Simulation of BRKSS Architecture for Data Warehouse Employing Shared Nothing Clustering

  • Bikramjit Pal
  • Mallika De
Keywords: BRKSS Architecture, Shared Nothing Clustering, Buffer, Cloud, Router, Switch, Hubs, Load Balancing, Databases, Turnaround Time, Throughput, Waiting Time, Ports


The BRKSS Architecture is based upon shared nothing clustering that can scale-up to a large number of computers, increase their speed and maintain the work load. The architecture comprises of a console along with a CPU that also acts as a buffer and stores information based on the processing of transactions, when a batch enters into the system. This console is connected to a switch (p-ports) which is again connected to the c-number of clusters through their respective hubs. The architecture can be used for personal databases and for online databases like cloud through router. This architecture uses the concept of load balancing by moving the transaction among various nodes within the clusters so that the overhead of a particular node can be minimised. In this paper we have simulated the working of BRKSS architecture using JDK 1.7 with Net beans 8.0.2. We compared the result of performance parameters sch as turnaround time, throughput and waiting time with existing hierarchical clustering model.


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Pal Bikramjit, Chowdhury Rajdeep, Verma Kumar Gaurav, Dasgupta Saswata, & Dutta Shubham. (2016). Proposed BRKSS architecture for performance enhancement of data warehouse employing shared nothing clustering. International Science Press, 9(21), 111 – 121.

Lee, S. (2011). Shared-nothing vs. shared-disk cloud database architecture. International Journal of Energy, Information and Communications, 2(4), 211-216.

Popeanga, J. (2014). Shared-nothing’ cloud data warehouse architecture. Database Systems Journal, V(4), 3-11.

Müseler, T. (2012). A survey of shared-nothing parallel database management systems [Comparison between teradata, greenplum and netezza implementation]. Available at:

De Witt, D., J., & Gray, J. (1991). Parallel database systems: The future of database processing or a passing fad?. Available at:

Furtado, P. (2009). A survey on parallel and distributed data warehouses. IGI Publishing, 5(2), 57-77.

Minhas, U., F., Lomet, D., & Thekkath, C., A. (2011). Chimera: Data sharing flexibility, shared nothing simplicity. IDEAS, Springer Verlag.

Datta, A., Moon, B., & Thomas, H. (1998). A case for parallelism in data warehousing and OLAP. Proceedings of the 9th International Conference on Database and Expert Systems Applications.

DeWitt, D., J., & Gray, J. (1992). Parallel database systems: The future of high performance database systems. Communication of the ACM, 35(6), 85–98.

Ezeife, C., I. & Barker, K. (1995). A comprehensive approach to horizontal class fragmentation in a distributed object based system. Distributed and Parallel Databases, 1, 247–272.

Ezeife, C. I. (1998). A partition-selection scheme for warehouse aggregate views. International Conference of Computing and Information, Manitoba, Canada.

Jurgens, M. & Lenz, H–J. (1999). Tree based indexes vs. bitmap indexes: A performance study. Available at:

Kimball, R. (1996). The data warehouse toolkit. (3rd ed.). Wiley and Sons, Inc. Available at:,%203rd%20Edition.pdf.

Patel, A., & Patel, J. M. (2102). Data modeling techniques for data warehouse. International Journal of Multidisciplinary Research, 2(2), 240–246.

Farhan, M., S., Marie, M., E., El-Fangary, L., M., & Helmy, Y., K. (2011). An integrated conceptual model for temporal data warehouse security. Computer and Information Science, 4(4), 46–57.

Eder, J. & Koncilia, C. (2001). Changes of dimension data in temporal data warehouses. Proceedings of Third International Conference on Data Warehousing and Knowledge Discovery, Munich, Germany, LNCS, Springer, 284–293.

Golfarelli, M., Maio, D., & Rizzi, S. (1998). The dimensional fact model: A conceptual model for data warehouses. International Journal of Cooperative Information Systems, 7(2-3), 215–247.

Golfarelli, M. & Rizzi, S. (1998). A methodological framework for data warehouse design. Proceedings of ACM First International Workshop on Data Warehousing and OLAP, DOLAP, Washington, 3–9.

Bernardino, J. & Madeira, H. (2019). Data warehousing and OLAP: Improving query performance using distributed computing. Available at:

Albrecht, J., Gunzel, H., & Lehner, W. (1998). An architecture for distributed OLAP. International Conference on Parallel and Distributed Processing Techniques and Applications. Available at:

Comer, D. (1979). The ubiquitous B-tree. ACM Computing Surveys, 11(2), 121–137.

How to Cite
Bikramjit Pal, & Mallika De. (2019). Simulation of BRKSS Architecture for Data Warehouse Employing Shared Nothing Clustering. International Journal of Engineering and Management Research, 9(1), 5-18.