Probabilistic reasoning in expert systems: theory and algorithms
Probabilistic reasoning in expert systems: theory and algorithms
Journal of the American Society for Information Science
Response time densities in generalised stochastic petri net models
WOSP '02 Proceedings of the 3rd international workshop on Software and performance
The Vision of Autonomic Computing
Computer
Accuracy vs. efficiency trade-offs in probabilistic diagnosis
Eighteenth national conference on Artificial intelligence
Feedback Control with Queueing-Theoretic Prediction for Relative Delay Guarantees in Web Servers
RTAS '03 Proceedings of the The 9th IEEE Real-Time and Embedded Technology and Applications Symposium
Introduction: Service-oriented computing
Communications of the ACM - Service-oriented computing
An analytical model for multi-tier internet services and its applications
SIGMETRICS '05 Proceedings of the 2005 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
OGSA-based grid workload monitoring
CCGRID '05 Proceedings of the Fifth IEEE International Symposium on Cluster Computing and the Grid (CCGrid'05) - Volume 2 - Volume 02
Performance problem localization in self-healing, service-oriented systems using Bayesian networks
Proceedings of the 2007 ACM symposium on Applied computing
Using magpie for request extraction and workload modelling
OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
Sequential update of Bayesian network structure
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Expert Systems with Applications: An International Journal
Autonomic computing control of composed web services
Proceedings of the 2010 ICSE Workshop on Software Engineering for Adaptive and Self-Managing Systems
Model comparison in Emergency Severity Index level prediction
Expert Systems with Applications: An International Journal
Future Generation Computer Systems
Application of Bayesian Networks for Autonomic Network Management
Journal of Network and Systems Management
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The new paradigm of service-oriented computing facilitates easy construction of dynamic, complex distributed systems. Recent research has shown that machine learning methods can be a promising way to autonomously and accurately derive models to assist autonomic management software or humans in understanding system behaviors and making informed decisions. However, the efficacy of different machine learning techniques in describing various system behaviors and meeting distinct application needs has not been systematically understood. Such an understanding can prove crucial in management infrastructure design and implementation for service-oriented systems. This paper is an initial step to bridge the gap and specifically contrasts the applications of Bayesian networks (BN) and neural networks (NN) in modeling the response time of service-oriented systems. Relatively simple BN and NN models are designed and implemented as a base of the comparison study. As far as model performance is concerned, a wide range of simulations show that BNs offer better accuracy, are less sensitive to small data set size and are therefore more suited for environments that change rapidly and need frequent response time model reconstructions; whereas NNs can achieve faster model evaluation time and support management routines that demand intensive response time predictions. From a non-performance perspective, it is analytically concluded that BNs can be more easily understood by human and support multi-direction evaluation, while NNs provide more flexible response time representation.