3rd International Week on Management of Networks and Services End-to-End Virtualization of Networks and Services Manweek 2007, October 29-November 2, San José, CA, USA
Market-Based Hierarchical Resource Management using Machine Learning
Ramy Farha1, Alberto Leon-Garcia1
1University of Toronto, Canada
Abstract. Service providers are constantly seeking ways to reduce the
costs incurred in managing the services they deliver. With the increased
distribution and virtualization of resources in the next generation network
infrastructure, novel resource management approaches are sought
for effective service delivery. In this paper, we propose a market-based
hierarchical resource management mechanism using Machine Learning,
which consists of a negotiation phase where customers are allocated the
resources needed by their activated service instances, and a learning
phase where service providers adjust the prices of their resources in order
to steer the network infrastructure towards the desired goal of increasing
their revenues, while delivering the mix of services requested by their
customers. We present the operation of such a market where distributed
and virtualized resources are traded as commodities between autonomic
resource brokers performing the negotiation and learning on behalf of
service providers. We perform extensive simulations to study the performance
of the proposed hierarchical resource management mechanism.
