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Proceedings of the Eleventh International Conference on Data Engineering (1995)
Taipei, Taiwan
Mar. 6, 1995 to Mar. 10, 1995
ISSN: 1063-6382
ISBN: 0-8186-6910-1
pp: 44
K.A. Hua , Dept. of Comput. Sci., Central Florida Univ., Orlando, FL, USA
W. Tavanapong , Dept. of Comput. Sci., Central Florida Univ., Orlando, FL, USA
H.C. Young , Dept. of Comput. Sci., Central Florida Univ., Orlando, FL, USA
ABSTRACT
There has been a wealth of research in the area of parallel join algorithms. Among them, hash-based algorithms are particularly suitable for shared-nothing database systems. The effectiveness of these techniques depends on the uniformity in the distribution of the join attribute values. When this condition is not met, a severe fluctuation may occur among the bucket sizes, causing uneven workload for the processing nodes. Many parallel join algorithms with load balancing capability have been proposed to address this problem. Among them, the sampling and incremental approaches have been shown to provide an improvement over the more conventional methods. The comparison between these two approaches, however, has not been investigated. In this paper, we improve these techniques and implement them on an nCUBE/2 parallel computer to compare their performance. Our study indicates that the sampling technique is the better approach.
INDEX TERMS
distributed databases; scheduling; software performance evaluation; storage management; parallel algorithms; resource allocation; fluctuations; database theory; performance evaluation; load balancing techniques; join operations; multicomputer database systems; parallel join algorithms; hash-based algorithms; shared-nothing database systems; join attribute values distribution uniformity; bucket size fluctuations; uneven workload; processing nodes; sampling technique; incremental approach; nCUBE/2 parallel computer
CITATION

W. Tavanapong, K. Hua and H. Young, "A performance evaluation of load balancing techniques for join operations on multicomputer database systems," Proceedings of the Eleventh International Conference on Data Engineering(ICDE), Taipei, Taiwan, 1995, pp. 44.
doi:10.1109/ICDE.1995.380411
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