Title
Modellus: Automated modeling of complex internet data center applications
Abstract
The rising complexity of distributed server applications in Internet data centers has made the tasks of modeling and analyzing their behavior increasingly difficult. This article presents Modellus, a novel system for automated modeling of complex web-based data center applications using methods from queuing theory, data mining, and machine learning. Modellus uses queuing theory and statistical methods to automatically derive models to predict the resource usage of an application and the workload it triggers; these models can be composed to capture multiple dependencies between interacting applications. Model accuracy is maintained by fast, distributed testing, automated relearning of models when they change, and methods to bound prediction errors in composite models. We have implemented a prototype of Modellus, deployed it on a data center testbed, and evaluated its efficacy for modeling and analysis of several distributed multitier web applications. Our results show that this feature-based modeling technique is able to make predictions across several data center tiers, and maintain predictive accuracy (typically 95% or better) in the face of significant shifts in workload composition; we also demonstrate practical applications of the Modellus system to prediction and provisioning of real-world data center applications.
Year
DOI
Venue
2012
10.1145/2180861.2180865
TWEB
Keywords
Field
DocType
modellus system,data mining,automated relearning,data center,complex internet data center,feature-based modeling technique,automated modeling,complex web-based data center,internet data center,real-world data center application,data center tier,queuing theory,prediction error,machine learning
Data mining,Workload,Computer science,Testbed,Provisioning,Queueing theory,Internet data center,Web application,Data center,The Internet
Journal
Volume
Issue
ISSN
6
2
1559-1131
Citations 
PageRank 
References 
14
0.77
24
Authors
6
Name
Order
Citations
PageRank
Peter Desnoyers163941.59
Timothy Wood2168189.36
Prashant J. Shenoy36386521.30
Rahul Singh4140.77
Sangameshwar Patil5257.02
Harrick Vin6393.87