Market-Based Hierarchical Resource Management Using Machine Learning

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.