. In link reconfiguration stage, the energy-aware method was adopted to select substrate link candidate set for migration, and Dijkstra algorithm was used to select the shortest available physical path to redeploy the virtual links on it. The simulation results show that, compared with energy-aware relocation heuristic algorithm, HEAR algorithm can reduce energy consumption by about 20%, and increase acceptance rate by about 10%, which means it can save energy consumption, improve the acceptance rate.