Network topology

About: Network topology is a research topic. Over the lifetime, 52259 publications have been published within this topic receiving 1006627 citations.

Information source detection in the SIR model: a sample-path-based approach

Abstract: This paper studies the problem of detecting the information source in a network in which the spread of information follows the popular Susceptible-Infected-Recovered (SIR) model. We assume all nodes in the network are in the susceptible state initially, except one single information source that is in the infected state. Susceptible nodes may then be infected by infected nodes, and infected nodes may recover and will not be infected again after recovery. Given a snapshot of the network, from which we know the graph topology and all infected nodes but cannot distinguish susceptible nodes and recovered nodes, the problem is to find the information source based on the snapshot and the network topology. We develop a sample-path-based approach where the estimator of the information source is chosen to be the root node associated with the sample path that most likely leads to the observed snapshot. We prove for infinite-trees, the estimator is a node that minimizes the maximum distance to the infected nodes. A reverse-infection algorithm is proposed to find such an estimator in general graphs. We prove that for g + 1-regular trees such that gq > 1, where g + 1 is the node degree and is the infection probability, the estimator is within a constant distance from the actual source with a high probability, independent of the number of infected nodes and the time the snapshot is taken. Our simulation results show that for tree networks, the estimator produced by the reverse-infection algorithm is closer to the actual source than the one identified by the closeness centrality heuristic. We then further evaluate the performance of the reverse infection algorithm on several real-world networks.

Radial Network Reconfiguration Using Genetic Algorithm Based on the Matroid Theory

Abstract: This paper deals with distribution network (DN) reconfiguration for loss minimization. To solve this combinatorial problem, a genetic algorithm (GA) is considered. In order to enhance its ability to explore the solution space, efficient genetic operators are developed. After a survey of the existing DN topology description methods, a theoretical approach based on the graph and matroid theories (graphic matroid in particular) is considered. These concepts are used in order to propose new intelligent and effective GA operators for efficient mutation and crossover well dedicated to the DN reconfiguration problem. All resulting individuals after GA operators are claimed to be feasible (radial) configurations. Moreover, the presented approach is valid for planar or nonplanar DN graph topologies and avoids tedious mesh checks for the topology constraint validation. The proposed method is finally compared to some previous topology coding techniques used by other authors. The results show smaller or at least equal power losses with considerably less computation effort.

SPAIN: COTS data-center Ethernet for multipathing over arbitrary topologies

Abstract: Operators of data centers want a scalable network fabric that supports high bisection bandwidth and host mobility, but which costs very little to purchase and administer. Ethernet almost solves the problem - it is cheap and supports high link bandwidths - but traditional Ethernet does not scale, because its spanning-tree topology forces traffic onto a single tree. Many researchers have described "scalable Ethernet" designs to solve the scaling problem, by enabling the use of multiple paths through the network. However, most such designs require specific wiring topologies, which can create deployment problems, or changes to the network switches, which could obviate the commodity pricing of these parts.In this paper, we describe SPAIN ("Smart Path Assignment In Networks"). SPAIN provides multipath forwarding using inexpensive, commodity off-the-shelf (COTS) Ethernet switches, over arbitrary topologies. SPAIN pre-computes a set of paths that exploit the redundancy in a given network topology, then merges these paths into a set of trees; each tree is mapped as a separate VLAN onto the physical Ethernet. SPAIN requires only minor end-host software modifications, including a simple algorithm that chooses between pre-installed paths to efficiently spread load over the network. We demonstrate SPAIN's ability to improve bisection bandwidth over both simulated and experimental data-center networks

QoS-Aware Adaptive Routing in Multi-layer Hierarchical Software Defined Networks: A Reinforcement Learning Approach

Abstract: Software-defined networks (SDNs) have been recognized as the next-generation networking paradigm that decouples the data forwarding from the centralized control. To realize the merits of dedicated QoS provisioning and fast route (re-)configuration services over the decoupled SDNs, various QoS requirements in packet delay, loss, and throughput should be supported by an efficient transportation with respect to each specific application. In this paper, a QoS-aware adaptive routing (QAR) is proposed in the designed multi-layer hierarchical SDNs. Specifically, the distributed hierarchical control plane architecture is employed to minimize signaling delay in large SDNs via three-levels design of controllers, i.e., the super, domain (or master), and slave controllers. Furthermore, QAR algorithm is proposed with the aid of reinforcement learning and QoS-aware reward function, achieving a time-efficient, adaptive, QoS-provisioning packet forwarding. Simulation results confirm that QAR outperforms the existing learning solution and provides fast convergence with QoS provisioning, facilitating the practical implementations in large-scale software service-defined networks.

Fault tolerant deployment and topology control in wireless networks

Abstract: This paper investigate fault tolerance for wireless ad hoc networks. We consider a large-scale of wireless networks whose nodes are distributed randomly in a unit-area square region. Given n wireless nodes V, each with transmission range rn, the wireless networks are often modeled by graph G(V,rn) in which two nodes are connected if their Euclidean distance is no more than rn.We first consider how the transmission range is related with the number of nodes in a fixed area such that the resulted network can sustain k fault nodes with high probability. We show that, for a unit-area square region, the probability that the network G(V,rn) is (k+1)-connected is at least e-e-α when the transmission radius rn satisfies n π rn2 ≥ ln n + (2k-1) ln ln n -2ln k! + 2α for k>0 and n sufficiently large. This result also applies to mobile networks when the moving of wireless nodes always generates randomly distributed positions. Our simulations show that n should be larger than 500 if k=2 or 3 and α = log n and n should be larger than 2500 if k=2 or 3 and α = log log n.We then present a localized method to control the network topology given a (k+1)-faults tolerant deployment G(V,rn) of wireless nodes such that the resulting topology is still (k+1)-faults tolerant but with O(kn) communication links maintained. We show that the constructed topology is also a length spanner. Here a subgraph H is spanner of graph G, if for any two nodes, the length of the shortest path connecting them in H is no more than a small constant factor of the length of the shortest path connecting them in G.Finally, we conduct some simulations to study the practical transmission range to achieve certain probability of k-connected when n is not large enough.