Resource management in cloud computing with frictions and congestion weather

Cloud infrastructures with virtualized CPU and memory resources have the potential for providing high quality of service at increased levels of energy efficiency. By dynamically tailoring the capacity of a virtual machine to workload demands, a cloud infrastructure can significantly reduce the number of physical resources it has online, saving on decreased power costs. These resource management techniques, however, have yet to gain widespread appeal among network engineers due to the significant delays and setup costs in activating or reconfiguring cloud resources. A further challenge is these "frictions" fluctuate through time, based on complex system-wide supply and demand "weather" patterns in the cloud as a whole. In this paper, we develop a loss queueing model for capacity provisioning for a virtual machine that draws its computation resources from the cloud under varying friction cost. We solve for the optimal control policy using dynamic programming and discuss its intuitive structural properties. Finally we run simulations to compare the performance of the optimal policy against two benchmarks and a heuristic policy.