Subject: [Int-area] FW: New Version Notification for draft-deshpande-intarea-ai-based-tcpip-00.txt

From: "Vineet Deshpande (vindeshp)" <[email-masked]>
Date: Fri, 24 Jun 2016 12:17:10 +0000

Hello,

Request review and comments. Please refer PDF for more clear diagrams.
Apologies for the format issues.

Thanks & Regards,

Vineet Deshpande
ENGINEER.NETWORK CONSULTING

[email-masked]
Phone: +91 80 4429 1223

Cisco.com <http://www.cisco.com>

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On 13/06/16, 6:49 AM, "[email-masked]"
<[email-masked]> wrote:

>
>A new version of I-D, draft-deshpande-intarea-ai-based-tcpip-00.txt
>has been successfully submitted by Vineet Deshpande and posted to the
>IETF repository.
>
>Name:		draft-deshpande-intarea-ai-based-tcpip
>Revision:	00
>Title:		Intelligent Automation of Cloud and Network through AI Based
>TCP-IP Model
>Document date:	2016-06-12
>Group:		Individual Submission
>Pages:		10
>URL:            
>https://www.ietf.org/internet-drafts/draft-deshpande-intarea-ai-based-tcpi
>p-00.txt
>Status:         
>https://datatracker.ietf.org/doc/draft-deshpande-intarea-ai-based-tcpip/
>Htmlized:       
>https://tools.ietf.org/html/draft-deshpande-intarea-ai-based-tcpip-00
>
>
>Abstract:
>	This draft describes an AI based TCP-IP model. This draft describes
>	how Networks are evolving from Wireless Networks and Programming.
>	This draft describes how Big Data bottlenecks are present in
>	current Networks.
>	This draft describes Network and Cloud Orchestrated Reflectors
>	for resolving the Big Data bottleneck at Inter-AS and CSC
>	(Carrier Supporting Carrier) level. This draft proposes a Context
>	Mapping Language for E2E context-awareness and M2M communication.
>	This draft proposes Path diversity in Wired Networks as it evolves
>	from Wireless Networks. Path diversity through Cloud Orchestrated
>	Reflectors can be implemented through the Wireless concepts of
>	Rx Diversity, Tx Diversity and MIMO (Multi-Input, Multi-Output)
>	This draft is more of a fundamental concept that identifies how the
>	Network is evolving. This draft explains about the implicit
>	life cycle, which is also somewhat mathematical as it involves
>	one-to-many and many-to-one function mapping between Cloud and
>	Network reflectors. Then it relates the many cloud reflectors
>	together throug	Wireless network technologies like Tx, Rx
>	diversity and MIMO. This draft explains how this can be
>	implemented in Wired networks through Cloud software, path
>	diversity, spatial multiplexing	and Diversity precoding.
>
>Introduction
>
>	This draft:
>	1.Identifies the Big data, Digital Data bottleneck in present
>	Network.
>	2.Identifies that Networks are evolving from Wireless Networks
>	as well as Programming.
>	3. Provides a resolution to the Data bottleneck problem by evolving
>	the present Wired Networks and Cloud architectures to incorporate
>	features from Wireless networks and Programming.
>
>	This draft begins at a very basic level where it identifies the
>	problem	in the M2M communication. John Backus (Inventor of FORTRAN
>	or Formula Translation) describes the Von Neumann bottleneck as
>	an Intellectual	bottleneck.
>	This Intellectual bottleneck also affects the TCP-IP model or the
>	Networking layers. There is a static nature to the TCP-IP model. If
>	an AI approach is chosen to understand the TCP-IP layers the
>	problem in M2M communication can be further classified.
>	The problem is that there is a duality between the state machine
>	and the program control. However, the state machine is like a point
>	value or a basic computational element. This point value links the
>	state machine to Wireless Networks through Mass-Energy equivalence.
>	 Thus the state machine comes first and then the Program control.
>	From the AI based TCP-IP model and analysis of digital data it can
>	be seen that this duality is centred between the Internet and the
>	Transport layers.
>	The Digital Data or Big Data is the central point around which we
>	are	building a network architecture, therefore a design model
>	somewhat different from SDN or TCP-IP can be arrived at.
>	This is described through the diagrams in this draft:
>	1.	Implicit Lifecycle with Big Data in Centralised Control plane
>	and Distributed forwarding
>	2.	Implicit Lifecycle between Network, Cloud and the Internet
>	(aka Network of Networks).
>
>	Impact on EPN (Evolved Programmable Networks):
>	Having arrived at these models we can understand how the Network is
>	not just evolving from Programming but is evolving from the M2M
>	bottleneck or M2M duality. This duality has Digital data or Big
>	data as the central point. This digital data can be considered as
>	 an infinitesimal mass value or a finite state element.
>	Considering the implicit lifecycle and the mass-energy equivalence
>	 we can arrive at the conclusion that Networks are also evolving
>	firstly from Wireless Networks and then from Programming.
>	As the Network is evolving from both Wireless Networking as well
>	as Software Programming we can thus find out more about the
>	inter-working of the Cloud (more of a software based and Wireless
>	 concept) and the Network (Hardware based and both a Wired,
>	Wireless concept). The digital or Big data is the central
>	connecting point so it cannot be declassified from both the Cloud
>	 and the Network. Based on this analysis it can be seen that the
>	Big Data bottleneck occurs at the Inter-AS and CSC(Carrier
>	supporting Carrier) level. A Network grows as we add more physical
>	 hosts. A Network will keep growing and scaling.
>	Thus we can predict from Wireless Network architecture and
>	Programming how the present Network needs to evolve (grow and
>	scale) to merge with the Cloud Architecture.
>	Through new features described in my draft such as Cloud
>	Orchestrated Reflectors, Network Orchestrated Reflectors,
>	Context Mapping, Path diversity (as evolved from Wireless concepts
>	such as Tx Diversity, Rx diversity and MIMO) the Big data or
>	Digital data bottleneck problem in present Networks can be
>	resolved.
>
>Description:
>	The goal is to build a Seamless cloud infrastructure which allows
>	any CE to be seamlessly connected to the Internet (or SP). The
>	 Infrastructure would combine high speed core (data center)
>	 architecture with the Service provider and also to the end user.
>	 The Seamless cloud does not have the drawbacks of the hybrid cloud
>	 division that breaks down the Cloud into a public and a private
>	 cloud infrastructure. The Seamless Cloud infrastructure can
>	 provide an alternate way of building the CSC (Carrier Supporting
>	 Carrier) framework which is the present SP backbone design.
>
>	A Distributed Intelligence system is more suited for the
>	hierarchical layered system that defines the Network. A centralized
>	control plane for the entire network may have vulnerabilities and
>	also greatly increases the complexity. A distributed system is more
>	practical considering the Routing/Switching infrastructure and
>	Network layers that we have. From an AI perspective this would be a
>	bottoms-up approach rather than a top-down approach.
>	Please see below diagram of the TCP/IP model from this AI
>	perspective.It is important to understand that the AI in a Network
>	or a Cloud is centered around the Internet and Transport Layer
>	while still needing a Top-down approach from the Application layer
>	and a bottoms-up approach from the Link layer. The Cisco proprietary
>	parameter appropriately named as "weight" in the BGP protocol is
>	suggestive of the fact that the Intelligence in a Network needs to
>	be centered around the Network and Transport Layers. Also the term
>	"Weighted" appears in QoS as "Weighted Fair Queueing".
>
>	TCP/IP Layers from an AI/IoT perspective (AI Based TCP-IP Model)
>
>	________________________________________________________________
>	Application	| Process to Process 			| Application		|
>				| Applied on IoT 				|					|
>				|(Cognitive,Symbolic)			|					|
>				| Traditional top-down AI 		|					|
>	____________|_______________________________|___________________|
>	Transport   | Host to Host      			| Transport   		|
>				| Applied on IoT as well 		|					|
>				| as underlying hardware		|					|
>				| Could be both top-down AI as	|					|
>				| well as bottoms-up AI       	|					|
>	____________|_______________________________|___________________|
>	Internet	| Internet						| Internet  		|
>				| Underlying hardware			|					|
>				| Machine Learning(Neural 		|					|
>				|	networks)					|					|
>				| Bottoms up AI 				|					|
>	____________|_______________________________|___________________|
>	Link		| Link							| Link       		|
>				| Underlying hardware			|					|
>				| Machine Learning(Learnt from	|					|
>				|	IoT or Sensors)				|					|
>				| Bottoms up AI 				|					|
>	____________|_______________________________|___________________|
>
>	AI based FSM with IntelligentFlow in contrast to OpenFlow needs to
>	 be developed to build an AI based Network Infrastructure.
>
>	This FSM can form a sort of Shadow Router that tracks and defines
>	the	Network from a bottoms-up approach.
>	The underlying hardware approach can assist in fast switching the
>	data as in Shortest path bridging.
>	Basically TCP is stateful, IP is stateless and BGP is stateful.
>	IntelligentFlow: Scaling the Data Flow
>	If an AI based FSM can somehow be sandboxed and moved between
>	devices (or hosts) then IntelligentFlow may be possible with the
>	bottoms up and top down AI approach.
>	From a programming point of view, a combination of high level and
>	low level programming is needed by which we can Sandbox the AI
>	based FSM between hosts. This calls for introducing an
>	IntelligentStack as opposed to an Openstack within the data flow
>	of the AI Based TCP/IP model. This IntelligentStack could work in
>	tangent with the Elastic Compute Cloud of Openstack by providing
>	more traction over the Internet. The AI based FSM Sandbox can
>	optimize the traffic flows by acting as Intermediary in the Data
>	flow model. The Sandbox can scale up or reverse-scale the flows.
>	This could solve the BGP slow convergence and divergence problems.
>	AI is like the logic behind a thought process.
>	A Combinatorial logic is needed to combine the low level and high
>	level languages. This combination can develop into a very basic
>	type of M2M Intelligence which is essential for IoT.
>	From a high level programming perspective, the data type is a
>	program construct and the control flow is a type of program
>	execution (in a Machine).
>	From a low level perspective, the AI based FSM can do the reverse
>	which is to program the data flow by sending the control plane
>	information(to a Machine).
>	In other words, there is a duality in the M2M architecture through
>	the State Machine and the Program Control. This maybe the only way
>	to tackle the Von Neumann Bottleneck:
>	The Von Neumann bottleneck was described by John Backus in his 1977
>	ACM  Turing Award lecture. According to Backus:
>	Surely there must be a less primitive way of making big changes in
>	the store than by pushing vast numbers of words back and forth
>	through the von Neumann bottleneck. Not only is this tube a literal
>	bottleneck for the data traffic of a problem, but, more
>	importantly, it is an intellectual bottleneck that has kept us tied
>	to word-at-a-time thinking instead of encouraging us to think in
>	terms of the larger conceptual units of the task at hand. Thus
>	programming is basically planning and detailing the enormous
>	traffic of words through the Von Neumann bottleneck, and much of
>	that traffic concerns not significant data itself, but where to
>	find it.[22][23]
>	This is contrast to the duality in the Cloud that is implied in the
>	Intercloud Architecture. Hence according to me the Seamless Cloud
>	can incorporate the M2M architecture via the Network that cannot be
>	declassified from the Cloud.
>	The Combinatorial logic behind the above categorization of a
>	Machine (M2M) is that the binary or digital data is not completely
>	defined and can be divided into low level machine learning based
>	data (letters or numbers presented to Machine for reading)
>	and high level Top down AI based data (checks inputs of letters
>	or numbers against a description). The binary data gets more well
>	defined (turing complete?) when put into a finite state machine.
>	When binary data is put into a state machine it adds value and
>	thus weight into the machine. This is where BGP, TCP, IP protocols
>	come into the picture through the weight parameter and the CBWFQ in
>	QoS. This is where we need to find the data based on the Von
>	Neumann bottleneck. If this M2M Intelligence can be developed
>	Robots can identify Objects and take actions through Proximity
>	Sensors. Robots can focus on near Objects, identify them and take
>	actions. A great boost for IoT. From a bio-technology perspective
>	the network is akin to a nervous system controlled by a centralized
>	Neural network. However, both distributed and centralized
>	intelligence may be required considering the hierarchical
>	distributed layers in the legacy network data flow model where both
>	a bottoms-up and top-down AI model is required. Neural networks
>	have a concept called Convergence and Divergence.
>	In Networking the focus is mostly on convergence through routing
>	protocols. However, a divergent approach is also needed in order
>	to meet the AI/IoT based TCP/IP model requirement.
>
>	As described above I have defined an AI Based TCP-IP model.
>	Through that model I explained the significance of the "weight"
>	parameter. This allows us to define an Intelligent Automation
>	for Autonomous system through Cloud and Network orchestration.
>
>	There can be alternate ways of approaching Inter-AS and CSC.
>	The weight parameter is localized in an AS. With this being
>	introduced in Segment routing it allows dynamic scaling of
>	such parameters. Thus an AS can grow or shrink. Or a Private
>	AS can be dynamically generated. A public AS can scale, grow or
>	shrink.
>	In essence this means adding more value and functions into AS
>	numbers through algorithms which can dynamically alter the
>	Autonomous systems without impacting the network in any way.
>	Autonomous systems could be further classified into Temporary and
>	Permanent Autonomous Systems. This could serve as the mapping
>	between Cloud and network resources because the network cannot
>	be declassified from the cloud. This mapping can form an interface
>	between Network computing and Cloud computing.
>	This is rather like finding a number within a number. This
>	algorithm could involve randomly changing numbers. Making numbers
>	big or small. However, one-to-many is not the same as many-to-one.
>	Quantum computing with Qubits and Shor Algorithm could implement
>	such an algorithm.
>	The efficiency of Shor's algorithm is due to the efficiency of the
>	quantum Fourier transform, and modular exponentiation by repeated
>	squarings.
>	These concepts of an Automated AS can be of significance in the IoT
>	scenario where IoT devices can pe present anywhere and everywhere.
>	IoT technologies like Smart Grid, Smart Transportation,Smart Cities
>	require a more robust and intelligent M2M communication.
>	This is rather oddly somewhat like APIPA in Windows but now at the
>	Autonomous System level.
>	The M2M interface between Cloud and Network can be further defined
>	based on the concept of finding a number within a number. There is
>	an element of time in-variance in the network while there is an
>	element of time variance in the Cloud. This means that the network
>	convergence system needs to act as a Network Orchestrated Reflector
>	based on the ASN (Autonomous System number) while the Cloud needs
>	to act as Cloud Orchestrated Reflector based on the Cloud platform
>	ASN (Autonomous System number). This defines the interface between
>	the two which in turn defines the mapping between Cloud and
>	Network. Thus Fast Computing in the Cloud compensates for the slow
>	computation in the Network.
>	The Network orchestrated reflector and Cloud orchestrated reflector
>	defined above are similar to the Route reflector but include ASN
>	and other relevant features.
>	The loop or life cycle between the Network and Cloud reflectors can
>	be closed through the Segment routing and "weight" parameter There
>	may not really be a need for Quantum computing as of now to
>	implement these concepts.
>
>	There is an Implicit Life cycle and need for Seamless Cloud
>	Infrastructure as indicated by the diagrams below:
>
>	Diagram 1: Implicit Life cycle (time bound) as the Network
>	cannot be declassified from the Cloud (one way)
>
>	|-----> Internet -----> Network -----> Cloud Computing -----|
>	|															|
>	|															|
>	|-----------------------------------------------------------|												
>			|
>
>	Diagram 2: Implicit life cycle with Centralised Controller and
>	distributed forwarding
>
>	|-----> Centralised <-----> Big Data <-----> Distributed <--|
>	|	    Controller							  Forwarding	|
>	|															|
>	|-----------------------------------------------------------|
>
>	Diagram 3: Need for Seamless Cloud infrastructure
>
>	|---> Hybrid <---> Seamless Cloud <---> Virtual private <---|
>	|	    Cloud							 Cloud				|
>	|															|
>	|------> Public Cloud <-----------> Private Cloud <---------|
>
>	Big Data analysis for TCP-IP:
>	1. Big Data analysis in terms of Artificial Intelligence,
>	 Digital footprint, Programming and Quality. Not only in terms
>	 of Volume
>	2. Big Data is the central intelligence that sits between the
>	control plane and the forwarding plane.
>	3. Big Data can be considered as a centralized intelligence around
>	 which we are trying to put our concepts together. Big data is
>	 centered around IP, BGP and TCP.
>	4. Big Data in terms of volume (content) drags us down to the link
>	layer. Concepts such as MPLS, switching take us away from Big Data.
>	5. Big Data is both a centralized intelligence and a bottleneck.
>	A Distributed intelligence approach is also thus mandated.
>	6. Intelligent automation in the Cloud through Big Data (Hadoop,
>	 MapReduce) needs to interface with the Network at the Internet
>	 and Transport layers.
>	What is the proof for Scale and manageability of these concepts ?
>	Big Data is centered around TCP, IP and BGP layers. This is the
>	 forwarding path information in IGP via IBGP as well as EBGP.
>	The interconnect between EBGP and IBGP is through Autonomous s
>	ystems (ASN).
>	The slow convergence problem is in BGP and in IBGP.
>	This convergence issue is uncorrelated and exponential.
>	However, this affects EBGP scenarios and overall Internet
>	stability and scalability.
>
>	This scale problem can only be solved by options which
>	 take into consideration path diversity and matching for
>	 LPM (Longest Prefix Match) between Autonomous systems.
>	The number of unique Autonomous networks has grown to
>	 47000 in mid-2014.
>	Inter-AS options and CSC are big data bottlenecks.
>	 The present Inter-AS options consider solutions such as
>	 inter-VRF, ASBR with MP-BGP,etc. As these consider the
>	 routes and prefixes, they do not really solve the scale
>	 problem. Thus we need to look for alternate ways to approach
>	 this problem.
>	Manipulating AS numbers, Cloud and network orchestrated Reflectors,
>	 HRR is required.
>	Segment routing with parameters (eg: weight introduced into Segment
>	 routing) which can manipulate BGP AS path, communities are thus
>	 required.
>	I use the word Cloud orchestrated Reflectors and Network
>	orchestrated Reflectors as the problem needs to be abstracted from
>	the Internet Architecture in order to avoid manageability issues in
>	the present and in the future.
>	Based on the diagram involving the Cloud, Internet and Network
>	which is an implicit life cycle I believe that this is the only
>	option to scale the Internet in the future. This is taking into
>	consideration the limitations right now in BGP convergence,
>	Inter-AS and Cloud computing.
>	As this is a limitation issue even if the manageability is not
>	feasible as of now it would still have to be considered in the
>	future.
>	The implicit life cycle proves that the Internet needs to be
>	changed at the core as well and not just at the edges.
>	Cloud orchestrated reflectors are also important as the Internet
>	continues to scale with the Cloud and SDN. Big data as defined
>	in Hadoop through MapReduce suggests that the interface between
>	Cloud and Network is also important.
>	New BGP AF/SAF options do not really solve this scalability
>	problem:
>	IP6
>	IP-VPN
>	BGP Flow Specification
>	Pseudo Wires
>	L2VPN
>	2547 Multicast VPNs
>
>	The scalability problem needs to be solved taking into
>	consideration the fact that the network cannot be declassified
>	from the Cloud. Hence The IP Packet need to be classified through
>	a new AF/SAF which could likely involve BGP, ASN, segment routing
>	and path diversity, etc.
>	Alternate ways to approach inter-AS is needed as the traditional
>	 methods are traffic (big data) bottlenecks.
>	This draft proposes the following:
>	Cloud and Network Orchestrated Reflectors to route the
>	Multi-service, multi-tenant data between Cloud and Network.
>	Limitations of the  Cloud and Network can only be resolved through:
>	1. IP Packet
>	2. Segment routing (weight parameter, load balancing, distributed
>	 computing, QoS, etc)
>	3. New AS numbers (having Cloud and Network orchestrated reflectors
>	 in a reserved, private or a public AS)
>	4. Cloud and Network orchestrated Reflectors
>	5. New BGP AF/SAF having Segment routing
>	6. New Inter-AS options considering Segment Routing, distributed
>	 intelligence, Path diversity, LPM (longest prefix match)
>
>	Benefits:
>	1. Inter-AS options are based on inter-VRF, MPLS VPN and ASBRs
>	which re-distribute routes. Re-distributing repeats the routes.
>	 Segment routing with RR is a better option. MPLS takes the Big
>	 data away from Internet layer and towards Link layer.
>	2. Segment routing needs to focus on Big Data, routing for Path
>	 diversity, LPM (Longest prefix match), QoS and Multi-service
>	 Mutli-tenant offerings.
>	3. The benefits with Segment routing are that it can avoid
>	 redistribution and rely on path diversity, distributed
>	 intelligence, distributed computing, etc through the
>	 interworking of Cloud and Network.
>	4. Benefits for Multi-service Multi-tenancy is greater scale,
>	 redundancy, distributed computing, distributed intelligence,
>	 Improved exchange and transit points for Services. These
>	 benefits outweigh the present Inter-AS options which are
>	 a big data bottleneck. Context Maps in BGP, Segment Routing
>	 and SDN Controllers. The traditional route maps in BGP can
>	 match for routes, BGP PA, communities. However for distributed
>	 intelligence and SDN Controllers the Service providers need
>	 to be more context-aware.
>	 Segment routing can act as a Centralized Controller in the SDN
>	 framework. However BGP, Segment routing does not support or cannot
>	 differentiate between different contexts (virtual, cloud, network)
>	 or services such as Network services , Web services (WSDL), Cloud
>	 services(REST API). A context map can map and act as an interface
>	 between the Cloud services, Network services and Web services.
>	The concept of Path diversity is analogous to the concept of
>	Transmit diversity and Receiver diversity in Wireless networks.
>	The concept of Cloud reflectors is thus analogous to the concept
>	of reflection in Wireless networks. Therefore it should be
>	theoretically possible to implement features like MIMO
>	(Multi-input Multi-output) and Tx, Rx diversity in Wired networks
>	through COR and context mapping. MIMO concepts such as Spatial
>	Multiplexing and Diversity Precoding can be implemented through
>	Cloud Orchestrated Reflectors. This would also indirectly resolve
>	the problem of sub-optimal routing that occurs through Route
>	reflectors. Context maps or Context Mapping Language (CML) are
>	the next evolutionary step in Cloud-fog-Computing:
>	Route map-> Policy map -> Context map
>	RPL --> CML
>	This mapping between Cloud and Network is rather like an M2M
>	interface therefore the Context Maps or CML needs to be in both
>	BGP as well as in the SDN Controller or Cloud.
>	ODL learns the Customer network through the NOR and transfers the
>	topology to the COR transparently. COR is just a contextual
>	representation of the NOR. There is a one-to-many mapping between
>	NOR and COR. For one NOR or ODL we can have multiple COR.
>	This is the most efficient way of Cloud and Network inter-working.
>	Context maps can give us E2E context awareness for Smart cities,
>	Smart grid, etc.
>
>	Diagram 4: Cloud and Network Orchestrated Reflectors with
>	Centralised Controller
>
>					|--- COR
>				 ___|____________
>		NOR ----|_SDN_Controller_|
>					|
>					|--- COR
>	Security
>
>	From a Security perspective the AI based bottoms-up/Top-down
>	 approach can help develop an Internet Map or a Cloud Map from
>	 this Seamless Cloud which can be further developed to form an
>	 Intelligent Secure network which is a Security-aware, Zone-aware
>	 Seamless Cloud. The Seamless cloud can dynamically identify and
>	 mitigate security vulnerabilities. Personalized Secure clouds can
>	 become a reality. Security boundaries and perimeters for the Cloud
>	 can be more clearly identified.
>	 A Distributed Intelligence system is more suited for the hierarchical
>	 layered system that defines a Network. A centralized control plane
>	 for the entire network may have vulnerabilities and also greatly
>	 increases the complexity. A distributed system is more practical
>	 considering the Routing/Switching infrastructure and Network layers
>	 that we have. From an AI perspective my draft would be a bottoms-up
>	 approach rather than a top-down approach.
>	 The Network needs to grow (and scale) towards the Cloud and not
>	 just the other way round.
>
>
>
>                  
>        
>
>
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