Authors:
Nima Sharifimehr
and
Samira Sadaoui
Affiliation:
University Of Regina, Canada
Keyword(s):
Object pool service, markov model, prediction, automatic tuning, workload modeling, enterprise applications.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Data Engineering
;
Engineering Information System
;
Enterprise Information Systems
;
Information Retrieval
;
Information Systems Analysis and Specification
;
Ontologies and the Semantic Web
;
Pattern Recognition
;
Software Engineering
Abstract:
One of the most challenging concerns in the development of enterprise software systems is how to manage effectively and efficiently available resources. Object pooling service as a resource management facility significantly improves the performance of application servers. However, tuning object pool services is a complicated task that we address here through a predictive automatic approach. Based on dynamic markov models, which capture high-order temporal dependencies and locally optimize the required length of memory, we find patterns across object invocations that can be used for prediction purposes. Subsequently, we propose an effective automatic tuning solution, with reasonable time costs, which takes advantage of past and future information about activities of object pool services. Afterwards, we present experimental results which demonstrate the scalability and effectiveness of our novel tuning solution, namely predictive automatic tuning service.