Lecture2 - COT 5611 Operating Systems Design Principles...

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COT 5611 Operating Systems Design Principles Spring 2010 Dan C. Marinescu Office: HEC 439 B Office hours: M-Wd 1:00-2:00 PM
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Innovations are now full-circle 2
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Factors affecting complexity 3 Complexity of computing and communication systems New components New applications Interconnectivity + mobility, embedded devices Physical constraints Larger segment of population using the systems Optimization of resource consumption Timing Constraints
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Software follows hardware 0 10 20 30 40 50 60 W i ndow s 3. 1 ( 1992) Windows NT Solaris (1998) Windows 95 Windows 98 N T 5. 0 RedHat Linux 6.2 (2000) R ed Hat Linux 7.1 ( 200 1) ndows X P Vista Millions of lines of source code
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Cheap Pervasive
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Pervasive qualitative change year log (people per computer) Slide from David Culler, UC Berkeley Number crunching Embedded Sense/control Word processing Communication
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Latency improves slowly 1 10 100 1000 123456789 1 0 1 1 Year # Improvement wrt year #1 Moore’s law (~70% per year) DRAM access latency (~7% per year) Speed of light (0% per year)
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Heat is a problem
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Recent Intel CPU Clock Rate 486 Pentium PentiumPro Pentium III Pentium 4 Pentium 4 HT mHz
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Lecture 2 10 Systems and the environment ± System Î a set of interconnected components that has a an expected behavior observed at the interface with its environment ± The environment Î a critical component to be considered in the design of any system ± The systems we are concerned consist of ² hardware and ² software
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Lecture 2 11 Two sources of complexity 1. Cascading and interacting requirements ± 1.1 When the number of requirements grows then the number of exception grows. ± 1.2 The principle of escalating complexity:
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Lecture 2 12 Two sources of complexity (cont’d) ± 1.3 Meeting many requirements with a single design Î the need for generality. Advice: avoid excessive generality. ± 1.4 Requirement changes: ² Example: the electric car produced by Tesla.
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Lecture 1 13 Two sources of complexity (cont’d) ± 2. High performance ² 2.1 Every system must satisfy performance standards. ² 2.2 The law of diminishing return Î the more one improves one performance metrics the more effort the next improvement will require
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Lecture 2 14 Modularity for Coping with Complexity ± Why does modularity reduce complexity Î we can focus on the interaction within one module/component. ± Example: assume that: ² B - the # of bugs in a program is proportional with N , the number of statements ² T- the time to debug a program is proportional with N x B thus it is proportional with N 2 ² Now we divide the program in K modules each with N/K statements each: ± The time to debug a module is proportional with (N/K) 2 ± The time to debug the K modules is K x (N/K) 2 = N 2 /K ± We have reduced the time by a factor of K. Is that so?
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Lecture 2 15 Abstractions and Coping with Complexity ± Abstraction Î separation of the ² interface from the internals or ² specification from implementation Example: you do not need to know how the engine of your car works in order to drive the car ± Why abstractions reduce complexity Î because they minimize the interconnections between components.
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This note was uploaded on 05/12/2010 for the course CS COP5611 taught by Professor Dancristianmarinescu during the Spring '10 term at University of Central Florida.

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Lecture2 - COT 5611 Operating Systems Design Principles...

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