Umbrella : A deployable SDN-enabled IXP switching fabric

Software Defined internet eXchange Points (SDXs) are a promising solution to the long-standing limitations and problems of interdomain routing. While proposed SDX architectures have improved the scalability of the control plane, these solutions have ignored the underlying fabric upon which they should be deployed. This work makes the case for a new fabric architecture that proposes stronger control and data plane separation

Characterizing a Meta-CDN

CDNs have reshaped the Internet architecture at large. They operate (globally) distributed networks of servers to reduce latencies as well as to increase availability for content and to handle large traffic bursts. Traditionally, content providers were mostly limited to a single CDN operator. However, in recent years, more and more content providers employ multiple CDNs to serve the same content and provide the same services. Thus, switching between CDNs, which can be beneficial to reduce costs or to select CDNs by optimal performance in different geographic regions or to overcome CDN-specific outages, becomes an important task. Services that tackle this task emerged, also known as CDN broker, Multi-CDN selectors, or Meta-CDNs. Despite their existence, little is known about Meta-CDN operation in the wild. In this paper, we thus shed light on this topic by dissecting a major Meta-CDN. Our analysis provides insights into its infrastructure, its operation in practice, and its usage by Internet sites. We leverage PlanetLab and Ripe Atlas as distributed infrastructures to study how a Meta-CDN impacts the web latency

Space Generic Open Avionics Architecture (SGOAA) reference model technical guide

This report presents a full description of the Space Generic Open Avionics Architecture (SGOAA). The SGOAA consists of a generic system architecture for the entities in spacecraft avionics, a generic processing architecture, and a six class model of interfaces in a hardware/software system. The purpose of the SGOAA is to provide an umbrella set of requirements for applying the generic architecture interface model to the design of specific avionics hardware/software systems. The SGOAA defines a generic set of system interface points to facilitate identification of critical interfaces and establishes the requirements for applying appropriate low level detailed implementation standards to those interface points. The generic core avionics system and processing architecture models provided herein are robustly tailorable to specific system applications and provide a platform upon which the interface model is to be applied

Rethinking IXPs' architecture in the age of SDN

© 2018 IEEE. Software-defined Internet eXchange points (SDXs) are a promising solution to the long-standing limitations and problems of interdomain routing. While the proposed SDX architectures have improved the scalability of the control plane, these solutions have ignored the underlying fabric upon which they should be deployed. In this paper, we present Umbrella, a software-defined interconnection fabric that complements and enhances those architectures. Umbrella is a switching fabric architecture and management approach that improves the overall robustness, limiting control plane dependence, and suitable for the topology of any existing Internet eXchange Point (IXP). We validate Umbrella through a real-world deployment on two production IXPs, TouSIX and NSPIXP-3, and demonstrate its use in practice, sharing our experience of the challenges faced

ENDEAVOUR: A Scalable SDN Architecture For Real-World IXPs.

Innovation in interdomain routing has remained stagnant for over a decade. Recently, IXPs have emerged as economically-advantageous interconnection points for reducing path latencies and exchanging ever increasing traffic volumes among, possibly, hundreds of networks. Given their far-reaching implications on interdomain routing, IXPs are the ideal place to foster network innovation and extend the benefits of SDN to the interdomain level. In this paper, we present, evaluate, and demonstrate EN- DEAVOUR, an SDN platform for IXPs. ENDEAVOUR can be deployed on a multi-hop IXP fabric, supports a large number of use cases, and is highly-scalable while avoiding broadcast storms. Our evaluation with real data from one of the largest IXPs, demonstrates the benefits and scalability of our solution: ENDEAVOUR requires around 70% fewer rules than alternative SDN solutions thanks to our rule partitioning mechanism. In addition, by providing an open source solution, we invite ev- eryone from the community to experiment (and improve) our implementation as well as adapt it to new use cases.European Union’s Horizon 2020 research and innovation programme under the ENDEAVOUR project (grant agreement 644960)

3D Point Capsule Networks

In this paper, we propose 3D point-capsule networks, an auto-encoder designed to process sparse 3D point clouds while preserving spatial arrangements of the input data. 3D capsule networks arise as a direct consequence of our novel unified 3D auto-encoder formulation. Their dynamic routing scheme and the peculiar 2D latent space deployed by our approach bring in improvements for several common point cloud-related tasks, such as object classification, object reconstruction and part segmentation as substantiated by our extensive evaluations. Moreover, it enables new applications such as part interpolation and replacement.Comment: As published in CVPR 2019 (camera ready version), with supplementary materia

3D Point Capsule Networks

In this paper, we propose 3D point-capsule networks, an auto-encoder designed to process sparse 3D point clouds while preserving spatial arrangements of the input data. 3D capsule networks arise as a direct consequence of our novel unified 3D auto-encoder formulation. Their dynamic routing scheme and the peculiar 2D latent space deployed by our approach bring in improvements for several common point cloud-related tasks, such as object classification, object reconstruction and part segmentation as substantiated by our extensive evaluations. Moreover, it enables new applications such as part interpolation and replacement

Dynamic Composition of Functions for Modular Learning

Compositionality is useful to reduce the complexity of machine learning models and increase their generalization capabilities, because new problems can be linked to the composition of existing solutions. Recent work has shown that compositional approaches can offer substantial benefits over a wide variety of tasks, from multi-task learning over visual question-answering to natural language inference, among others. A key variant is functional compositionality, where a meta-learner composes different (trainable) functions into complex machine learning models. In this thesis, I generalize existing approaches to functional compositionality under the umbrella of the routing paradigm, where trainable arbitrary functions are \u27stacked\u27 to form complex machine learning models