Taking the Business Intelligence to the Clouds

TLDR: The cloud hosting of BI has been demonstrated with the help of a simulation on OPNET comprising a cloud model with multiple OLAP application servers applying parallel query loads on an array of servers hosting relational databases, reflecting that true and extensible parallel processing of database servers on the cloud can efficiently process OLAP applications demands on cloud computing.

Abstract: Cloud computing is gradually gaining popularity among businesses due to its distinct advantages over self-hosted IT infrastructures. The software-as-a-service providers are serving as the primary interfacing to the business users community. However, the strategies and methods for hosting mission critical business intelligence (BI) applications on cloud is still being researched. BI is a highly resource intensive system requiring large scale parallel processing and significant storage capacities to host the data warehouses. OLAP (online analytical processing) is the user-end interface of BI that is designed to present multi-dimensional graphical reports to the end users. OLAP employs data cubes formed as a result of multidimensional queries run on an array of data warehouses. In self-hosted environments it was feared that BI will eventually face a resource crunch situation because it won't be feasible for companies to keep on adding resources to host the never ending expansion of data warehouses and the OLAP demands on the underlying networking. Cloud computing has instigated a new hope for future prospects of BI. But how will BI be implemented on cloud and how will the traffic and demand profile look like? This research has attempted to answer these key questions in this paper pertaining to taking BI to the cloud. The cloud hosting of BI has been demonstrated with the help of a simulation on OPNET comprising a cloud model with multiple OLAP application servers applying parallel query loads on an array of servers hosting relational databases. The simulation results have reflected that true and extensible parallel processing of database servers on the cloud can efficiently process OLAP application demands on cloud computing. Hence, the BI designer needs to plan for a highly partitioned database running on massively parallel database servers in which, each server hosts at least one partition of the underlying database serving the OLAP demands.