Performance Evaluation and Prediction for Legacy Information Systems

Database-centric information systems are critical to the operations of large organisations. In particular, they often process a large amount of data with stringent performance requirements. Currently, however, there is a lack of systematic approaches to evaluating and predicting their performance wh...

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Veröffentlicht in:29th International Conference on Software Engineering (ICSE'07) S. 540 - 549
Hauptverfasser: Yan Jin, Tang, A., Jun Han, Yan Liu
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.05.2007
Schlagworte:
Australia
Capacity planning
Hardware
Information systems
Performance analysis
Predictive models
Production systems
Software performance
System performance
ISBN:9780769528281, 0769528287
ISSN:0270-5257
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Abstract Database-centric information systems are critical to the operations of large organisations. In particular, they often process a large amount of data with stringent performance requirements. Currently, however, there is a lack of systematic approaches to evaluating and predicting their performance when they are subject to an exorbitant growth of workload. In this paper, we introduce such a systematic approach that combines benchmarking, production system monitoring, and performance modelling (BMM) to address this issue. The approach helps the performance analyst to understand the system's operating environment and quantify its performance characteristics under varying load conditions via monitoring and benchmarking. Based on such realistic measurements, modelling techniques are used to predict the system performance. Our experience of applying BMM to a real-world system demonstrates the capability of BMM in predicting the performance of existing and enhanced software architectures in planning for its capacity growth.
AbstractList Database-centric information systems are critical to the operations of large organisations. In particular, they often process a large amount of data with stringent performance requirements. Currently, however, there is a lack of systematic approaches to evaluating and predicting their performance when they are subject to an exorbitant growth of workload. In this paper, we introduce such a systematic approach that combines benchmarking, production system monitoring, and performance modelling (BMM) to address this issue. The approach helps the performance analyst to understand the system's operating environment and quantify its performance characteristics under varying load conditions via monitoring and benchmarking. Based on such realistic measurements, modelling techniques are used to predict the system performance. Our experience of applying BMM to a real-world system demonstrates the capability of BMM in predicting the performance of existing and enhanced software architectures in planning for its capacity growth.
Author Yan Liu
Jun Han
Tang, A.
Yan Jin
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  surname: Yan Liu
  fullname: Yan Liu
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SubjectTerms Australia
Capacity planning
Hardware
Information systems
Performance analysis
Predictive models
Production systems
Software performance
System performance
Title Performance Evaluation and Prediction for Legacy Information Systems
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