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Intrinsic brain activity is characterized by highly organized co-activations between different regions, forming clustered spatial patterns referred to as resting-state networks. The observed co-activation patterns are sustained by...
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Intrinsic brain activity is characterized by highly organized co-activations between different regions, forming clustered spatial patterns referred to as resting-state networks. The observed co-activation patterns are sustained by the intricate fabric of millions of interconnected neurons constituting the brain's wiring diagram. However, as for other real networks, the relationship between the connectional structure and the emergent collective dynamics still evades complete understanding. Here, we show that it is possible to estimate the expected pair-wise correlations that a network tends to generate thanks to the underlying path structure. We start from the assumption that in order for two nodes to exhibit correlated activity, they must be exposed to similar input patterns from the entire network. We then acknowledge that information rarely spreads only along a unique route but rather travels along all possible paths. In real networks, the strength of local perturbations tends to decay as they propagate away from the sources, leading to a progressive attenuation of the original information content and, thus, of their influence. Accordingly, we define a novel graph measure, topological similarity, which quantifies the propensity of two nodes to dynamically correlate as a function of the resemblance of the overall influences they are expected to receive due to the underlying structure of the network. Applied to the human brain, we find that the similarity of whole-network inputs, estimated from the topology of the anatomical connectome, plays an important role in sculpting the backbone pattern of time-average correlations observed at rest.
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In this paper, we strive to improve the throughput of heterogeneous cellular networks by exploiting the pre-cached files at user end to manage interference. We consider a transmission scheme, where network-coded multicast is emplo...
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In this paper, we strive to improve the throughput of heterogeneous cellular networks by exploiting the pre-cached files at user end to manage interference. We consider a transmission scheme, where network-coded multicast is employed to help cancel multi-user interference, and reconstructed interference cancellation (RIC) is used to help eliminate inter-cell interference. Because RIC is opportunistic, base station (BS) muting is used to coordinate the residual strong interference. Since user association affects residual interference and is coupled with BS muting while both are operated in a very different timescale from content caching, we jointly optimize user association and BS muting for a given caching policy to maximize the number of users simultaneously served by the transmission scheme. By transforming the formulated problem into a maximal independent set problem with constructed conflict graph, the global optimal solution is found with graph theory methods. By exploiting the topology feature of heterogeneous networks, we proceed to propose two low-complexity algorithms, respectively, implemented in a centralized and distributed manner, which are viable for large-scale networks. Simulation results show that the optimized transmission scheme achieves a remarkable performance gain over the existing schemes.
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By using tomography-based topology inference method, attackers can infer the topology of a network without the collaboration of the internal nodes in that network, which can greatly improve the efficiency of their subsequent link ...
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By using tomography-based topology inference method, attackers can infer the topology of a network without the collaboration of the internal nodes in that network, which can greatly improve the efficiency of their subsequent link flood attack (LFA) behaviors. In order to defend against adversarial tomography-based topology inference, we propose a network topology obfuscation mechanism named AntiTomo, which is a proactive deception based network anti-reconnaissance method. By providing the attackers with well-designed obfuscated path measurement metrics, AntiTomo can lead the attackers to form a fake network topology view, which hides the key elements (i.e., the key links and the key nodes) of the physical network. To generate an obfuscated network topology with high deceptive features efficiently, AntiTomo uses the multi-objective optimization model to construct the obfuscated topology with security and low-cost features. Our experimental analysis based on several typical real network topologies shows that AntiTomo can generate an effective obfuscated network topology with high deceptive, low cost, and high efficiency, which can defend against tomography-based network topology reconnaissance effectively.
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We address the problem of generating physical realistic optical transport network topologies. This type of network has characteristics that differ from scale-free networks, such as the Internet. Based on the analysis of a set of r...
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We address the problem of generating physical realistic optical transport network topologies. This type of network has characteristics that differ from scale-free networks, such as the Internet. Based on the analysis of a set of real transport topologies, we identify and assess relevant characteristics. A method to generate realistic topologies is proposed. The proposed method is validated by comparing the characteristics of computer-generated and real-world optical transport networks.
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Networking research often requires realistic topologies to study performance and resilience. This project introduces TopoHub, an open repository of reference network topologies of variety of network sizes based on the Gabriel grap...
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Networking research often requires realistic topologies to study performance and resilience. This project introduces TopoHub, an open repository of reference network topologies of variety of network sizes based on the Gabriel graph model. The accompanying Python package offers functionalities for topology generation, analysis, and integration with the Mininet network emulator. A web interface allows users to explore topologies, including visualization of link utilization under various traffic demands. The project aims to provide a comprehensive topology data source for networking researchers, enabling standardized benchmarking and reproducible research.
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摘要 :
Networking research often requires realistic topologies to study performance and resilience. This project introduces TopoHub, an open repository of reference network topologies of variety of network sizes based on the Gabriel grap...
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Networking research often requires realistic topologies to study performance and resilience. This project introduces TopoHub, an open repository of reference network topologies of variety of network sizes based on the Gabriel graph model. The accompanying Python package offers functionalities for topology generation, analysis, and integration with the Mininet network emulator. A web interface allows users to explore topologies, including visualization of link utilization under various traffic demands. The project aims to provide a comprehensive topology data source for networking researchers, enabling standardized benchmarking and reproducible research.
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The rapid development of network function virtualization (NFV) enables a communication network to provide in-network services using virtual network functions (VNFs) deployed on general IT hardware. While existing studies on NFV fo...
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The rapid development of network function virtualization (NFV) enables a communication network to provide in-network services using virtual network functions (VNFs) deployed on general IT hardware. While existing studies on NFV focused on how to provision VNFs from the provider's perspective, little is done about how to validate the provisioned resources from the user's perspective. In this work, we take a first step towards this problem by developing an inference framework designed to "look into" the NFV network. Our framework infers the structure and state of the overlay formed by VNF instances, ingress/egress points of measurement flows, and critical points on their paths (branching/joining points). Our solution only uses external observations such as the required service chains and the end-to-end performance measurements. Besides the novel application scenario, our work also fundamentally advances the state of the art on topology inference by considering (i) general topologies with general measurement paths, and (ii) information of service chains. Our evaluations show that the proposed solution significantly improves both the reconstruction accuracy and the inference accuracy over existing solutions, and service chain information is critical in revealing the structure of the underlying topology.
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Information-Centric Networking (ICN) has recently emerged as a result of the increased demand to access contents regardless of their location in the network services. This new approach facilitates content distribution as a service...
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Information-Centric Networking (ICN) has recently emerged as a result of the increased demand to access contents regardless of their location in the network services. This new approach facilitates content distribution as a service of the network with lower delay and higher security in comparison with the current IP network. Applying ICN in current IP infrastructure leads to major complexities. One approach to deploy ICN with less complexity is to integrate ICN with Software Defined Networking (SDN). The SDN controller manages the content distribution, caching, and routing based on the users' requests. In this paper, we extend these context by addressing the ICN topology management problem over the SDN network to achieve an improved user experience as well as network performance. In particular, a centralized controller is designed to construct and manage the ICN overlay. Experimental results indicate that this adopted topology management strategy achieves high performance, in terms of low failure in interest satisfaction and reduced download time compared to a plain ICN.
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Thermostatically controlled load (TCL, such as heating, ventilation, and air conditioning system) is a desirable demand-side flexibility source in distribution networks. It can participate in regulation services and mitigate power...
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Thermostatically controlled load (TCL, such as heating, ventilation, and air conditioning system) is a desirable demand-side flexibility source in distribution networks. It can participate in regulation services and mitigate power imbalances from fluctuating distributed renewable generation. To effectively utilize the load flexibility from spatially and temporally distributed TCLs in a distribution network, it is necessary to consider power flow constraints to avoid possible voltage or current violations. Published works usually adopt optimal power flow models (OPF) to describe these constraints. However, these models require accurate topology of the distribution network that is often unobservable in practice. To bypass this challenge, this paper proposes a novel learning-based OPF to optimize TCLs for regulation services. This method trains three regression multi-layer perceptrons (MLPs) based on the distribution network's historical operation data to replicate its power flow constraints. The trained MLPs are further equivalently reformulated into linear constraints with binary variables so that the optimization problem becomes a mixed-integer linear program that can be effectively solved. Numerical experiments based on the IEEE 123-bus system validate that the proposed method can achieve better TCL power scheduling performance with guaranteed feasibility and optimality than other state-of-art models.
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We propose a network architecture consisting of long-reach passive optical network-based access and a transparent optical core network. The end users are connected to the remote node or the local exchange (LE) through an optical d...
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We propose a network architecture consisting of long-reach passive optical network-based access and a transparent optical core network. The end users are connected to the remote node or the local exchange (LE) through an optical distribution network, and the remote node is connected by disjoint feeder fiber links to central offices located in two distinct metro/core (MC) nodes. This method of connecting a single remote node to two geographically separate MC nodes for dedicated protection in case of feeder fiber failure is referred to as dual homing. In this work, we explore the benefits of dual homing in the access in simultaneously providing better resilience and load balancing in the core network considering connections between LEs. While looking into the benefits of dual homing in terms of network resiliency, we also explore whether the path redundancies added by dual homing play a role in providing efficient distribution of load across the core network and thereby reduce the cost of provisioning capacity in terms of number of lightpaths, transponders, etc. Dual homing at both the source and destination LEs offers more options for paths between LEs through the core network. Our results show that dual-homed access proves to be advantageous over single-homed access in terms of enhancing both core network resiliency and facilitating better load balancing.
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