Adaptive use of a cluster of PCs for data warehousing applications

Abstract

KeywordsCluster computing, cluster of workstations, warehousing, OLAP, parallel data warehousing. data ABSTRACT Data for decision oriented technologies, like data warehousing and On-Line Analytical Processing (OLAP) systems, are ever increasing. As they store and handle data of historical nature, the volume of data involved could be very large, requiring more efficient ways of dealing with the same. Recent advances in parallel computing and high-speed networks using a cluster of PCs or workstations (COWs) offer a solution for providing this scale up in performance by parallelism of data, and its processing, in the data warehouse. However, there are issues peculiar to clusters that first need to be considered. One of the important issues in the case of a non-dedicated cluster is to adapt itself to the changing environment due to its usage. This paper looks at some of the adaptation strategies that could be applied for data warehousing and OLAP systems implemented on a COW.

An Approach for Data Warehouse Based on Grid

“…In general, analytical queries involve aggregations of large data sets, which results in serious performance problems if ad-hoc queries are to be answered online. One method to avoid performance bottlenecks is to use parallel hardware, i.e., SMP, MPP [4], COW [5] or Cluster [6][7][8], which are able to cope with the data volume.…”