On Workload Characterization of Relational Database Environments
IEEE Transactions on Software Engineering
Memory system characterization of commercial workloads
Proceedings of the 25th annual international symposium on Computer architecture
Performance characterization of a Quad Pentium Pro SMP using OLTP workloads
Proceedings of the 25th annual international symposium on Computer architecture
The Asilomar report on database research
ACM SIGMOD Record
Configuring and Tuning Databases on the Solaris Platform
Configuring and Tuning Databases on the Solaris Platform
Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey
Data Mining and Knowledge Discovery
Characterization of database access pattern for analytic prediction of buffer hit probability
The VLDB Journal — The International Journal on Very Large Data Bases
Performance Analysis of Affinity Clustering on Transaction Processing Coupling Architecture
IEEE Transactions on Knowledge and Data Engineering
DBMSs on a Modern Processor: Where Does Time Go?
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
SPRINT: A Scalable Parallel Classifier for Data Mining
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Characteristics of production database workloads and the TPC benchmarks
IBM Systems Journal - End-to-end security
Automatically classifying database workloads
Proceedings of the eleventh international conference on Information and knowledge management
Primitives for workload summarization and implications for SQL
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
The Psychic-Skeptic Prediction framework for effective monitoring of DBMS workloads
Data & Knowledge Engineering
On predictive modeling for optimizing transaction execution in parallel OLTP systems
Proceedings of the VLDB Endowment
Near real-time analytics with IBM DB2 analytics accelerator
Proceedings of the 16th International Conference on Extending Database Technology
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The type of the workload on a database management system (DBMS) is a key consideration in tuning the system. Allocations for resources such as main memory can be very different depending on whether the workload type is Online Transaction Processing (OLTP) or Decision Support System (DSS). A DBMS also typically experiences changes in the type of workload it handles during its normal processing cycle. Database administrators must, therefore, recognize the significant shifts of workload type that demand reconfiguring the system in order to maintain acceptable levels of performance. We envision autonomous, self-tuning DBMSs that have the capability to manage their own performance by automatically recognizing the workload type and then reconfiguring their resources accordingly. In this paper, we present an approach to automatically identifying a DBMS workload as either OLTP or DSS. We build a classification model based on the most significant workload characteristics that differenti ate OLTP from DSS and then use the model to identify any change in the workload type. We construct and compare classifiers built from two different sets of industry-standard workloads, namely the TPC-C and TPC-H benchmarks, and the Browsing and Ordering profiles from the TPC-W benchmark. We conduct various sets of experiments that show that our workload classifiers are reliable, and have high accuracy in recognizing the type of the workload mix and in estimating the degree of its concentration.