The Impact of Workload Clustering on Transaction Routing
DocUID: 1998-003 Full Text:
PDF Author: Christos Nikolaou, Alexandros Labrinidis, Volker Bohn, Donald Ferguson, Michalis Artavanis, Christos Kloukinas, Manolis Marazakis
Abstract: The qualitative and quantitative description of the workload of a system is very important for capacity planning and performance management. In large-scale transaction processing systems, dynamic workload control algorithms are applied to optimize system performance. Such algorithms can benefit from the results of workload clustering algorithms that partition the workload into classes consisting of units of work exhibiting similar characteristics. This paper presents CLUE, a clustering environment for OLTP workload characterization. CLUE provides a library of clustering algorithms that classify transactions into classes, according to their database reference patterns. This paper introduces HALC, a new batch-mode heuristic clustering algorithm, designed to cope with the large volume of input data that is typical for real-life applications. Next, an on the fly clustering algorithm based on neural networks is described. This algorithm can be used in an on-line fashion in systems whose characteristics change through time. This paper provides an evaluation of the performance of HALC and the on the fly algorithms in terms of execution times and statistical metrics related to the quality of clusters that they compute, for both synthetic and real-life workload traces. Finally, this paper quantifies the impact of workload clustering on the performance of three dynamic transaction routing algorithms for Shared-Nothing transaction processing systems
Published In: Institute of Computer Science, FORTH, Technical Report No. 238
Pages: pp. 1-26
Place Published: University of Crete, Greece
Year Published: 1998
Project: Others Subject Area: Transaction Models
Publication Type: Technical Report
Sponsor: Others