1. COT 5611 Operating Systems Design Principles Spring 2014
Dan C. Marinescu
Office: HEC 304
Office hours: M-Wd 3:30-5:30 PM

2. Lecture 3
Reading assignment: class slides of Lectures 2 and 3 and the class notes “distributed systems-basic concepts” available online.
Last time
Man-made systems and the environment
Complex systems
Factors affecting complexity
Sources of complexity
Coping with complexity
Modularity
Abstractions
Hierarchy
Characteristics of complex systems
Propagation of effects
Incommensurate scaling
Tradeoffs
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Lecture 3

3. Today Computers as a distinct species of complex systems
Milestones in the evolution of computing and communication technology.
Factors affecting the complexity of computing and communication systems
The software
The exponential growth
Coping with complexity
Resource sharing and complexity
Scale-free systems or how nature deals with complexity.
Lessons learned.
Names and fundamental abstractions
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4. Innovations are now full-circle
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5. Factors affecting complexity of computing and communication systems
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6. Analog, digital, and hybrid systems
Analog systems:
the noise from individual components accumulate and
the number of components is limited
Digital systems:
are noise-free
the number of components is not limited
regeneration  restoration of digital signal levels
static discipline the range of the analog values a device accepts for each input digital value should be wider than the range of analog output values
digital components could fail but big mistakes are easier to detect than small ones!!
Hybrid systems
e.g., quantum computers and quantum communication systems
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7. Factors affecting complexity of computing and communication systems
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8. Computers a distinct species of complex systems
The complexity of computer systems not limited by the laws of physics  distant bounds on composition
Digital systems are noise-free.
The hardware is controlled by software
The rate of change unprecedented
The cost of digital hardware has dropped in average 30% per year for the past 35 years
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9. Computers are controlled by software
Composition of hardware limited by laws of physics.
Composition of software is not physically constrained;
software packages of 107 lines of code exist
Abstractions hide the implementation beneath module interfaces and allow the
creation of complex software
modification of the modules
Abstractions can be leaky. Example, representation of integers, floating point numbers.
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10. Exponential growth of computers
Unprecedented:
when a system is ready to be released it may already be obsolete.
when one of the parameters of a system changes by a factor of
2 other components must be drastically altered due to the incommensurate scaling.
10  the systems must be redesigned; E.g.; balance CPU, memory, and I/O bandwidth;
does not give pause to developers
to learn lessons from existing systems
find and correct all errors
negatively affects “human engineering”  ability to build reliable and user-friendly systems
the legal and social frameworks are not ready
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11. Coping with complexity of computer systems
Modularity, abstraction, layering, and hierarchy are necessary but not sufficient.
An additional technique  iteration
Iteration
Design increasingly more complex functionality in the system
Test the system at each stage of the iteration to convince yourself that the design is sound
Easier to make changes during the design process
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12. Iteration – design principles
Make it easy to change
the simplest version must accommodate all changes required by successive versions
do not deviate from the original design rationale
think carefully about modularity  it is very hard to change it.
Take small steps; rebuild the system every day, to discover design flaws and errors. Ask others to test it.
Don’t rush to implementation. Think hard before starting to program.
Use feedback judiciously
use alpha and beta versions
do not be overconfident from an early success
Study failures  understand that complex systems fail for complex reasons.
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13. Curbing complexity
In absence of physical laws curb the complexity use good judgment.
Easier said than done because:
temptation to add new features than in the previous generation
competitors have already incorporated the new features
the features seem easy to implement
the technology has improved
human behavior: arrogance, pride, overconfidence…
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14. Resource sharing and complexity
A main function of the OS is resource sharing.
Sharing computer resources went through several stages with increased levels of complexity:
A. Many-to-one
B. One-to-one
C. Many-to-many
D. Peer-to-peer
E. Cloud Computing
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17. Millions of lines of source code
Software follows hardware – the operating systems become increasingly more complex
Millions of lines of source code
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18. Cheap  Pervasive
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19. Pervasive  qualitative change
Number crunching
log (people per computer)
Word processing
Communication
So if we look at the computing spectrum today, each of the classes exist simultaneously. In a room we can put 100s of teraflops and many petabytes of computing.
Our productivity, document preparation, and personal information management fits in our pocket and a new class is emerging that will provide a means of streaming information to and from the physical world like we have never seen before.
Embedded
Sense/control
year
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Slide from David Culler, UC Berkeley

20. Latency improves slowly
Moore’s law (~70% per year)
Improvement wrt year #1
Speed of light (0% per year)
DRAM access latency (~7% per year)
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Year #

21. Heat is a problem
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22. Recent Intel CPU Clock Rate
Pentium 4 HT
Pentium 4
Pentium III
mHz
PentiumPro
Pentium
486
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23. What went right? Unbounded composibility General-purpose computers
Only need to make one thing fast
Separate architecture from implementation
S/W can exploit new H/W
Cumulative R&D investment over years
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24. How the nature deals with complexity
For biological systems: symmetry, construction of complex biological structures from building blocks.
Self-organization though difficult to define, its intuitive meaning is reflected in the observation made by Alan Turing that ``global order can arise from local interactions''
Scale-free systems. Each component interacts directly only with a small number of other components.
Man-made systems to imitate nature!!
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25. Scale-free systems
The scale-free organization can be best explained in terms of the network model of the system, a random graph with vertices representing the entities and the links representing the relationships among them.
In a scale-free organization, the probability P(m) that a vertex interacts with m other vertices decays as a power law:
with d a positive real number, regardless of the type and function of the system, the identity of its constituents, and the relationships between them.
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26. Examples of scale-free systems
The collaborative graph of movie actors where links are present if two actors were ever cast in the same movie; in this case d=2.
The power grid of the Western US has some 5,000 vertices representing power generating stations; in this case d=4.
The World Wide Web, d=2.1. This means that the probability that m pages point to one page is
P(m) = m-2.1
The citation of scientific papers d=3.
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