Title :
A Generic Learning Approach to Modelling Netflows from Historic Observations
Author :
Chronz, Peter A. ; Feldhaus, Florian ; Kasprzak, Pawel
Abstract :
In this paper we present a generic learning algo- rithm that models the communication patterns between services. Current service landscapes especially in federated environments are characterized by a huge number of services and by a high de- gree of change. In this paper we present a method for quantifying the communication patterns between services in a autonomous fashion to allow predictions of future usage patterns in the service landscape for optimization and simulation. The proposed learning algorithm uses machine learning techniques and generates a probabilistic model based on observed network flow information. We perform the learning and evaluate the learning algorithm based on real world netflow data captured on a cloud testbed. The paper finally discusses potential applications of the proposed algorithm in a autonomous optimization framework for service management.
Keywords :
IP networks; cloud computing; learning (artificial intelligence); optimisation; probability; telecommunication traffic; Netflow modelling; autonomous optimization framework; cloud testbed; communication pattern modelling; generic learning approach; historic observation; machine learning techniques; network flow information; probabilistic model; service landscape; service management; usage pattern prediction; Computational modeling; Correlation; Data models; Hidden Markov models; Optimization; Prediction algorithms; Predictive models; autonomic computing; clustering; communication patterns; machine learning; model combination; netflows; workload prediction;
Conference_Titel :
Open Cirrus Summit (OCS), 2012 Seventh
Conference_Location :
Beijing
DOI :
10.1109/OCS.2012.36