Cloud BI: Future of Business Intelligence in the Cloud

TL;DR

This paper explores how Business Intelligence can be effectively implemented on cloud platforms, demonstrating through simulation that parallel processing on cloud servers can meet OLAP demands efficiently.

Contribution

It presents a simulation-based approach showing that cloud-based BI can handle OLAP workloads with scalable parallel processing, addressing resource limitations of traditional self-hosted systems.

Findings

Parallel query processing on cloud servers is efficient for OLAP workloads.

Cloud BI requires highly partitioned databases across multiple servers.

Simulation results support scalable, resource-efficient cloud BI deployment.

Abstract

Cloud computing is gradually gaining popularity among businesses due to its distinct advantages over self-hosted IT infrastructures. Business Intelligence (BI) is a highly resource intensive system requiring large-scale parallel processing and significant storage capacities to host data warehouses. In self-hosted environments it was feared that BI will eventually face a resource crunch situation because it will not be feasible for companies to keep adding resources to host a neverending expansion of data warehouses and the online analytical processing (OLAP) demands on the underlying networking. Cloud computing has instigated a new hope for future prospects of BI. However, how will BI be implemented on cloud and how will the traffic and demand profile look like? This research attempts to answer these key questions in regards 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.