Machine Learning at the Edge: A Data-Driven Architecture with Applications to 5G Cellular Networks

The fifth generation of cellular networks (5G) will rely on edge cloud deployments to satisfy the ultra-low latency demand of future applications. In this paper, we argue that such deployments can also be used to enable advanced data-driven and Machine Learning (ML) applications in mobile networks. We propose an edge-controller-based architecture for cellular networks and evaluate its performance with real data from hundreds of base stations of a major U.S. operator. In this regard, we will provide insights on how to dynamically cluster and associate base stations and controllers, according to the global mobility patterns of the users. Then, we will describe how the controllers can be used to run ML algorithms to predict the number of users in each base station, and a use case in which these predictions are exploited by a higher-layer application to route vehicular traffic according to network Key Performance Indicators (KPIs). We show that the prediction accuracy improves when based on machine learning algorithms that rely on the controllers' view and, consequently, on the spatial correlation introduced by the user mobility, with respect to when the prediction is based only on the local data of each single base station.Comment: 15 pages, 10 figures, 5 tables. IEEE Transactions on Mobile Computin

Deterministic Fluid Models for Internet Congestion Control

113 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2003.We then study the impact of parameter choice of an AQM (active queue management) scheme that can be used at the router. We argue that, depending upon the choice of the parameters of the AQM scheme, one would obtain a rate-based model or a rate-and-queue-based model as the deterministic limit of a stochastic system with a large number of users. We argue that, a virtual-queue-based AQM scheme is very robust to the choice of parameters in achieving a low-loss, low-delay, and high-utilization operation. However, with a real-queue-based marking, the choice of parameters is much more critical.U of I OnlyRestricted to the U of I community idenfinitely during batch ingest of legacy ETD

Deterministic Fluid Models for Internet Congestion Control

113 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2003.We then study the impact of parameter choice of an AQM (active queue management) scheme that can be used at the router. We argue that, depending upon the choice of the parameters of the AQM scheme, one would obtain a rate-based model or a rate-and-queue-based model as the deterministic limit of a stochastic system with a large number of users. We argue that, a virtual-queue-based AQM scheme is very robust to the choice of parameters in achieving a low-loss, low-delay, and high-utilization operation. However, with a real-queue-based marking, the choice of parameters is much more critical.U of I OnlyRestricted to the U of I community idenfinitely during batch ingest of legacy ETD

Rate-Based versus Queue-Based Models of Congestion Control

Mathematical models of congestion control capture the congestion indication mechanism at the router in two different ways: rate-based models, where the queue-length at the router does not explicitly appear in the model, and queue-based models, where the queue length at the router is explicitly a part of the model. Even though most congestion indication mechanisms use the queue length to compute the packet marking or dropping probability to indicate congestion, we argue that, depending upon the choice of the parameters of the AQM scheme, one would obtain a rate-based model or a rate-and-queue-based model as the deterministic limit of a stochastic system with a large number of users

Congestion Control for Fair Resource Allocation in Networks with Multicast Flows

The problem of congestion control in networks with multicast multirate traffic along with unicast sessions has been addressed in this paper. We present a decentralized algorithm which enables the different rate-adaptive receivers in different multicast sessions to adjust their rates to satisfy some fairness criterion. We propose a one-bit ECN marking strategy to be used at the nodes. The congestion control mechanism does not require any per-flow state information for unicast flows at the nodes. Per receiver state information may be required for each multicast flow. The congestion control mechanism takes into account the diverse user requirements when different receivers within a multicast session have different utility functions, but does not assume the network to have any knowledge about the receiver utility functions and also converges under certain reasonable assumptions