Query's optimization in data warehouse on the cloud using fragmentation

Abstract

Nowadays Cloud Computing occupies an advanced place in the field of service-oriented technologies. The cloud provides a flexible environment for customers to host and process their information through an outsourced infrastructure. This information was habitually located on local servers. Many applications dealing with massive data is routed to the cloud. Data Warehouse (DW) also benefit from this new paradigm to provide analytical data online and in real time. DW in the Cloud benefited of its advantages such flexibility, availability, adaptability, scalability, virtualization, etc. Improving the DW performance in the cloud requires the optimization of data processing time. The classical optimization techniques (indexing, materialized views and fragmentation) are still essential for DW in the cloud. The DW is partitioned before being distributed across multiple servers (nodes) in the Cloud. When queries containing multiple joins or ask voluminous data stored on multiple nodes, inter-node communication increases and consequently the DW performance degrades. In this paper we propose an approach for improving the performance of DW in the Cloud. Our approach is based on a mapping placed on nodes leased by the client. It consists to memorize: (i) the requests received by the node, (ii) information about DW; (iii) an algorithm of query processing. We use the data stored in the map for fragmenting the DW in order to minimize the inter-node communications.