The Experts below are selected from a list of 150948 Experts worldwide ranked by ideXlab platform

Nick Feamster - One of the best experts on this subject based on the ideXlab platform.

  • improving network management with software defined networking
    IEEE Communications Magazine, 2013
    Co-Authors: Nick Feamster
    Abstract:

    Network management is challenging. To operate, maintain, and secure a communication network, network operators must grapple with low-Level vendor-specific configuration to implement complex High-Level network policies. Despite many previous proposals to make networks easier to manage, many solutions to network management problems amount to stop-gap solutions because of the difficulty of changing the underlying infrastructure. The rigidity of the underlying infrastructure presents few possibilities for innovation or improvement, since network devices have generally been closed, proprietary, and vertically integrated. A new paradigm in networking, software defined networking (SDN), advocates separating the data plane and the control plane, making network switches in the data plane simple packet forwarding devices and leaving a logically centralized software program to control the behavior of the entire network. SDN introduces new possibilities for network management and configuration methods. In this article, we identify problems with the current state-of-the-art network configuration and management mechanisms and introduce mechanisms to improve various aspects of network management. We focus on three problems in network management: enabling frequent changes to network conditions and state, providing support for network configuration in a HighLevel language, and providing better visibility and control over tasks for performing network diagnosis and troubleshooting. The technologies we describe enable network operators to implement a wide range of network policies in a High-Level Policy language and easily determine sources of performance problems. In addition to the systems themselves, we describe various prototype deployments in campus and home networks that demonstrate how SDN can improve common network management tasks.

Yangxuan Wu - One of the best experts on this subject based on the ideXlab platform.

  • Automatic belief network modeling via Policy inference for SDN fault localization
    Journal of Internet Services and Applications, 2016
    Co-Authors: Yongning Tang, Khalid Elmansor, Zhiwei Xu, Guang Cheng, Feng Chen, Yangxuan Wu
    Abstract:

    Fault localization for SDN becomes one of the most critical but difficult tasks. Existing tools typically only address a specific part of the problem (e.g., control plane verification, flow checker). In this paper, we propose a new approach to tackle SDN fault localization by automatically Modeling via Policy Inference (called MPI) the causality between SDN faults and their symptoms to a belief network. In the MPI system, a service oriented High Level Policy language is used to specify network services provisioned between end nodes. MPI parses each service provisioning Policy to a logical Policy view, which consists of a pair of logical end nodes, a traffic pattern specification, and a list of required network functions (or a service function chain). An SDN controller takes the policies from multiple parties and provisions the requested services on its orchestrated SDN network. MPI queries the controller about the network topology and retrieves flow rules from all SDN switches. MPI maps the Policy view to the corresponding implementation view, in which all the logical components in the Policy view are mapped to the actual system components along with the actual network topology. Referring to the component causality graph templates derived from SDN reference model, the implementation view of the current running network services can be modeled as a belief network. A heuristic fault reasoning algorithm is adopted to search for the most likely root causes. MPI has been evaluated in both a simulation environment and a real network system for its accuracy and efficiency. The evaluation shows that MPI is a Highly scalable, effective and flexible modeling approach to tackle fault localization challenges in a Highly dynamic and agile SDN network.

Edmundo Arrios - One of the best experts on this subject based on the ideXlab platform.

  • scaling up agroforestry requires research in rather than for development
    Current Opinion in Environmental Sustainability, 2014
    Co-Authors: R Coe, F L Sinclai, Edmundo Arrios
    Abstract:

    Recent High-Level Policy papers call for scaling-up agroforestry to sustainably increase agricultural production and maintain environmental services. Evidence suggests that this will not be achieved by wide scale promotion of a few iconic agroforestry practices. Instead, three key issues need to be addressed. First, fine-scale variation in social, economic and ecological context and how this creates a need for local adaptation. Second, the importance of developing appropriate service delivery mechanisms, markets, and institutional contexts, as well as technologies. Third, appropriate research design, within the scaling process, that enables co-learning amongst research, development and private sector actors. This requires a new paradigm that builds on previous integrated systems approaches, but goes further, by embedding research centrally within development praxis.

Ricardo Uauy - One of the best experts on this subject based on the ideXlab platform.

  • food livestock production energy climate change and health
    The Lancet, 2007
    Co-Authors: Anthony J. Mcmichael, Colin D Butler, John Powles, Ricardo Uauy
    Abstract:

    Food provides energy and nutrients, but its acquisition requires energy expenditure. In post-hunter-gatherer societies, extra-somatic energy has greatly expanded and intensified the catching, gathering, and production of food. Modern relations between energy, food, and health are very complex, raising serious, High-Level Policy challenges. Together with persistent widespread under-nutrition, over-nutrition (and sedentarism) is causing obesity and associated serious health consequences. Worldwide, agricultural activity, especially livestock production, accounts for about a fifth of total greenhouse-gas emissions, thus contributing to climate change and its adverse health consequences, including the threat to food yields in many regions. Particular Policy attention should be paid to the health risks posed by the rapid worldwide growth in meat consumption, both by exacerbating climate change and by directly contributing to certain diseases. To prevent increased greenhouse-gas emissions from this production sector, both the average worldwide consumption Level of animal products and the intensity of emissions from livestock production must be reduced. An international contraction and convergence strategy offers a feasible route to such a goal. The current global average meat consumption is 100 g per person per day, with about a ten-fold variation between High-consuming and low-consuming populations. 90 g per day is proposed as a working global target, shared more evenly, with not more than 50 g per day coming from red meat from ruminants (ie, cattle, sheep, goats, and other digastric grazers).

Ken Caluwaerts - One of the best experts on this subject based on the ideXlab platform.

  • Hierarchical Reinforcement Learning for Quadruped Locomotion.
    arXiv: Learning, 2019
    Co-Authors: Deepali Jain, Atil Iscen, Ken Caluwaerts
    Abstract:

    Legged locomotion is a challenging task for learning algorithms, especially when the task requires a diverse set of primitive behaviors. To solve these problems, we introduce a hierarchical framework to automatically decompose complex locomotion tasks. A High-Level Policy issues commands in a latent space and also selects for how long the low-Level Policy will execute the latent command. Concurrently, the low-Level Policy uses the latent command and only the robot's on-board sensors to control the robot's actuators. Our approach allows the High-Level Policy to run at a lower frequency than the low-Level one. We test our framework on a path-following task for a dynamic quadruped robot and we show that steering behaviors automatically emerge in the latent command space as low-Level skills are needed for this task. We then show efficient adaptation of the trained Policy to a different task by transfer of the trained low-Level Policy. Finally, we validate the policies on a real quadruped robot. To the best of our knowledge, this is the first application of end-to-end hierarchical learning to a real robotic locomotion task.

  • IROS - Hierarchical Reinforcement Learning for Quadruped Locomotion
    2019 IEEE RSJ International Conference on Intelligent Robots and Systems (IROS), 2019
    Co-Authors: Deepali Jain, Atil Iscen, Ken Caluwaerts
    Abstract:

    Legged locomotion is a challenging task for learning algorithms, especially when the task requires a diverse set of primitive behaviors. To solve these problems, we introduce a hierarchical framework that can automatically learn to decompose complex locomotion tasks. A High-Level Policy issues commands in the form of a latent vector and also selects for how long the low-Level Policy will execute the latent command. Concurrently, the low-Level Policy uses the latent command and only the robot’s on-board sensors to control the robot’s actuators. Our approach allows the High-Level Policy to run at a lower frequency than the low-Level one. We test our framework on a path-following task for a dynamic quadruped robot and we show that steering behaviors automatically emerge in the latent command space as low-Level skills are needed for this task. We then show efficient adaptation of the trained Policy to new tasks by transfer of the trained low-Level Policy. Finally, we validate the policies on a real quadruped robot. To the best of our knowledge, this is the first application of end-to-end hierarchical learning to a real robotic locomotion task.