1,550 research outputs found
Space Shuffle: A Scalable, Flexible, and High-Bandwidth Data Center Network
Data center applications require the network to be scalable and
bandwidth-rich. Current data center network architectures often use rigid
topologies to increase network bandwidth. A major limitation is that they can
hardly support incremental network growth. Recent work proposes to use random
interconnects to provide growth flexibility. However routing on a random
topology suffers from control and data plane scalability problems, because
routing decisions require global information and forwarding state cannot be
aggregated. In this paper we design a novel flexible data center network
architecture, Space Shuffle (S2), which applies greedy routing on multiple ring
spaces to achieve high-throughput, scalability, and flexibility. The proposed
greedy routing protocol of S2 effectively exploits the path diversity of
densely connected topologies and enables key-based routing. Extensive
experimental studies show that S2 provides high bisectional bandwidth and
throughput, near-optimal routing path lengths, extremely small forwarding
state, fairness among concurrent data flows, and resiliency to network
failures
An analysis of the lifetime of OLSR networks
The Optimized Link State Routing (OLSR) protocol is a well-known route discovery protocol for ad-hoc networks. OLSR optimizes the flooding of link state information through the network using multipoint relays (MPRs). Only nodes selected as MPRs are responsible for forwarding control traffic. Many research papers aim to optimize the selection of MPRs with a specific purpose in mind: e.g., to minimize their number, to keep paths with high Quality of Service or to maximize the network lifetime (the time until the first node runs out of energy). In such analyzes often the effects of the network structure on the MPR selection are not taken into account. In this paper we show that the structure of the network can have a large impact on the MPR selection. In highly regular structures (such as grids) there is even no variation in the MPR sets that result from various MPR selection mechanisms. Furthermore, we study the influence of the network structure on the network lifetime problem in a setting where at regular intervals messages are broadcasted using MPRs. We introduce the ’maximum forcedness ratio’, as a key parameter of the network to describe how much variation there is in the lifetime results of various MPR selection heuristics. Although we focus our attention to OLSR, being a widely implemented protocol, on a more abstract level our results describe the structure of connected sets dominating the 2-hop neighborhood of a node
Shape from Shading through Shape Evolution
In this paper, we address the shape-from-shading problem by training deep
networks with synthetic images. Unlike conventional approaches that combine
deep learning and synthetic imagery, we propose an approach that does not need
any external shape dataset to render synthetic images. Our approach consists of
two synergistic processes: the evolution of complex shapes from simple
primitives, and the training of a deep network for shape-from-shading. The
evolution generates better shapes guided by the network training, while the
training improves by using the evolved shapes. We show that our approach
achieves state-of-the-art performance on a shape-from-shading benchmark
Dual theory of transmission line outages
A new graph dual formalism is presented for the analysis of line outages in
electricity networks. The dual formalism is based on a consideration of the
flows around closed cycles in the network. After some exposition of the theory
is presented, a new formula for the computation of Line Outage Distribution
Factors (LODFs) is derived, which is not only computationally faster than
existing methods, but also generalizes easily for multiple line outages and
arbitrary changes to line series reactance. In addition, the dual formalism
provides new physical insight for how the effects of line outages propagate
through the network. For example, in a planar network a single line outage can
be shown to induce monotonically decreasing flow changes, which are
mathematically equivalent to an electrostatic dipole field.Comment: 8 pages, 3 figures, 1 table; Accepted at IEEE Transactions on Power
System
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