# US 2016/0366017 A1 · Method of network traffic management in information and communication systems

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- Number: US 2016/0366017 A1
- Document ID: US20160366017A1
- Status: Published application
- Kind: A1
- Application number: 14/773,408
- Filed: 2015-06-09
- Published: 2016-12-15
- Earliest claimed: 2015-06-09
- Assignees: Allnet Broker Sp. Z O. O.
- Applicants: Allnet Broker Sp. Zo. o.
- Inventors: Krzysztof Graczykowski
- Web page: https://patentgrep.com/patent/US20160366017A1

## CPC

- H04L 41/0816: the condition being an adaptation, e.g. in response to network events
- G05B 13/029: {using neural networks and expert systems}
- G16B 40/00: ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
- H04L 41/0823: characterised by the purposes of a change of settings, e.g. optimising configuration for enhancing reliability (for optimising operational conditions of wireless networks H04W24/02)
- H04L 41/0894: Policy-based network configuration management
- H04L 41/142: using statistical or mathematical methods
- H04L 41/16: using machine learning or artificial intelligence
- H04L 41/5003: Managing SLA; Interaction between SLA and QoS
- H04L 41/5025: by proactively reacting to service quality change, e.g. by reconfiguration after service quality degradation or upgrade
- H04L 43/08: Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters
- H04L 47/24: Traffic characterised by specific attributes, e.g. priority or QoS
- H04L 47/821: {Prioritising resource allocation or reservation requests}
- H04L 47/822: {Collecting or measuring resource availability data}
- H04L 47/83: based on usage prediction
- H04L 67/535: {Tracking the activity of the user (network monitoring arrangements H04L43/00; recording of computer activity G06F11/34)}
- H04L: TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION (arrangements common to telegraphic and telephonic communication H04M)
- G05B: CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS (systems for controlling or regulating non-electric variables G05D; systems for regulating electric or magnetic variables G05F; control devices or systems insofar as characterised by mechanical features only G05G)
- G16B: BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY

## Abstract

The subject of the invention is a method of network traffic management in information and communication systems (in particular, in active network devices) where it is desired/operated/insufficient to use the QoS (Quality of Service) characteristics in order to improve the quality of information and communication services. The method lies in the fact that, by using a multilayer neural network, knowledge base and feedback, the system is able to autonomously adjust QoS parameters and settings in the device to the existing conditions and circumstances.

## Claims

### Claim 1

A method of network traffic management in information and communication systems, in the mediation network devices for data transmission in information and communication networks, in manageable active network devices which enable the user or administrator to modify settings and parameters relating to QoS via the intranet configuration site of the device or configuration file, and which have mechanisms and settings eliminating or limiting the number of occurrences or the scale of problems specific to information and communication networks, such as: network congestion, latency, jitter, and packet loss, by determining the limits of certain parameters, in particular, the bandwidth, number of connections, session time, as well as by the use of prioritizing, in particular, particular services or connections from/to a specific address, in particular, through a customizable set of policies for configuration of rules and policies capable of reconfiguring the device settings depending on the existing/foreseen in the policy event, wherein, in addition to a configurable set of policies, it has an implemented multilayer artificial neural network which analyses in real time, in discrete points in time, the current parameters of connection and active data transmission sent though the device; based on simple input data comprising, among other things: above data (observations) and the collected data relating to the current activity of users and devices in the network, and archival data concerning their activity, gathered in the internal knowledge base (knowledge), the aforementioned neutral network is able to autonomously and unsupervisedly define and set new values of parameters and settings for QoS (QoS settings) on a managed network device, changing previous parameters and settings without reset, thus allowing trouble-free operation despite the changes of parameters and settings, wherein the user is also able to affect (configuration) the strategy of QoS parameters and settings selection via the intranet site or file with configuration of autonomous mode settings (mode settings); these settings are realised in a clear and comprehensive way for users, using linguistic variables with values in any min-max range, such as: “reliability”, “security” or “efficiency” which affect each other and also the result of solution activity, thus they characterise desired strategy and are taken into account when defining new QoS parameters and settings (QoS settings) with the next iteration of their determination in artificial neural network which takes these variables (mode setting) on the inputs of the first layer, and information on the current parameters, bandwidth and use of available connections (observations), and the current QoS parameters and settings (QoS settings), as well as information stored in the internal knowledge base, relating to the archival network and user behaviours; it calculates values of the activation function in individual neurons, giving the specified result for the upper layer of neurons, wherein the type and coefficients of activation function are derived from the best configuration from among 100 most recent iterations, subjected to random modifications of values of individual QoS parameters, each within the range of +/−5%, and the parameters that have no numerical value, such as enabling/disabling prioritization of a certain service, are modified not more than every minute or at a sudden change in the link state (observations), and the outputs of the neural network consist of: archive of user activity and network parameters (knowledge) as well as QoS parameters and settings of the network device (QoS settings).