An example method for discovering and grouping application endpoints in a network environment is provided and includes discovering endpoints communicating in a network environment, calculating affinity between the discovered endpoints, and grouping the endpoints into separate endpoint groups (EPGs) according to the calculated affinity, each EPG comprising a logical grouping of similar endpoints for applying common forwarding and policy logic according to logical application boundaries. In specific embodiments, the affinity includes a weighted average of network affinity, compute affinity and user specified affinity.

Discovering and grouping application endpoints in a network environment

Systems, methods, and computer-readable media for handling failure scenarios during data collection and analysis for assurance. In some examples, a system can obtain a logical model of a network and, based on the logical model, identify a plurality of controllers on the network and a plurality of nodes on a fabric of the network. The system can probe the plurality of controllers and plurality of nodes for respective status information, including respective reachability information, respective login diagnostics information, and/or respective software information. Based on the respective status information, the system can determine conditions at the plurality of controllers and the plurality of nodes and define one or more assurance operations based on the conditions at the plurality of controllers and the plurality of nodes. The system can then perform the one or more assurance operations.

Handling controller and node failure scenarios during data collection

Disclosed are systems, methods, and computer-readable media for assuring tenant forwarding in a network environment. Network assurance can be determined in layer 1, layer 2 and layer 3 of the networked environment including, internal-internal (e.g., inter-fabric) forwarding and internal-external (e.g., outside the fabric) forwarding in the networked environment. The network assurance can be performed using logical configurations, software configurations and/or hardware configurations

Validation of routing information base-forwarding information base equivalence in a network

Disclosed are systems, methods, and computer-readable media for assuring tenant forwarding in a network environment. Network assurance can be determined in layer 1, layer 2 and layer 3 of the networked environment including, internal-internal (e.g., inter-fabric) forwarding and internal-external (e.g., outside the fabric) forwarding in the networked environment. The network assurance can be performed using logical configurations, software configurations and/or hardware configurations.

Validation of layer 2 interface and VLAN in a networked environment

Network validation between the logical level and the hardware level of a network

A network element and method for marking traffic in a packet-switched data network which comprises a distributed hierarchical naming system and the network element The network element comprises a marking rule store for storing marking rules, in which each marking rule is indicative of a quality of service level allocated to traffic on a connection impinging on the network element The network element derives from the distributed hierarchical naming system quality of service level data for a connection, stores in the marking rule store a marking rule for the connection based on the retrieved quality of service level data and marks traffic on the connection in accordance with the marking rule

Managing the quality of service levels (QoS) of network traffic

This document specifies the application of YANG modeling language to
JSON RPC 2.0 for the purposes of achieving interoperability between
implementations.

Application of YANG Modeling to JSON RPCs for Interoperability Purposes

Refactoring is a critical task in software maintenance, and is usually performed to enforce better design and coding practices, while coping with design defects. The Extract Method refactoring is widely used for merging duplicate code fragments into a single new method. Several studies attempted to recommend Extract Method refactoring opportunities using different techniques, including program slicing, program dependency graph analysis, change history analysis, structural similarity, and feature extraction. However, irrespective of the method, most of the existing approaches interfere with the developer’s workflow: they require the developer to stop coding and analyze the suggested opportunities, and also consider all refactoring suggestions in the entire project without focusing on the development context. To increase the adoption of the Extract Method refactoring, in this paper, we aim to investigate the effectiveness of machine learning and deep learning algorithms for its recommendation while maintaining the workflow of the developer. The proposed approach relies on mining prior applied Extract Method refactorings and extracting their features to train a deep learning classifier that detects them in the user’s code. We implemented our approach as a plugin for IntelliJ IDEA called AntiCopyPaster. To develop our approach, we trained and evaluated various popular models on a dataset of 18,942 code fragments from 13 Open Source Apache projects. The results show that the best model is the Convolutional Neural Network (CNN), which recommends appropriate Extract Method refactorings with an F-measure of 0.82. We also conducted a qualitative study with 72 developers to evaluate the usefulness of the developed plugin. The results show that developers tend to appreciate the idea of the approach and are satisfied with various aspects of the plugin’s operation. 

/pdf/just-in-time-code-duplicates-extraction-1v63kpi7.pdf

Just-in-Time Code Duplicates Extraction

Learning Just-in-Time Code Duplicates Extraction

A network element and method for marking traffic in a packet-switched data network which comprises a distributed hierarchical naming system and the network element. The network element comprises a marking rule store for storing marking rules, in which each marking rule is indicative of a quality of service level allocated to traffic on a connection impinging on the network element. The network element derives from the distributed hierarchical naming system quality of service level data for a connection, stores in the marking rule store a marking rule for the connection based on the retrieved quality of service level data and marks traffic on the connection in accordance with the marking rule.

Anton Radostinovinch Ivanov

Papers

Managing the quality of service levels (QoS) of network traffic

Application of YANG Modeling to JSON RPCs for Interoperability Purposes

Just-in-Time Code Duplicates Extraction

Learning Just-in-Time Code Duplicates Extraction

Deriving quality of service data from a distributed hierarchical naming system