DISTRIBUTED ENSEMBLE LEARNING FOR PROVISIONING INDICES, ACTIONS, AND INPUTS FOR ASSURANCE AND PERFORMANCE
Publication Date: 2018-Feb-06
Publishing Venue
The IP.com Prior Art Database
Related People
J.D. Stanley: AUTHOR [+5]
Abstract
Self-adapting machine learning software provides actionable insights to proactively upgrade other elements of the system and environments that are ancillary or adjacent to the main system. These suggested actions occur based on software threshold calculation matrices distributed throughout network nodes. Outputs and reports are distributed to humans, machines, or other math-based cognitive decision support systems to operate based on the actions or suggestions. The math-based learning approach to combining multiple machine learning engines and models produces higher accuracy levels and statistical outcomes than traditional one-calculation approaches.
Copyright 2018 Cisco Systems, Inc. 1
DISTRIBUTED ENSEMBLE LEARNING FOR PROVISIONING INDICES, ACTIONS, AND INPUTS FOR ASSURANCE AND PERFORMANCE
AUTHORS: J.D. Stanley
Carlos M. Pignataro Plamen Nedeltchev Victoria Blaylock
Andrea Gold Prabhat Bhattarai
CISCO SYSTEMS, INC.
ABSTRACT
Self-adapting machine learning software provides actionable insights to proactively
upgrade other elements of the system and environments that are ancillary or adjacent to the
main system. These suggested actions occur based on software threshold calculation
matrices distributed throughout network nodes. Outputs and reports are distributed to
humans, machines, or other math-based cognitive decision support systems to operate
based on the actions or suggestions. The math-based learning approach to combining
multiple machine learning engines and models produces higher accuracy levels and
statistical outcomes than traditional one-calculation approaches.
DETAILED DESCRIPTION
The emerging nature of distributed machine learning on edge devices, edge nodes,
and across distributed control points in infrastructures faces many challenges in ensuring
automation and orchestration. As those solutions evolve in the market, large scale
provisioning systems (e.g., Software Defined Networking (SDN) networks, Network
Functions Virtualization Infrastructure (NFVI), or even Operations Support Systems (OSS)
/ Business Support Systems (BSS) variants) will focus heavily on the service chains of the
infrastructures and workflows of the resources or services. The gap in this model is a
purpose-built flow technique on distributed Machine Learning (ML) engines.
Abstracting out calculations enables users to implement them as inputs into other
automation and infrastructure management solutions and/or as indices generated on the
ebbs and flows of the system environments. Users seek more insights and more data to help
Copyright 2018 Cisco Systems, Inc. 2
them make informed decisions across many variables to optimize resource deployments,
mitigate risks, or optimize infrastructure elements.
Most of the existing ML approaches in the market are siloed and standalone, and
are applied to a specific use case with a desired result. For example, cybersecurity software
calculations seek out security and threat risks. The same interrogated dataset may be
leveraged and examined from a different lens, such as inventory attributes (e.g., mixing the
cyber data with inventory data). This generates new datasets and insights for other purposes
and uses (e.g., insurance rates based on multi-factor risk assessments).
In distributed ML engine environments, using the system calculations and derived
data has value outside of the system. Where one user may see a set of readings as noise,
the other user may see value or may combine that data into their own adjacent system
calculations.
Thus, the problem relates to siloed calculations between systems, and especially to
systems that touch and flow over the same infrastructu...
