Dynamic Cloud Network Control under Reconfiguration Delay and Cost

Network virtualization and programmability allow operators to deploy a wide range of services over a common physical infrastructure and elastically allocate cloud and network resources according to changing requirements. While the elastic reconfiguration of virtual resources enables dynamically scaling capacity in order to support service demands with minimal operational cost, reconfiguration operations make resources unavailable during a given time period and may incur additional cost. In this paper, we address the dynamic cloud network control problem under non-negligible reconfiguration delay and cost. We show that while the capacity region remains unchanged regardless of the reconfiguration delay/cost values, a reconfiguration-agnostic policy may fail to guarantee throughput-optimality and minimum cost under nonzero reconfiguration delay/cost. We then present an adaptive dynamic cloud network control policy that allows network nodes to make local flow scheduling and resource allocation decisions while controlling the frequency of reconfiguration in order to support any input rate in the capacity region and achieve arbitrarily close to minimum cost for any finite reconfiguration delay/cost values.Comment: 15 pages, 7 figure

Multivariate Platoon Dispersion Modeling and Signal Coordination with A Predicted Platoon Max-Pressure Policy

This research aims to describe the evolution of platoon vehicle speed distribution parameters and platoon characteristics change quantitatively, and utilize this information to develop advanced signal timing strategies for urban arterials that could handle platoon traffic effectively. Real platoon vehicle speed data was collected. Combined with data from simulation, the speed distributions for both homogeneous and heterogeneous traffic flow were studied. The speeds follow a truncated normal distribution for homogeneous flow and a Gaussian mixture model for heterogeneous flow. The factors that influence the parameters of the truncated normal distribution were identified and a multivariate distribution parameter model was built to describe the relationship between distribution parameters and influencing factors mathematically. A modified platoon dispersion model is developed based on the previous model. The multivariate model and the modified platoon dispersion model are proved to predict downstream speeds quite well, as model validation suggests. Based on the previous model development, a novel signal timing strategy, the Predicted Platoon Max-Pressure Policy is developed. The policy utilizes the information calculated from previous models and adjust signal timing for each approach of each intersection on the arterial in real time. The performance of the proposed policy is evaluated and compared with traditional methods in simulation. Results show that the proposed policy greatly improves all measures of effectiveness, from 15% to 50% under various scenarios. The practicality of the Predicted Platoon Max-Pressure Policy is further examined in different conditions. Simulation results prove that the policy has great potential in implementation in the real world