Enabling the data driven enterprise

Enabling the Post-Hypervisor
Cognitive Computing Era
Rao Mikkilineni Ph D
January 24, 2019
Deliver Intelligent, Sentient, and Resilient
Data Driven Enterprise
1

Introduction
Rao Mikkilineni
• PhD – University Of California San Diego
• Research at University of Paris, Courant Institute of
Mathematical Sciences; Columbia University
• Worked at Bell Labs, Bellcore, US WEST, Five Startups
(Network Programs, SS8 Networks, LightSand
Communications, Comstock Systems, C3DNA) and Hitachi
Data Systems
• MBA from Japan American Institute of Management
Science (Hawaii) and Sophia University (Tokyo)
• Presented a new computing model going beyond Turing
Machines at the Turing Centenary Conference
2Copyright 2018 Dr. Rao Mikkilineni, Ph D.

Business Drivers for Virtualization
3
C3DNA Proprietary and Confidential
Unexpected Demands created by Consumer
and Internet Applications creating the demand
for communication and collaboration almost
at the speed of light
Web 2.0 Applications
Commerce at the Speed of light
Ever escalating cost of improving ROI and
lowering TCO in the Datacenter
SAN, NAS, Virtualization
HA/DR, Performance Optimization,
SecurityRising Datacenter
complexity
Rising TCO / Lower ROI
3
2
1
Copyright 2018 Dr. Rao Mikkilineni, Ph D.

4
Inflection Point in IT and Transition to an Era of Security,
Privacy and Individual Rights
Computer
Science
BusinessInformation
Technologies
Changing Computing Models
To Address Big Data & Fluctuations in Resource Demand & Availability
Cognitive Computing; Neural Networks
Elastic Computing Resources
Zero-touch Service Orchestration
Managed Bandwidth Slicing; 5G; L3
Data access at in-memory Speed
Communication, Collaboration and
Commerce at the Speed of Light
Business DriversTechnology Drivers

5
Agenda
• Emergence of the “data-driven” enterprise
• Emergence of cognitive computing models
• The convergence of business process automation and Deep Learning insight
• New “Cognitive Infrastructure” supporting the intelligent, sentient, and resilient
data-driven enterprise
 Cognitive and Infrastructure Agnostic Control Overlay
 Composable Services
 Cognitive Deep Learning Integration
• The theory behind the Cognitive Enterprise Era
• Food for thought “The Vision for the Future Data-Driven Enterprise”
• Summary

6
Drivers for New Information Processing Solutions
Data traffic is clogging up telco networks like fat in a bad artery, and few are optimistic about revenue growth.
"The traditional way of building networks is becoming too expensive,"
Axel Clauberg, Deutsche Telekom AG (NYSE: DT)'s vice president of IP and optical networks (among other
things) during a recent interview with Light Reading.
Mobile Computing and Video Services are driving the demand for high performance and low latency
computing at the edge
“The growth of the Internet of Things and the upcoming trend toward more immersive and interactive user
interfaces will flip the center of gravity of data production and computing away from central data centers and
out to the edge.”
Thomas Bittman, VP Distinguished Analyst, Gartner
Intelligence and insights based on AI/ML/DL are being pushed to the edge where the data source resides..
“A self-driving vehicle can’t afford to send gobs of raw sensor data upstream to the cloud and then wait for an
answer on target identification to return before deciding whether to brake or swerve. It needs to decide
immediately whether or not there’s a human in the crosswalk, but it can wait awhile before rendering an AI
judgment on whether the pedestrian’s attire was fashionable.”
Kevin Morris, Electronic Engineering Journal, May 16, 2018

Opportunity at Hand

What are the CIOs Saying About Information Technology?
9
Business Process Improvement
Efficiency & Cost Reduction
Cyber Security

What are the CIOs Saying?
10
Business Process Improvement
Efficiency & Cost Reduction
Cyber Security

Why are They Saying It?
• Big Data is becoming Bigger Faster Distributed Data
• Data grows 10x every 5 years; 80% is video
• New data is overwhelmingly unstructured & uncertain
• Avalanche of Real-Time Data from sensors, machines, and devices
• Bigger Faster Distributed Data Networks create new Business Value
• 360o perspective through intelligent information aggregation
• Operational intelligence with context aware prediction capability
• Real-time insights in transaction processing
• Realization of new business value demands dynamic and resilent Business
Process Management with automation
• Secure & Sentient (24X7)
• Agile & Anti Fragile
• Infrastructure agnostic, shared footprint
• Real-time management at globally distributed scale

Bottom Line
12
Deloitte Consulting
LLP’s Technology
Consulting
Machine Intelligence
Dark Analytics
Trust Economy
IT unbounded
Inevitable Architecture
Everything as a Service
Reimagining Core Systems

Bottom Line
13
Multi-ServiceArchitecture
Autonomic Service
Orchestration
Cognitive
Composable
Services
Cognitive Deep
Learning
Managing Functions,
Structure and
Fluctuations
Machine Intelligence
Dark Analytics
IT unbounded
Inevitable Architecture
Everything as a Service
Trust Economy
Reimagining Core Systems

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Symbolic computing with cognitive control overlay and
neural networks creating intelligent, sentient and resilient
Systems
INTELLIGENCE
Functional
Requirements
Execution
Non-Functional
Requirements
Execution
COGNITION
Distributed
Neural
Networks
Distributed
Big Data

Lesson from the Past
15
https://youtu.be/q6kVm2OHeK0
Click here for the video

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Composable Services with Cognitive Control Overlay
• Composable information
processing structures
• Decoupling of service management
from infrastructure management
• Ability to influence the computation
based on its “blueprint” of
description or specification
• Ability to infuse cognition (self-*
properties) into computing
• Overlay with hierarchical cognitive
control
https://www.youtube.com/watch?v=tu7EpD_bbyk&
feature=youtu.be (video showing cognitive multi-
cloud application orchestration)

Edge to Core – A New Approach with Cognitive Control
Overlay
17Platina Systems Proprietary & Confidential
Devices Access
Mobile
Fixed
Intelligent
Integration
Edge
Cloud
Video/Enterprise
Services
Edge
Cloud
ML/DL
Algorithms
Multi-Cloud Hosting
Business Process & Data
Analytics Execution
WAN SDN
Virtual Private &
Public Datacenters
Solution Providers
ML/DL
Algorithms
Business Logic
Execution

The Cognitive Enterprise
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Legacy Apps Virtual Private Datacenter Network Multi-Cloud Network
L7 L7L7
Physical Layer
Data Link Layer (MAC) VLAN
L1
L2
Network Layer (IP, Inter-VLAN)
Transport Layer
L3
L4
Session LayerL5
Presentation Layer
Application Layer (FD, url)
L6
L7
L3
Cognitive Control Layer
Alerting, Addressing, Supervision, Mediation
Service DNS
App Firewall
Invisible IP Network
•Hardware-based packet
forwarding
•High-performance packet
switching
•High-speed scalability
•Low latency
•Lower per-port cost
•Flow accounting
•Security
•Application Quality of service
(QoS) – Network QoS
Workload QoS
Eliminate Packet Sniffing
Data Path
Platina
Policy Based L7 SwitchL4, L5, L6
L3
L2
L1
L4, L5, L6
L3
L2
L1
L8 Cognitive Control Overlay

New Cloud Network Deployment at the Edge
Applications
App1
(CDN Private)
App1
(CDN Class 1)
App2
(Gaming)
App3
(Analytics)
App4
(Gambling)
App4
(Gambling)
App1
(CDN Class 2)
App3
(Analytics)
App4
(Gambling)
App2
(Gaming)
App2
(Gaming)
App3
(Analytics)
Configuration and Management
of Infrastructure & Micro-Services
Service
GW;FW
Zero-Touch
Provisioning
Secure Boot /
Image Mgmt
Load
Balancer
L3 Routing
Service
DNS
Edge Site 1 Edge Site 2
COTS Servers / Storage
BMC
RDMA &
NVMEoF
Flash &
NVM
Cloud Cluster
IaaS and PaaS Provisioning and Management
Replicated Cluster
Manager
Service
Routing
MonitoringScheduling
Edge cloud with full
automation of Aplication
and infrastructure at run
time.
Micro-services-centric
application QoS
assurance
Decoupled Application
Dev & Ops.
Main-frame computing
power with high
availability, performance
and security in a
distributed computing
cloud.

Deep Learning Networks

Emergence of Cognitive Computing
ModelsEnabling Multi-Service Platform
The Theory Behind

In The Beginning…
There are two kinds of
creation myths: those where
life arises out of the mud, and
those where life falls from the
sky.
In this creation myth,
computers arose from the
mud and code fell from the
sky.
- George Dyson
“Turing's Cathedral: The Origins of the Digital
Universe", New York: Random House, 2012.
22
“
“
The Digital Universe
created by the Turing/von
Neumann legacy is
expanding at a rate of
 Two trillion transistors
per second and
 Five trillion bits of
storage per second

The First Wave
Church-Turing Thesis and the Origin of
IT
Computing functions that are easily described by a list
of formal, mathematical rules or a sequence of event
driven actions such as modeling, simulation, business
workflows, interaction with devices, etc.
All algorithms that are Turing computable fall within the
of boundaries Church Turing thesis which states that “a
function on the natural numbers is computable by a
human being following an algorithm, ignoring resource
limitations, if and only if it is computable by a Turing
machine.”
23
Replacing a man in the process of computing a
real number (using a paper and pencil) by a
machine which is only capable of finite number
of conditions.
AlanTuring (1937)

The Second Wave: Distributed Interactive
Computing
Information Processing Structures
Distributed and communicating computing functions
to create a higher order (Universal Turing machine)
implementation executing a sequence of computing
functions (defined by functional requirements).
Information processing is still based on the
operation on numbers and is bounded by the
Church-Turing thesis.
Computation as operations on multimedia, such as
text, audio or video data, and Interactive
computation, or computation as interaction.
24
With the growth of the Internet and the WorldWide
Web, computing has become an inherently social
activity, rather than an isolated process, with new ways
of conceiving, designing, developing and managing
computational systems.
Prof. Mark Burgin (2018)

…Computation and Its Limits
“The key property of general-purpose
computer is that they are general
purpose. We can use them to
deterministically model any physical
system, of which they are not
themselves a part, to an arbitrary degree
of accuracy.
Their logical limits arise when we try
to get them to model a part of the
world that includes themselves.”
Cockshott P., MacKenzie L. M., and
Michaelson, G, (2012) Computation and
its Limits, Oxford University Press, Oxford.
25
A non-functional requirement is a requirement that
specifies criteria that can be used to judge the
operation of a system, rather than specific behaviors.
This should be contrasted with functional requirements
that define specific behavior or functions.
The plan for implementing functional requirements is
detailed in the system design. (The Computed)
The plan for implementing non-functional
requirements is detailed in the system architecture.
These requirements include availability, reliability,
performance, security, scalability and efficiency at run-
time. (The Computer)
Manageability is in the architecture …

Cognition, Self-Management and Autonomic Computing
• Cognition is the ability to process
information, apply knowledge, and
change the circumstance.
• Cognition is associated with intent and its
accomplishment through various
processes that monitor and control a
system and its environment.
• Cognition is associated with a sense of
“self” (the observer) and the systems with
which it interacts (the environment or the
“observed”).
• Cognition extensively uses time and
history in executing and regulating tasks
that constitute a cognitive process.
26
Managed Object Touch-Point

A Cognitive Computing Model
DIME (distributed intelligent managed
element) is a form of computing that
introduces manageability using Oracle*
design, Oracle networks and process
control by Oracles
It configures the computed with
appropriate resources, monitors its vital
signs and acts to optimize resources
based on a blueprints of descriptions of
the computers and the computed
It manages the Life-cycle quality of
computation, including mobility, self-
repair, replication, and security
27
* Following Turing’s comments in his
thesis borrowing the concept of the
Oracle of Delphi
Mikkilineni, R. (2011) Designing a New Class of Distributed Systems, Springer, New York.
Mikkilineni, R., Comparini, A. and Morana, G. (2012) ‘The Turing o-machine and the DIME Network Architecture:
Injecting the Architectural Resiliency into Distributed Computing’, Turing-100, The Alan Turing Centenary, EasyChair
Proceedings in Computing. Available online at: www.easychair.org/ publications/?page=877986046 (accessed on 10
October 2016).
https://magazine.cioreview.com/magazines/
September2017/Application_Management/

The Third Wave: Evolution of Cognitive
Computing Models
1. Information Processing in the face of Fluctuations in resource demand and availability
28
Nature computes by information
processing going on in networks of
agents, hierarchically organized in
layers. Informational structures self-
organize through processes of
natural/ physical/embodied
computation. (Dodig-Crnkovic and
Giovagnoli, 2013)

The Third Wave: Evolution of Cognitive
Computing Models
2. Neural Network Computing Models
Neural network model allows computers to
understand the world in terms of a hierarchy of
concepts to perform tasks that are easy to do
"intuitively", but are hard to describe formally or a
sequence of event driven actions such as
recognizing spoken words or faces.
29
CONNECTIONISM - can model temporal sequences, the standard connectionist models are not sufficiently powerful
because they do not include reliable structure in the environment. In addition, “connectionist modelers tend to think
in terms of single tasks and the most common forms of network are not good at handling multiple tasks which
interact.”
Wells,A. (2006). RethinkingCognitive Computation:Turing and the Science of Mind. Palgrave Macmillan:
London.

What Does Biology Tell Us?

Where do we Go from Here?

Enabling the Data-Driven Cognitive Era
The Vision

Big Data and the Need for a Sentient Enterprise
33
Service Speed, Availability,
Performance, Security, Cost
and Compliance
CPU, Memory, Bandwidth,
Latency, Storage Throughput,
IOPs and Capacity
Smart
Homes
Connected
Equipment
Connected
Factories
Mobile
Devices Smart Cities
Real-time data processing
At-source/On premises
Data visualization
Basic Analytics
Data caching, buffering
Data filtering, optimization
Device communication, and
management
Sensors and
Controllers
Big Data Processing
Data warehousing
Business Process Logic
Connected
Cars/trains

Current State of the Art
34
Executable
Business
Process
Algorithms
Big Data Islands
Insights
IoT
BlockChain
Non-Functional
Requirements
Functional
Requirements
Multi-Cloud
Network
Software Defined
Networks
Neural
Networks
Multiple Platforms Myriad Algorithms
Labor Intensive

Cognitive Business Process Dynamics
35
Multi-Service
Workloads
Workloads
(Application
Networks)
Application
• Business Process
Manager
• Business Process
• Application Network
Manager
• Service Delivery
• Application
Component Manager
• Application
Component
Cognitive Control Overlay
Cognitive Connection
Availability
Security
Billing
Performance
Compliance
Configure,
Monitor and
Control the
Evolution

The New Data-Driven Enterprise Technologies
Deep Learning Neural Networks
• Cognitive insights from natural language processing, voice, text and
video analytics
Cognitive Software Defined Infrastructure
• L3 Networking,
• In-memory computing networks (SSD/DRAM)
• Fiber connected Compute Clusters
• Autonomous Infrastructure as a Service
Autonomous Application workflow composition and
orchestration
• Cognitive workflow control overlay
• Dynamic reconfiguration of workflows to address fluctuations

Next Decade – Evolution of IT
37
My predictions for the next
decade:
• Distributed intelligent,
resilient and sentient
applications;
• High-Performance and
Low-Latency Edge Cloud
Network; Distributed AI;
• Cryptocosm and digital
asset life-cycle
management; and
• Artificial Consciousness
and culture that bring
harmony between
Humans, Intelligent
Machines, Resilient
devices and sentient
applications.

Summary: Addressing Function, Structure &
Fluctuations in a Post-Hypervisor Era
• Data-driven enterprise requires intelligent, sentient, and resilient software systems to
address information processing structures to deal with rapid fluctuations in resource
demand and availability.
• Communication, Collaboration and Commerce workflows at the speed of light demand
always-on anti-fragile systems
• Both autonomic computing and neural networks provide a next generation set of
technologies to meet the needs of the data-driven enterprise at the speed of light
• Crypto-Security and New Digital Asset Life-cycle Managent assures the Asset’s
Confidentiality, Integreity, Availability providing Privacy & Protection of Individual Rights

Further Food for Thought
Burgin, Mikkilineni. Cloud computing based on agent technology, super-recursive algorithms and
DNA, Int. J. Grid and Utility Computing, Vol. 9, No. 2, 2018 193
Mikkilineni R, Morana G., (2016) Cognitive Distributed Computing: A New Approach to Distributed
Datacenters with Self-Managing Services on Commodity Hardware, International Journal of Grid and
Utility Computing (IJGUC), Vol. 7, No. 2,
Mikkilineni, R., Comparini, A. and Morana, G. (2012a) ‘The Turing o-machine and the DIME Network
Architecture: Injecting the Architectural Resiliency into Distributed Computing’, Turing-100, The Alan
Turing Centenary, EasyChair Proceedings in Computing.
Mikkilineni, R. (2012b) ‘Going beyond computation and its limits: injecting cognition into computing’,
Applied Mathematics, Vol. 3, No. 11A, pp.1826–1835.
Mikkilineni, R., Morana, G., Zito, D. and Di Sano, M. (2012c) ‘Service virtualization using a non-von
Neumann parallel, distributed, and scalable computing model’, Journal of Computer Networks and
Communications, Vol. 2012, Article ID 604018, 10 pages.

Enabling the data driven enterprise

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