Overview of the seminar series at UTFSM
sponsored by the Fulbright Foundation
Dan C. Marinescu
Computer Science Division, EECS Department
University of Central Florida
Email: dcm@cs.ucf.edu
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The topics
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The eight seminars will be devoted to two topics related to information
processing
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Cloud computing  how can information processing be made more
economical, convenient, and effective today.
Quantum information processing  how information processing may look
tomorrow.
The seminars will be on Tuesdays and Thursdays.
The seminars are based on
Cloud Computing: Theory and Practice
http://www.cs.ucf.edu/~dcm/LectureNotes.pdf
(to be published in 2013 by Morgan Kaufmann.
Classical and Quantum Information (Chapter 1)
(by D.C. Marinescu and Gabriela M. Marinescu) published in 2011 by
Academic Press and available at
http://www.cs.ucf.edu/~dcm/Chile2012/ChileIndex.html
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Information
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2,450,000,000 Google hits for the word “information”.
The earliest historical meaning of the word information in English
was related to the act of informing, or giving form or shape to the
mind, as in education, instruction, or training. A quote from 1387:
"Five books come down from heaven for information of mankind."
(Oxford English Dictionary)…..Amazon.com was established later….
2672? (Japanese imperial
year based on the mythical founding of Japan by
Emperor Jimmu in 660 BC)
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More about information
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Information is a primitive concept (like matter or energy).
Information abstracts properties of and allows us to distinguish
objects/entities/phenomena.
There is a common expression of information, strings of bits, regardless
of the object/entity/process it describes. Bits are independent of their
physical embodiment.
Information is transformed using logic operations. Gates implement
logic operations and allow for automatic processing of information. The
usefulness of information increases if the physical embodiments of bits
and gates become smaller and we need less energy to process, store,
and transmit information.
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Network-centric computing and network-centric content
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John McCarthy was a visionary in computer science; in the early
1960s he formulated the idea that computation may be organized as
a public utility, like water and electricity.
Information processing can be done more efficiently on large farms
of computing and storage systems accessible via the Internet.
Grid computing – initiated by the National Labs in the early 1990s;
targeted primarily at scientific computing
 Utility computing – initiated in 2005-2006 by IT companies and targeted
at enterprise computing.
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While utility computing often requires a cloud-like infrastructure, its
focus is on the business model for providing the computing services
is based; cloud computing is a path to utility computing embraced by
major IT companies such as IBM, HP, Amazon, Oracle, Microsoft,
and others.
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The main features of cloud computing
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Uses Internet technologies to offer scalable and elastic services; the term
``elastic computing'' refers to the ability of dynamically acquiring
computing resources and supporting a variable workload.
The resources used for these services can be metered and the users can
be charged only for the resources they used.
The maintenance and security are ensured by service providers.
The service providers can operate more efficiently due to specialization
and centralization.
Cloud computing is cost-effective because of the multiplexing of
resources; lower costs for the service provider are past to the cloud users.
The application data is stored closer to the site where it is used in a device
and in a location-independent manner; potentially, this data storage
strategy increases reliability, as well as security and lowers
communication costs.
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Network-centric content
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Content  any type or volume of media, be it static or dynamic, monolithic
or modular, live or stored, produced by aggregation, or mixed.
The “Future Internet” will be content-centric; the creation and consumption
of audio and visual content is likely to transform the Internet to support
increased quality in terms of resolution, frame rate, color depth,
stereoscopic information.
Network centric content and network-centric computing share several traits:
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Data intensive  large scale simulation in science and engineering require
large volumes of data. Multimedia streaming transfers large volume of data.
Network-intensive  transferring large volumes of data requires high bandwidth
networks; parallel computing, computation stirring, data streaming require low
latency networks.
The systems are accessed using thin clients running on systems with limited
resources, e.g., wireless devices such as smart phones and tablets.
The infrastructure should support some form of workflow management.
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The “good” about cloud computing
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Shared resources such as CPU cycles, storage, network bandwidth);
resources can be aggregated to support data-intensive applications. When
multiple applications share a system their peak demands for resources are
not synchronized thus, multiplexing leads to a higher resource utilization.
Data sharing facilitates collaborative activities. Indeed, many applications in
science, engineering, as well as, industrial, financial, governmental
applications require multiple types of analysis of shared data sets and
multiple decisions carried out by groups scattered around the globe.
Cost reduction; concentration of resources creates the opportunity to pay as
you go for computing and thus eliminates the initial investment costs for a
private computing infrastructure as well as, the significant maintenance and
operation costs.
User convenience and elasticity; the ability to accommodate workloads
with very large peak-to-average ratios.
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Why cloud computing could be successful when other
paradigms have failed?
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It is in a better position to exploit recent advances in software, networking,
storage, and processor technologies promoted by the same companies who
provide cloud services.
It is focused on enterprise computing; its adoption by industrial
organizations, financial institutions, healthcare organizations and so on, has
a potential huge impact on the economy.
A cloud consists of a homogeneous set of hardware and software resources
in a single administrative domain (ADMIN). Security, resource management,
fault-tolerance, and quality of service are less challenging than in a
heterogeneous environment with resources in multiple ADMINs.
Provides the illusion of infinite computing resources; its elasticity frees the
applications designers from the confinement of a single system.
A cloud eliminates the need for up-front financial commitment and it is based
on a pay-as-you-go approach. Has the potential to attract new applications
fomenting a new era of industry-wide technological advancements.
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Challenges; research problems
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Availability of service; what happens when the service provider cannot
deliver?
Data lock-in; once a customer is hooked to one provider it is hard to
move to another. The standardization efforts at NIST!
Data confidentiality and auditability, a serious problem.
Data transfer bottlenecks; many applications are data-intensive.
Performance unpredictability, one of the consequences of resource
sharing; how to use resource virtualization and performance isolation
for QoS guarantees?
Elasticity, the ability to scale up and down quickly.
Resource management  is self-organization and self-management a
solution?
Security and confidentiality  new ideas badly needed.
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Topics for the cloud computing talks
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Cloud 2 - Basic concepts
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Cloud computing paradigms and services
 Cloud diversity and vendor lock in
 Energy use and ecological impact of large-scale data centers
 Ethical issues in cloud computing
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Cloud 3 - Cloud infrastructure and applications
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AWS - the Amazon Web Services
Google's view of cloud computing
Microsoft's Azure
Open-source platforms for cloud computing
How to use the AWS
Case study 1 - a cloud service for trust management in cognitive radio
networks
Case study 2 - adaptive data streaming from a clou
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Cloud infrastructure and applications (cont’d)
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Cloud 3; Cloud computing
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Challenges
Existing and new applications
Coordination and the Zookeeper
The Map-Reduce programming model
The GrepTheWeb application
Clouds in science and engineering
Benchmarks
Cloud 4; Virtualization
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Layering and virtualization
Virtual machines
Virtual machine monitors
Performance isolation; security isolation
Full and paravirtualization
Xen and vBlades
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Challenges for the future of information processing
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Heat dissipation, leakage, and other physical phenomena limit our
ability to build increasingly faster and, implicitly, increasingly smaller
solid-state devices.
It is very difficult to ensure the security of our communication.
We are overwhelmed by the volume of information we are bombarded
with, and it is increasingly more difficult to extract useful information
from the vast ocean of information surrounding us.
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Quantum theory; quantum information processing
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Quantum  Latin word meaning some quantity; in physics it is used with
the same meaning as the word discrete in mathematics.
Quantum theory is a mathematical model developed by Werner Heisenberg
and Erwin Schrödinger in the mid-1920s to describe the behavior of atomic
and sub-atomic particles.
The quantum mechanical model is characterized by the way it represents
the states of a physical system, the observables of the system, the
measurements of these observables, and the dynamics of the system.
Most laws governing the physical phenomena studied by quantum physics
are counter-intuitive and can only be presented with a rather sophisticated
mathematical apparatus.
Quantum Information Science  a new discipline emerged at the
intersection of Physics, Mathematics, and Computer Science.
Quantum Information Processing covers the transformation, storage, and
transmission of quantum information.
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Quantum systems
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Quantum concepts such as:
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Uncertainty,
 Superposition,
 Entanglement,
 No-cloning
do not have a correspondent in classical physics.
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Heisenberg’s Uncertainty Principle: the position and the momentum of
a quantum particle cannot be determined with arbitrary precision.
X  PX  h / 4
h=6.6262 x 10-34 Joule x second  Planck’s constant
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Non-determinism is a basic tenet of quantum physics
“Liebe Gott würfelt nicht”
(Dear God does not play
dice)
- Albert Einstein
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Polarization of Light
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Light  electromagnetic radiation. The electric and magnetic field
oscillate in a plane perpendicular to the direction of propagation and are
perpendicular to each other.
The dual, wave and corpuscular, nature of light:
Diffraction phenomena  wave-like behavior
 Photoelectric effect
 corpuscular/granular The light consists of quantum
“particles” called photons.
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Photon polarization is given by the electric field vector
Linearly polarized (vertical/horizontal)  the tip of the electric field vector
oscillates along any straight line in a plane perpendicular to the direction of
propagation.
 Circularly polarized (right- /left-hand)  the tip of the electric field vector
moves along a circle in a plane perpendicular to the direction of propagation:
 Elliptically polarized light  the tip of the electric field vector moves along an
ellipse in a plane perpendicular to the direction of propagation.
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In a beam of linearly polarized light each photon has a random
orientation of the polarization vector.
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Outlines of QIP seminars
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QIP1: Basic concepts
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The limitations of solid state technology
 Quantum information, the mathematical model of a qubit
 A historic perspective
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QIP2: Physical implementation of qubits
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Quantum gates and quantum circuits
One qubit gates: X, Y, Z, Hadamard, and phase-shift
Two qubit gates: CNOT
Three qubit gates: Fredkin and Toffoli
Universality of quantum gates
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Outline of QIP seminars (cont’d)
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QIP3 - Quantum computational models; quantum algorithms
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The quantum circuit model
Deutsch and Deutsch-Josza algorithm
Bernastein-Vazirani algorithm
Amplitude amplification
Grover's quantum search algorithm
QIP4 – Quantum information
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Density matrix
Pure and mixed states
Entanglement, monogamy of entanglement
The no-cloning theorem
Accessible information in a quantum measurement
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