Balancing the load of BI queries in a cloud computing environment

One of the most published programming models of cloud computing is MapReduce. Google MapReduce and Apache Hadoop MapReduce are various implementations of the model. On the other hand, with the rapid increase of data accumulated by organizations, Business Intelligence systems (BI) are implemented to support decision-making. BI queries sporadically require a huge amount of computation and storage capacity. This makes it a suitable candidate for the application of cloud computing. However, to the best of our knowledge, there have been no academic works devoted to the study of the issues of distributing BI data and operators to take advantage of MapReduce computation power. The research proposes a Greedy Allocation approach to resolve the issue. To demonstrate the feasibility of the proposed method, several BI queries designed in Business Content of SAP BIW module of ECC6.0 are used. The result reveals that the proposed method can find an effective plan in reasonable time compared to more exhaustive method.