SYSTEM AND METHOD FOR ENABLING INTELLIGENT NETWORK SERVICES BY COGNITIVE SENSE-ANALYZE-DECIDE-RESPOND FRAMEWORK

摘要

#$%^&*AU2019226295A120200326.pdf#####James & Wells Ref: 312472AU ABSTRACT SYSTEM AND METHOD FOR ENABLING INTELLIGENT NETWORK SERVICES BY COGNITIVE SENSE-ANALYZE-DECIDE-RESPOND FRAMEWORK 5 In the field of Internet of Things understanding need of applications and translating them to network parameters and protocol parameters is a major challenge. This disclosure addresses problem of enabling network services by cognitive sense-analyzedecide-respond framework. A processor implemented method is provided for enabling 10 network aware applications and applications aware networks by a sense analyze decide respond (SADR) framework. The processor implemented method includes sensing, at least one application parameter and at least one network parameter to obtain a plurality of sensed information; analyzing, the plurality of sensed information is filtered and synchronized to generate a plurality of derived parameters; determining, a plurality of 15 rules based on the plurality of derived parameters; validating, the plurality of rules for a plurality of scenarios to obtain plurality of decisions; and enabling, at least one of (i) network, (ii) application and (iii) protocol control based on the plurality of decisions. 20 [To be published with FIG. IB] 26James & Wells Ref: 312472AU I--------- ------------------------OE I Rsodoue prmtr Deieoue i API Layer Protocolu e etr, parameters, I desired state, iterated1 Respond module parameters Decide module Applicatio -- I------------n LR-L4l (20t) (206) parameter s for 1 cross layer optI Current State Features, Network, synchronized parameters, Protocol QoE paraMeters Application ---- C--gu----cogeti and A a sApplication S I | state - Logs, K Is Sense module Real-time 'I Machine Learning App I paramrn r202) parameters, Analyze module I Classification s(204) 1 Feature Extraction Network Applicati & Model Tunin onProtocol RSSI, Configu action, congestion, route, link Applicatio I state, prt & flow parameters n - - - -- --- i -- -- -- -- -- -- -- -- -- -- -- -- Sensing i--------------SDN NFV Legacy •Active, Passive/Indirect Sensing Configuration, Slicing, •Direct, Derived, Processed data Virtualization, Power, Route, collectiobadt conrol DeiceLocal vs. global data control... Device & Agnostic data collection methods Network Layer Application specific data collection Emulator data for training FIG. 1B 2/11