March toward a cognitive enterprise

1. Enabling the Post-Hypervisor Cognitive
Computing Era
Rao Mikkilineni Ph D
October 13 2018
Deliver Intelligent, Sentient, and Resilient
Data Driven Enterprise
Dr. Rao Mikkilineni, Ph D 1October 13, 2018

2. 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)
2Dr. Rao Mikkilineni, Ph D October 13, 2018

3. Business Drivers for Virtualization
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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
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2
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Dr. Rao Mikkilineni, Ph D October 13, 2018

4. 4
Inflection Point in IT and Transition to a Post-Hypervisor Cognitive Era
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. 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”
• Discussion
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6. 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

7. What are the CIOs Saying About Information Technology?
Dr. Rao Mikkilineni, Ph D 8
Business Process Improvement
Efficiency & Cost Reduction
Cyber Security
October 13, 2018

8. What are the CIOs Saying?
Dr. Rao Mikkilineni, Ph D 9
Business Process Improvement
Efficiency & Cost Reduction
Cyber Security
October 13, 2018

9. 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
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10. Bottom Line
Dr. Rao Mikkilineni, Ph D 11
Deloitte Consulting LLP’s
Technology Consulting
Machine Intelligence
Dark Analytics
Trust Economy
IT unbounded
Inevitable Architecture
Everything as a Service
Reimagining Core Systems
October 13, 2018

11. Bottom Line
Dr. Rao Mikkilineni, Ph D 12
Multi-ServiceArchitecture
Autonomic Service
Orchestration
Cognitive
Composable
Services
Cognitive Deep
Learning
Managing Functions,
Structure and FluctuationsMachine Intelligence
Dark Analytics
IT unbounded
Inevitable Architecture
Everything as a Service
Trust Economy
Reimagining Core Systems
October 13, 2018

12. Dr. Rao Mikkilineni, Ph D 13
Unlocking the value of dark data will require a cognitive overlay
that is intelligent, sentient and resilient
INTELLIGENCE
Functional
Requirements
Execution
Non-Functional
Requirements
Execution
COGNITION
Distributed
Neural
Networks
Distributed
Big Data
October 13, 2018

13. Lesson from the Past
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https://youtu.be/q6kVm2OHeK0
Dr. Rao Mikkilineni, Ph D October 13, 2018

14. Deep Learning Networks
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15. Emergence of Cognitive Computing ModelsEnabling
Multi-Service Platform
The Theory Behind

16. 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.
Dr. Rao Mikkilineni, Ph D 17
“
“
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
October 13, 2018

17. 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.”
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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.
Alan Turing (1937)
Dr. Rao Mikkilineni, Ph D October 13, 2018

18. 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.
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With the growth of the Internet and the World Wide
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)
Dr. Rao Mikkilineni, Ph D October 13, 2018

19. …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.
Dr. Rao Mikkilineni, Ph D 20
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 …
October 13, 2018

20. 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.
Dr. Rao Mikkilineni, Ph D 21
Managed Object Touch-Point
October 13, 2018

21. 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
Dr. Rao Mikkilineni, Ph D 22
* 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/
October 13, 2018

22. The Third Wave: Evolution of Cognitive Computing
Models
1. Information Processing in the face of Fluctuations in resource demand and availability
Dr. Rao Mikkilineni, Ph D
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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)
October 13, 2018

23. 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.
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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). Rethinking Cognitive Computation: Turing and the Science of Mind. Palgrave Macmillan:
London.
Dr. Rao Mikkilineni, Ph D October 13, 2018

24. What Does Biology Tell Us?
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25. Where do we Go from Here?
Dr. Rao Mikkilineni, Ph D 26October 13, 2018

26. Enabling the Data-Driven Cognitive Era
The Vision

27. Current State of the Art
Dr. Rao Mikkilineni, Ph D
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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
October 13, 2018

28. Cognitive Business Process Dynamics
Dr. Rao Mikkilineni, Ph D 29
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
October 13, 2018

29. The New Data-Driven Enterprise Technologies
Deep Learning Neural Networks
• Cognitive insights from natural language processing, voice, text and video
analytics
Cognitive Software Defined Networks
• Network Function Virtualization (NFV)
• L3 Networking and In-memory computing networks
Autonomous Application workflow composition and
orchestration
• Cognitive workflow control overlay
• Dynamic reconfiguration of workflows to address fluctuations
Dr. Rao Mikkilineni, Ph D 30October 13, 2018

30. 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
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31. 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.
32Dr. Rao Mikkilineni, Ph D October 13, 2018