Approach to problem of minimizing network power consumption based on robust optimization

Abstract

In this paper, a green and robust optimization model is introduced to minimize network power consumption, which allows fluctuations of traffic demands between source-destination pairs in the network. Our model is based on the green hose model, where the traffic is bounded by just total outgoing and incoming amount at each node. In addition to the green hose model, we use the ellipsoidal uncertainty set to allow a different type of fluctuations in traffic demands. Here, the total amount of squared errors in traffic demands is bounded by a constant which controls the total admissible fluctuations over the network.Applying the conic duality, we formulate our model in the form of mixed-integer second order cone programming (MISOCP) problem. Furthermore, we establish a relationship between our model and the green hose model with bound of link traffic (HLT) model, an developed version of the hose model known as the HLT.Numerical results demonstrate that each of the MISOCP problems can be solved to its optimality in a reasonable time by a general MISOCP solver, and that the proposed model has different tendency in performance with the green hose and green HLT models. KEYWORDSgreen computing, mixed-integer second-order cone programming, power consumption, robust optimization, second-order cone programming, traffic-demand matrix Int J Commun Syst. 2019;32:e3891.wileyonlinelibrary.com/journal/dac

Online Service Function Chain Deployment for Live-Streaming in Virtualized Content Delivery Networks: A Deep Reinforcement Learning Approach

“…RO and stochastic programming techniques have been used to model optimization under scenarios characterized by data uncertainty. Uncertainty concerning network traffic fluctuations or resource request amount can be modeled if one seeks to minimize network power consumption [33] or the costs related to cloud resource provisioning [34], for example. In [35], Marotta et al present a VNF placement and SFC routing optimization model that minimizes power consumption taking into account that resource requirements are uncertain and fluctuate in virtual Evolved Packet Core scenarios.…”