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Enabling the data driven enterprise
- 1. Enabling the Post-Hypervisor CognitiveComputing Era Rao Mikkilineni Ph D January 24, 2019 Deliver Intelligent, Sentient, and Resilient Data Driven Enterprise 1
- 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) • Presented a new computing model going beyond Turing Machines at the Turing Centenary Conference 2Copyright 2018 Dr. Rao Mikkilineni, Ph D.
- 3. Business Drivers forVirtualization 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. 4 Inflection Point inIT 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 Copyright 2018 Dr. Rao Mikkilineni, Ph D.
- 5. 5 Agenda • Emergence ofthe “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 Copyright 2018 Dr. Rao Mikkilineni, Ph D.
- 6. 6 Drivers for NewInformation 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 Copyright 2018 Dr. Rao Mikkilineni, Ph D.
- 7. Opportunity at Hand 8Copyright2018 Dr. Rao Mikkilineni, Ph D.
- 8. What are theCIOs Saying About Information Technology? 9 Business Process Improvement Efficiency & Cost Reduction Cyber Security Copyright 2018 Dr. Rao Mikkilineni, Ph D.
- 9. What are theCIOs Saying? 10 Business Process Improvement Efficiency & Cost Reduction Cyber Security Copyright 2018 Dr. Rao Mikkilineni, Ph D.
- 10. Why are TheySaying 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 11Copyright 2018 Dr. Rao Mikkilineni, Ph D.
- 11. Bottom Line 12 Deloitte Consulting LLP’sTechnology Consulting Machine Intelligence Dark Analytics Trust Economy IT unbounded Inevitable Architecture Everything as a Service Reimagining Core Systems Copyright 2018 Dr. Rao Mikkilineni, Ph D.
- 12. Bottom Line 13 Multi-ServiceArchitecture Autonomic Service Orchestration Cognitive Composable Services CognitiveDeep Learning Managing Functions, Structure and Fluctuations Machine Intelligence Dark Analytics IT unbounded Inevitable Architecture Everything as a Service Trust Economy Reimagining Core Systems Copyright 2018 Dr. Rao Mikkilineni, Ph D.
- 13. 14 Symbolic computing withcognitive 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 Copyright 2018 Dr. Rao Mikkilineni, Ph D.
- 14. Lesson from thePast 15 https://youtu.be/q6kVm2OHeK0 Click here for the video Copyright 2018 Dr. Rao Mikkilineni, Ph D.
- 15. 16 Composable Services withCognitive 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) Copyright 2018 Dr. Rao Mikkilineni, Ph D.
- 16. 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 Copyright 2018 Dr. Rao Mikkilineni, Ph D.
- 17. The Cognitive Enterprise 18 LegacyApps 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 Copyright 2018 Dr. Rao Mikkilineni, Ph D.
- 18. New Cloud NetworkDeployment at the Edge 19Copyright 2018 Dr. Rao Mikkilineni, Ph D. 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.
- 19. Deep Learning Networks 20Copyright2018 Dr. Rao Mikkilineni, Ph D.
- 20. Emergence of CognitiveComputing ModelsEnabling Multi-Service Platform The Theory Behind 21Copyright 2018 Dr. Rao Mikkilineni, Ph D.
- 21. In The Beginning… Thereare 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 Copyright 2018 Dr. Rao Mikkilineni, Ph D.
- 22. The First Wave Church-TuringThesis 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) Copyright 2018 Dr. Rao Mikkilineni, Ph D.
- 23. 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) Copyright 2018 Dr. Rao Mikkilineni, Ph D.
- 24. …Computation and ItsLimits “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 … Copyright 2018 Dr. Rao Mikkilineni, Ph D.
- 25. Cognition, Self-Management andAutonomic 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
- 26. A Cognitive ComputingModel 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/
- 27. 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) Copyright 2018 Dr. Rao Mikkilineni, Ph D.
- 28. 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.
- 29. What Does BiologyTell Us? 30Copyright 2018 Dr. Rao Mikkilineni, Ph D.
- 30. Where do weGo from Here? 31Copyright 2018 Dr. Rao Mikkilineni, Ph D.
- 31. Enabling the Data-DrivenCognitive Era The Vision 32Copyright 2018 Dr. Rao Mikkilineni, Ph D.
- 32. Big Data andthe 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 Copyright 2018 Dr. Rao Mikkilineni, Ph D.
- 33. Current State ofthe 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 Copyright 2018 Dr. Rao Mikkilineni, Ph D.
- 34. Cognitive Business ProcessDynamics 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 Copyright 2018 Dr. Rao Mikkilineni, Ph D.
- 35. The New Data-DrivenEnterprise 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 36Copyright 2018 Dr. Rao Mikkilineni, Ph D.
- 36. 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.
- 37. 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 38Copyright 2018 Dr. Rao Mikkilineni, Ph D.
- 38. Further Food forThought 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. 39Copyright 2018 Dr. Rao Mikkilineni, Ph D.