112,644 research outputs found
Sensor Placement for Learning in Flow Networks
Large infrastructure networks (e.g. for transportation and power
distribution) require constant monitoring for failures, congestion, and other
adversarial events. However, assigning a sensor to every link in the network is
often infeasible due to placement and maintenance costs. Instead, sensors can
be placed only on a few key links, and machine learning algorithms can be
leveraged for the inference of missing measurements (e.g. traffic counts, power
flows) across the network. This paper investigates the sensor placement problem
for networks. We first formalize the problem under a flow conservation
assumption and show that it is NP-hard to place a fixed set of sensors
optimally. Next, we propose an efficient and adaptive greedy heuristic for
sensor placement that scales to large networks. Our experiments, using datasets
from real-world application domains, show that the proposed approach enables
more accurate inference than existing alternatives from the literature. We
demonstrate that considering even imperfect or incomplete ground-truth
estimates can vastly improve the prediction error, especially when a small
number of sensors is available.Comment: 9 pages, 6 figure
Service Chaining Placement Based on Satellite Mission Planning in Ground Station Networks
As the increase in satellite number and variety, satellite ground stations should be required to offer user services in a flexible and efficient manner. Network function virtualization (NFV) can provide a new paradigm to allocate network resources on-demand for user services over the underlying network. However, most of the existing work focuses on the virtual network function (VNF) placement and routing traffic problem for enterprise data center networks, the issue needs to further study in satellite communication scenarios. In this paper, we investigate the VNF placement and routing traffic problem in satellite ground station networks. We formulate the problem of resource allocation as an integer linear programming (ILP) model and the objective is to minimize the link resource utilization and the number of servers used. Considering the information about satellite orbit fixation and mission planning, we propose location-aware resource allocation (LARA) algorithms based on Greedy and IBM CPLEX 12.10, respectively. The proposed LARA algorithm can assist in deploying VNFs and routing traffic flows by predicting the running conditions of user services. We evaluate the performance of our proposed LARA algorithm in three networks of Fat-Tree, BCube, and VL2. Simulation results show that our proposed LARA algorithm performs better than that without prediction, and can effectively decrease the average resource utilization of satellite ground station networks
NeuRoute: Predictive Dynamic Routing for Software-Defined Networks
This paper introduces NeuRoute, a dynamic routing framework for Software
Defined Networks (SDN) entirely based on machine learning, specifically, Neural
Networks. Current SDN/OpenFlow controllers use a default routing based on
Dijkstra algorithm for shortest paths, and provide APIs to develop custom
routing applications. NeuRoute is a controller-agnostic dynamic routing
framework that (i) predicts traffic matrix in real time, (ii) uses a neural
network to learn traffic characteristics and (iii) generates forwarding rules
accordingly to optimize the network throughput. NeuRoute achieves the same
results as the most efficient dynamic routing heuristic but in much less
execution time.Comment: Accepted for CNSM 201
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