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Backbone network

About: Backbone network is a research topic. Over the lifetime, 4100 publications have been published within this topic receiving 52938 citations. The topic is also known as: distributed backbone & serial backbone.


Papers
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Proceedings ArticleDOI
13 Mar 2005
TL;DR: It is shown that intelligent channel assignment is critical to Hyacinth's performance, and distributed algorithms that utilize only local traffic load information to dynamically assign channels and to route packets are presented, and their performance is compared against a centralized algorithm that performs the same functions.
Abstract: Even though multiple non-overlapped channels exist in the 2.4 GHz and 5 GHz spectrum, most IEEE 802.11-based multi-hop ad hoc networks today use only a single channel. As a result, these networks rarely can fully exploit the aggregate bandwidth available in the radio spectrum provisioned by the standards. This prevents them from being used as an ISP's wireless last-mile access network or as a wireless enterprise backbone network. In this paper, we propose a multi-channel wireless mesh network (WMN) architecture (called Hyacinth) that equips each mesh network node with multiple 802.11 network interface cards (NICs). The central design issues of this multi-channel WMN architecture are channel assignment and routing. We show that intelligent channel assignment is critical to Hyacinth's performance, present distributed algorithms that utilize only local traffic load information to dynamically assign channels and to route packets, and compare their performance against a centralized algorithm that performs the same functions. Through an extensive simulation study, we show that even with just 2 NICs on each node, it is possible to improve the network throughput by a factor of 6 to 7 when compared with the conventional single-channel ad hoc network architecture. We also describe and evaluate a 9-node Hyacinth prototype that Is built using commodity PCs each equipped with two 802.11a NICs.

1,636 citations

Proceedings ArticleDOI
25 Apr 2019
TL;DR: A simplified network based on a query-independent formulation, which maintains the accuracy of NLNet but with significantly less computation is created, and this simplified design shares similar structure with Squeeze-Excitation Network (SENet), which generally outperforms both simplified NLNet and SENet on major benchmarks for various recognition tasks.
Abstract: The Non-Local Network (NLNet) presents a pioneering approach for capturing long-range dependencies, via aggregating query-specific global context to each query position. However, through a rigorous empirical analysis, we have found that the global contexts modeled by non-local network are almost the same for different query positions within an image. In this paper, we take advantage of this finding to create a simplified network based on a query-independent formulation, which maintains the accuracy of NLNet but with significantly less computation. We further observe that this simplified design shares similar structure with Squeeze-Excitation Network (SENet). Hence we unify them into a three-step general framework for global context modeling. Within the general framework, we design a better instantiation, called the global context (GC) block, which is lightweight and can effectively model the global context. The lightweight property allows us to apply it for multiple layers in a backbone network to construct a global context network (GCNet), which generally outperforms both simplified NLNet and SENet on major benchmarks for various recognition tasks.

1,202 citations

Proceedings ArticleDOI
08 Jun 1997
TL;DR: This paper uses an approximation to the minimum connected dominating set (MCDS) of the ad-hoc network topology as the virtual backbone, and maintains local copies of the global topology of the network, along with shortest paths between all pairs of nodes.
Abstract: We impose a virtual backbone structure on the ad-hoc network, in order to support unicast, multicast, and fault-tolerant routing within the ad-hoc network. This virtual backbone differs from the wired backbone of cellular networks in two key ways: (a) it may change as nodes move, and (b) it is not used primarily for routing packets or flows, but only for computing and updating routes. The primary routes for packets and flows are still computed by a shortest-paths computation; the virtual backbone can, if necessary provide backup routes to handle interim failures. Because of the dynamic nature of the virtual backbone, our approach splits the routing problem into two levels: (a) find and update the virtual backbone, and (b) then find and update routes. The key contribution of this paper is to describe several alternatives for the first part of finding and updating the virtual backbone. To keep the virtual backbone as small as possible we use an approximation to the minimum connected dominating set (MCDS) of the ad-hoc network topology as the virtual backbone. The hosts in the MCDS maintain local copies of the global topology of the network, along with shortest paths between all pairs of nodes.

836 citations

Posted Content
TL;DR: A simple and modular Split-Attention block that enables attention across feature-map groups ResNet-style is presented that preserves the overall ResNet structure to be used in downstream tasks straightforwardly without introducing additional computational costs.
Abstract: It is well known that featuremap attention and multi-path representation are important for visual recognition. In this paper, we present a modularized architecture, which applies the channel-wise attention on different network branches to leverage their success in capturing cross-feature interactions and learning diverse representations. Our design results in a simple and unified computation block, which can be parameterized using only a few variables. Our model, named ResNeSt, outperforms EfficientNet in accuracy and latency trade-off on image classification. In addition, ResNeSt has achieved superior transfer learning results on several public benchmarks serving as the backbone, and has been adopted by the winning entries of COCO-LVIS challenge. The source code for complete system and pretrained models are publicly available.

822 citations

Proceedings Article
25 Apr 2012
TL;DR: The goal is to automatically find an important class of failures, regardless of the protocols running, for both operational and experimental networks, with a general and protocol-agnostic framework, called Header Space Analysis (HSA).
Abstract: Today's networks typically carry or deploy dozens of protocols and mechanisms simultaneously such as MPLS, NAT, ACLs and route redistribution. Even when individual protocols function correctly, failures can arise from the complex interactions of their aggregate, requiring network administrators to be masters of detail. Our goal is to automatically find an important class of failures, regardless of the protocols running, for both operational and experimental networks. To this end we developed a general and protocol-agnostic framework, called Header Space Analysis (HSA). Our formalism allows us to statically check network specifications and configurations to identify an important class of failures such as Reachability Failures, Forwarding Loops and Traffic Isolation and Leakage problems. In HSA, protocol header fields are not first class entities; instead we look at the entire packet header as a concatenation of bits without any associated meaning. Each packet is a point in the {0,1}L space where L is the maximum length of a packet header, and networking boxes transform packets from one point in the space to another point or set of points (multicast). We created a library of tools, called Hassel, to implement our framework, and used it to analyze a variety of networks and protocols. Hassel was used to analyze the Stanford University backbone network, and found all the forwarding loops in less than 10 minutes, and verified reachability constraints between two subnets in 13 seconds. It also found a large and complex loop in an experimental loose source routing protocol in 4 minutes.

756 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023121
2022276
2021249
2020212
2019223
2018190