Comparing the use of bayesian networks and neural networks in response time modeling for service-oriented systems

Abstract

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. Copyright 2007 ACM.