A Multi-Dimensional Indexing for Complex Query in Cloud Computing

Data indexing is one of the most important techniques for distributed storage systems in cloud computing environments since the application data has been partitioned among different storage nodes of the data center. With the rapid development of Web applications, most query requests about metadata information are more complicated. However, the state-of-the-art indexing mechanisms for distributed storage system cannot support complex query, such as multi-dimensional query and range query. To address this issue, we firstly construct the definition of prefix binary tree (PBT) in this paper to support range query process. We then investigate a multi-dimensional indexing for complex query in cloud computing (M-Index) by the combination of pyramid-technique and PBT to transform the multi-dimensional metadata into a single-dimensional key. Data are distributed to overlay networks based on the key and consistent hashing to implement the efficient acquisition and distribution of data. On this basis, we propose a query algorithm based on M-Index which will support multi-dimensional query and range query. Last but not the least, theoretic analysis proves that M-Index possesses fine complex query efficiency as well as completeness of query results. And furthermore, the experiment results demonstrate that our indexing mechanism can outperform the existing relevant mechanisms in query efficiency and load balancing.