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IPV6/NSH METADATA BASED FOG ANALYTICS TO DISTRIBUTE THE CLASSIFIER/ANALYTIC LOAD TO DIFFERENT 6LOWPAN NODES

IP.com Disclosure Number: IPCOM000244748D
Publication Date: 2016-Jan-08
Document File: 6 page(s) / 135K

Publishing Venue

The IP.com Prior Art Database

Related People

Carlos M. Pignataro: AUTHOR [+2]

Abstract

A method is presented herein in which a 6LowPAN Router (6LR) is instructed to act as a classifier to add additional Metadata in an IPv6 packet. An IPv6 Router Advertisement from a 6LowPAN Border Routers (6LBR) is used to dynamically instruct (on an as needed basis) the 6LR to act as a classifier. The 6LBR uses the Metadata information for various data analytics purposes and removes the Metadata before forwarding out. The IPv6 Extension Header may be converted, based on Metadata information, to NSH Metadata (Type 2) and forwarded over a Service Function Path. This solution helps distribute the load associated with classification, DPI, analytics, etc., to different nodes, thereby achieving seamless distribution of load and resource utilization among different low power nodes.

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IPV6/NSH METADATA BASED FOG ANALYTICS TO DISTRIBUTE THE CLASSIFIER/ANALYTIC LOAD TO DIFFERENT 6LOWPAN NODES

AUTHORS:

 Carlos M. Pignataro Nagendra Kumar Nainar

CISCO SYSTEMS, INC.

ABSTRACT

    A method is presented herein in which a 6LowPAN Router (6LR) is instructed to act as a classifier to add additional Metadata in an IPv6 packet. An IPv6 Router Advertisement from a 6LowPAN Border Routers (6LBR) is used to dynamically instruct (on an as needed basis) the 6LR to act as a classifier. The 6LBR uses the Metadata information for various data analytics purposes and removes the Metadata before forwarding out. The IPv6 Extension Header may be converted, based on Metadata information, to NSH Metadata (Type 2) and forwarded over a Service Function Path. This solution helps distribute the load associated with classification, DPI, analytics, etc., to different nodes, thereby achieving seamless distribution of load and resource utilization among different low power nodes.

DETAILED DESCRIPTION

    The very basic intention of Fog computing is to reduce the load from a centralized cloud by distributing the analytics close to the edge. This not only helps distribute load, but also makes the data analytics useful as delayed result of data analytics is nearly useless.

    While Fog computing has proven to be useful, it still has associated challenges depending on the computing/power capabilities of a Fog node. For example, a Fog node handling tens and thousands of sensors will need to perform Deep Packet Inspection (DPI) if payload based analytics is required. This introduces challenges in terms of:


Processing scale of the Fog node


Power consumption associated with payload DPI and analytics

Copyright 2016 Cisco Systems, Inc.

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    Another option is to use flow based analytics, but this is not suitable for many use cases due to the limited information available/collected from the packet header.

    Presented herein is a solution that may be considered as something in between flow-based and payload-based analytics. The analytics load is distributed along the path from {Host to Fog to Cloud} by distributing the classification/DPI role and analytics to different nodes along the path and using metadata to carry relevant information (more than the flow-based approach and less than the payload based-approach), allowing data analytics to be performed on the relevant information without significant delay.

Figure 1

    In the topology shown in Figure 1, In sensors are connected over 6LR (6LowPAN Router) to 6LBR (6LowPAN Border Routers) to a WAN Cloud. The basic building block of this solution involves an efficient way of distributing the functionality of classification, metadata augmentation and analytics to different nodes. The first hop classifier (6LR) includes relevant information as Metadata to an IPv6 Extension Header. An edge router, like a 6LBR or a Fog router, will perform analytics on the Metadata and take a necessary forwarding action.

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