摘要：Edge-clouds provide a promising approach to significantly improve network operational costs by moving computation closer to the network edge. A key challenge in such systems is to decide where and when services should be migrated in response to user mobility and demand variation. The objective is to optimize operational costs while providing rigorous performance guarantees. In this work, we model this as a sequential decision making problem using Markov Decision Process (MDP). However, departing from traditional solution methods (such as dynamic programming) that require extensive statistical knowledge and are computationally prohibitive, we develop a novel alternate methodology. First we establish an interesting decoupling property of the MDP that reduces it to two independent MDPs on disjoint state spaces. Then, using the technique of Lyapunov optimization over renewals, we design an online control algorithm for the decoupled problem that is provably cost-optimal. This algorithm does not require any statistical knowledge of the system parameters and can be implemented efficiently. We validate the performance of our algorithm using extensive trace-drive simulations. Our overall approach in general and can be applied to other MDPs that possess a similar decoupling property.
个人简介：Rahul Urgaonkar is a Research Staff Member with the Cloud-Based Networks group at the IBM TJ Watson Research Center. He is currently a task lead on the US Army Research Laboratory (ARL) funded Network Science Collaborative Technology Alliance (NS CTA) program. He is also a Primary Researcher in the US/UK International Technology Alliance (ITA) research programs. His research is in the area of stochastic optimization, algorithm design and control with applications to communication networks and cloud-computing systems. Before joining IBM research, Rahul was a Scientist with the Network Research group at Raytheon BBN Technologies where he worked on several government funded projects, including the NS CTA and ITA programs. He obtained his Masters and PhD degrees from the University of Southern California in 2005 and 2011 respectively and his Bachelor’s degree (all in Electrical Engineering) from the Indian Institute of Technology Bombay in 2002.