Allocating data cubes on a grid environment: endorsing an alternative approach

Abstract

Usually data warehouses store a large volume of data, keeping in their structures all information decision-makers need on their daily activities. Usually, a significant amount of this information is processed and materialized in data cubes to be explored latter using the most flexible ways that on-line analytical processing platforms dispose to do that. However, this materialization process requires a lot of computational resources (processing time and storage), provoking as well serious performance bottlenecks in analytical systems. The distribution of such analytical structures across an organization seems to be quite appellative to attenuate such annoying effects, being in most of the cases a good alternative for improving system performance without great expense. Still, a distributed computational platform use to be less expensive than a centralized one, what give it some good advantages in any solution related to data cube distribution. Following this slope appears Grid environments. As we know these can use computational resources geographically dispersed, heterogeneously and non-dedicated as it was a single a unique environment. However, in order to reach higher performance levels, Grids need proper scheduling and data so they can be able to respond effectively to the jobs that were sent to them. In this paper, we integrate a performance prediction method in a data cube distribution strategy to define a more effective allocation of data cube views in a conventional Grid environment with the ability to receive them.