1. ARAB ACADEMY FOR SCIENCE &
TECHNOLOGY & MARITIME TRANSPORT
College of Engineering & Technology
Computer Engineering Department
Post-Graduate Student
System Science & Engineering Documentation
By:
Eng. Ismail Fathalla El-Gayar
Under Supervision Of:
Prof.Dr. Mohamed Taher El-Sonni
Dr. Ahmed Abou-El-Farag
2. System Science & Engineering
Contents
Part 1 : System Science & Engineering
Introduction
• Motivation & Applications
• Report Organization
System Concepts & Definitions
Introduction To System
o System Definition
o System Classification
o System science
o System Engineering
System Function, Behavior and Structure
o System Patterns
o System Structure & Dynamics
o System Behavior
o System Properties
o System Characteristics
o System Sustainable
o System Life Cycle
o System Development Life Cycle
Related System Definitions
o Engineering & Scientific Methodology
o Integrated Logistic Support
o Systematic
o Cybernetics
o Ergonomics
o Systemic
Feedback & Feedback Types
o Introduction To Feedback
o Feedback Importance
o Feedback Types
Introduction To System Modeling
Thinking Process
o Analogical
o Inductive
o Deductive
o Abductive
System Modeling
• Linguistic
• Visualization
• Mathematical
• Physical
3. Part 2: Statistics & Probability
Overview Of Statistics & Probability
Basic Concepts on Statistics & Probability
o Basic Concepts
o Measure Of Dispersions
o Causes of not knowing things precisely
o Probability & Density Functions
o Distributions
Stochastic Process & Markov Chain
o Stochastic Process
o Markov Chain
Principal Component Analysis ( PCA)
o Definition
o Applications
o Graphical Model
o Complete Example
Part 3 : Case Study : Dependability
Introduction To Dependability
Dependability Elements
o Attributes
Availability
Reliability
Safety
Confidentiality
Integrity
Maintainability
o Threats
Fault
Error
Failure
o Means
Fault Preventation
Fault Removal
Fault Forecasting
Fault Tolerance
4. Fault, Error & Failure Classifications
o Fault Classes
o Error Classifications
o Failure Classes
Measuring Dependability
o Measuring Dependability Concepts
o Fault Tree Analysis Method
o Software Tools For Measuring Dependability
Dependability Benchmark
o Benchmark & Dependability Benchmark
o Elements of Performance & Dependability Benchmarking
o Basic Definitions on Dependability Benchmarking
Summary & Conclusion
List Of References & Figures
o List Of References
o List Of Figures
7. System Science & Engineering is one of the most important Courses in our life, This
course has a different felling for anyone who take this course, it depend on how you think and
how you imagine the course , this course learn me a lot of things first of all learned me how to
be a philosopher , how to illustrate what I think in a good way , I learned also the scientific
methodology on thinking how to base my idea & how to think in a good way , the
representation of the knowledge how to be so simple & in this document , I tried to make this
concept & trained to be good on it by using system models that I found it so interested as the
visualization ,mathematical , linguistic & physical model , this course that I really enjoyed so
much in learning it and I really want to learn system more & more , I learned also practical
expressions that benefit me in my work in any system , I learned about how system be
Reliable , Available , Usable , maintainable ….etc , The dependability was my case study in
this document I learned how system can be dependable & how to measure this dependability?
, also I learned the fault , error & failure chain which harm any system and how to detect it &
stop it fast before it will be hazard or a Failure of System . Also Another thing which I
learned about Markov chain & Stochastic process which helps me a lot in analysis of any
process also the transforms , probability , statistics ,Principal component analysis , so I
Learned a tools which I can benefit from them a lot in my life in analysis any system or
problem I will find in my life . All of this but still more & more Benefits I don't mention yet
so as many as I talk, I can't explain this course represents what for me.
Motivation & Applications:-
System Science & Engineering was a very successful course to me , I have learned
many topics which will help me in life , all of this topic I have learned from my masters
Prof.Dr.Mohamed Taher El-Sunni & Dr.Ahmed Abo El-Farag which I want to thanks
Them both for their efforts on this course which was very successful to me, So from this
beginning point my masters in this course was the first to motivate me to make this
documentation to illustrate what I have been learned in this course , I really enjoying making
this document because it's content is what I have learned for 5 months being in this term as a
topic & more of this as a methodologies of how I can think & How can I simplify The
information & See all things from A holistic view, In my point of view I see that a course like
system science & Engineering must be learned to all engineers in the world so that they can
know how to deal with a system well, how to control & know the performance of this system,
how to develop the system….etc . So this was my second motivate to make this document & I
will give it to all the engineers I know to be an abstract & a reference to one of the most
important courses in the world.
This Course Application is too many in any factory, any system in your house as an example :
car, refrigerator, television, computer … etc , you will need the basic of system science to
know this system well.
8. Report Organization:
The Report Organized in an illustrative flow which makes the reader can imagine &
understand the topic well as follow:
Part 1: System Science & Engineering
This Part introduces The Meaning Of System , Characteristics , properties , attributes ,
classification & Some definitions that relate to system science & System engineering also
this chapter has many exciting topics like system like system life cycles & Development life
cycles , System feedbacks & it's types , thinking process types & what is meant by system
process ? , System modeling & its importance & Types, As We See this part talking generally
about System Science & its Related Topics.
Part 2: Statistics & Probability
This Part introduces probability & statistic Concepts, Importance, Applications. Also talking
about Joint probability & Distributions Types & Normal Distribution as An Example, Also
Talked about exciting topics most use this days like Stochastic Processes & Markov chain,
Principal Component Analysis…etc.
Part 3: Case Study
The Last Part talking about my Case Study which is the Dependability of Any System as a
whole view to the dependability, performance, measures, I also take a look about another
attributes like Reliability, Availability, Maintainability, Safety, Confidentiality, Integrity also
I take a look about the threats of dependability which cause dependability failure & minimize
the dependability of a system like the faults, errors & failure chain and the way to preventing,
removing, forecasting, tolerance this error , Also talked about dependability Benchmark &
Software used for the benchmarking.
10. Introduction To System:-
Definition of SYSTEM:- A set of components integrated together to perform
a certain goal surrounded by a certain Environment within a boundary observed by a
set of observers
Figure .121
Comparing between Some Definitions Of important
Organizations & Known Authors in SYSTEM:-
Define Components Integration Goal
ANSI/EIA[1] end products , enabling aggregation To achieve a given purpose.
products
IEEE[2] elements and processes A set or arrangement - whose behavior satisfies
related customer/operational needs and
provides for life cycle sustainment
of the products
ISO/IEC[3] elements A combination - to achieve one or more stated
interacting elements - purposes
organized
NASA[4] elements (include all The combination - to produce the capability to meet a
hardware, software, function together need.
equipment, facilities,
personnel, processes, and
procedures needed for
this purpose)
11. Classification of Systems:
Natural System and Human-Made System:
Natural System – a high degree of order and equilibrium, such as
seasons, food chains, water cycle
Human-made system – technology based system
Physical and Conceptual System:
Physical system – in physical form or space
Conceptual system – in ideas, plans, concepts, hypotheses
Static and dynamic System:
Static system – structure without activity
Dynamic system – structural components with activity
Closed and Open System:
Closed system – one that does not interact with its environment
Open system – one that interact with its environment
What is SYSTEM SCIENCE:-
• Is an interdisciplinary field of science that studies the nature of complex
systems in nature, society, and science, It aims to
develop interdisciplinary foundations, which are applicable in a variety of
areas, such as engineering, biology, medicine and social sciences.
What is SYSTEM ENGINEERING:-
• is Defined as the art of designing & Optimizing Systems , Starting with
expressed needs & ending up with the complete set of specifications for all the
system elements (Aslaksin& Belcher 992)
12. System Function, Behavior and Structure:-
System Patterns:-
A pattern is more than either just the problem or just the solution
structure:
It includes both the problem and the solution, along with the rationale that binds them
together. A problem is considered with respect to conflicting forces, detailing why the
problem is a problem. A proposed solution is described in terms of its structure, and
includes a clear presentation of the consequences both benefits and liabilities—of
applying the solution.
Types of Patterns:-
Low level pattern to solve implementation
Idioms specific problems
Medium scale pattern to organize sub-
Design system functionality in application domain
in independent way
High Level pattern to help to specify the
Architecture fundamental structure of the system
Figure .122
Architecture Pattern:-
Expresses a fundamental structural organization schema for any system .it provides a
set of predefined sub-systems, specifies their responsibilities, and includes rules and
guidelines for organizing the relationships between them [Buschmann, Meunier,
Rohnert, Sommerland]
Design Pattern:-
Describes a commonly- recurring structure of communicating components that solve a
general design problem in a particular context [Gamma , Helm , Johnson]
Idioms Pattern:-
Describes how to implement particular aspects of components or the relationships
between them. [Buschmann, Meunier, Rohnert, Sommerland][*]
13. Note: Each pattern is a three-part rule, which expresses a relation between:-
( a certain context, a problem, and a solution).
System Structure & Dynamics:-
System Structure: - A graphical representation of the pattern. Class
diagrams and Interaction diagrams may be used for this
System dynamics: -
is an approach to understanding the behavior of complex over time. It deals
with internal feedback loops and time delays that affect the behavior of the entire
system. What makes using system dynamics different from other approaches to
studying complex systems is the use of feedback loops and stocks and flows. These
elements help describe how even seemingly simple systems display
baffling nonlinearity.
System Behavior:-is what the system does to implement its function and is described
by a sequence of states.
System Attributes:-
The term attributes classifies functional or physical features of a system.
Examples include gender; unit cost; nationality, state, and city of residence; type of
sport; organizational position manager; and fixed wing aircraft versus rotor.(Wasson)
System Properties:-
The term, properties, refers to the mass properties of a system.(Wasson)
Examples include composition; weight; density; and size such as length, width, or
height.
14. System Characteristics:-
The term characteristics refer to the behavioral and physical qualities that
uniquely identify each system. (Wasson)
- Behavioral characteristics examples include predictability and
responsively.
- Physical characteristics examples include equipment warm-up
and stabilization profiles; equipment thermal signatures; aircraft radar
cross-sections; vehicle acceleration to cruise speed, handling, or stopping;
and whale fluke markings.
When we characterize system, there are four basic types of characteristics we consider:
General Characteristics
•stated in marketing brochures where key features are emphasized to capture a client
Operating or Behavioral Characteristics
•describe system features related to usability, survivability, and performance
Physical Characteristics
•relate to nonfunctional attributes such as size, weight, color, capacity
System Aesthetics
•relate to the “look and feel” of a system
. Figure .123
15. System Sustainable:-
Sustainability refers to a quality and system of life that allows people to meet
their current needs without compromising the resources available for future
generations to meet their future needs. Sustainability rests on the belief that we can
coexist with the environment if we work to ensure our actions are not harmful to it.
Essentially, it means ensuring that we leave our environment no worse than we found
it.
System Life Cycle
Development
Production
Operation
Disposal
Figure .124
17. Related System Definitions:-
System thinking:-
Is a framework that is based on the belief that the component parts of a system
can best be understood in the context of relationships with each other and with other
systems, rather than in isolation.
Systematic
• is a study of systems and their application to the problem of understanding
ourselves and the world,
– Formal Systematic
– Pure Systematic
– Applied Systematic
– Practical Systematic
Cybernetics
Is the interdisciplinary study of the structure of regulatory systems.
Cybernetics is closely related to control theory and systems theory. cybernetics is
equally applicable to physical and social (that is, language-based) systems
Systemic
To study systems from a holistic point of view. It is an attempt at
developing logical, mathematical, engineering and philosophical paradigms and
frameworks in which physical, technological, biological, social, cognitive, and
metaphysical systems can be studied and modeled.(Bunge (1979))
18. Ergonomics
Is the scientific discipline concerned with the understanding of
interactions among humans and other elements of a system, and the profession that
applies theory, principles, data and methods to design in order to optimize human
well-being and overall system performance.(International Ergonomics Association)
Methodology:
"the analysis of the principles of methods, rules, and postulates
employed by a discipline"
"the systematic study of methods that are, can be, or have been
applied within a discipline"
Scientific Methodology: - (deduced from Definition of Methodology)
Is To Analysis by a scientific way ( Methods , Rules )
Engineering Methodology: - (deduced from Definition of Methodology)
Is To Analysis by an Engineering way ( Methods , Rules )
Integrated Logistic Support (ILS):-
Is the management organization that plans and directs the activities of many
technical disciplines associated with the identification and development
of logistics support and system requirements for military systems or equipment / parts
19. Feedback & Feedback Types
Introduction to Feedback
When the system is part of a chain of cause-and-effect that forms a circuit or
loop, then the event is said to "feed back" into itself.
Feedback Importance
Feedback used to give indicator about the output is the output is
good or we need to change in the input or in the system.
It is very important in any system to develop the performance of
the system feedback methods also used in community systems & society
systems not also the systems related to engineering.
Feedback Types
Feedback has many types we can't mentioned it all so we will mention as an
example:-
Positive & Negative Feedback
Positive when the feedback signal can amplify the input signal, leading to
more modification.
Negative when the feedback signals dampen the effect of the input signal,
leading to less modification.
Introduction to System Modeling:-
System modeling is a technique to express, visualize, analyze and transform
the architecture of a system. Here, a system may consist of software components,
hardware components, or both and the connections between these components. A
system model is then a skeletal model of the system.
20. Thinking Process:-
Is any process of estimating or inferring how local policies, actions, or changes
influence the state of the neighboring universe
It also can be defined, as an approach to problem solving, as viewing "problems" as
parts of an overall system, rather than reacting to present outcomes or events and
potentially contributing to further development of the undesired issue or problem
Analogical
Refers to a process of finding and using a known experience or domain to
understand an unknown phenomenon or domain.
Inductive
Moving from specific observations to broader generalizations and theories.
Informally, we sometimes call this a "bottom up" approach (please note that it's
"bottom up" which is the kind of thing the bartender says to customers when he's
trying to close for the night!).
Deductive
Works from the more general to the more specific. Sometimes this is informally
called a "top-down" approach. We might begin with thinking up a theory about our
topic of interest. We then narrow that down into more specific hypotheses that we can
test. We narrow down even further when we collect observations to address the
hypotheses.
Abductive
Starts from a set of accepted facts and infers their most likely, or best, explanations.
The term abduction is also sometimes used to just mean the generation of hypotheses
to explain observations or conclusions
22. Model:-
A model is a simplification of another entity, which can be a physical thing or
another model. The model contains exactly those characteristics and properties
of the modeled entity which are relevant for a given task. A model is minimal
with respect to a task, if it does not contain any other characteristics than those
relevant for the task.
A model is a representation of one or more concepts that may be realized
in the physical world. It generally describes a domain of interest. A key
feature of a model is that it is an abstraction that does not contain all the detail
of the modeled entities within the domain of interest. Models are represented
in many forms including graphical, mathematical, and logical representations,
and physical prototypes.
For example, a model of a building may include a blueprint and a scaled prototype
physical model. The building blueprint is a specification for one or more buildings
that are built. The blueprint is an abstraction that does not contain all the building's
detail such as the characteristics of its materials.
A model must:
Relates to an entity
be a simplification of that entity
be a related to a task and an objective
may relate to a not yet existing entity
23. Modeling Types:-
Modeling Methods
Linguistic Visualization Mathematical Physical
Modeling Modeling Modeling Modeling
Describe by Describe by Describe by Describe by
Words Graphs & Mathematical tangible Materials
Animations Equations
Figure .131
Linguistic Modeling:-
It is a method for Modeling by using Language describe our system by
language Expressions
Example: Description Of A Car:- It is a block in which has 4 tires , it moves
forward & Backward , this block consist of a Salon , Engine ,Electrical &
Mechanical Sub-systems , Used for traveling distances.
Visualization Modeling:-
It is a method for modeling by using a visualize images & Diagrams to express
The system idea, relationships, components ….etc
24. As Seen In The Figure (A Periodic Table Of Visualization Method):
Figure .132
The Table Consist Of category for Visualization (by Colors):-
Metaphor Compound Strategy
visualization visualization visualization
Concept Information Data
visualization visualization visualization
Figure .133
25. Mathematical Modeling:-
It is a method for modeling by using mathematical equations to express the
system as an equation & variables
A representation of the essential aspects of an existing system , which presents
knowledge of that system in usable form'. (Eykhoff (1974))
Example:-
Physical Modeling:-
It is a method for modeling by using tangible materials to express the system,
can be a physical object such as an architectural model of a building. Uses of
an architectural model include visualization of internal relationships within the
structure or external relationships of the structure to the environment.
Example As An Empty Cup :-
Figure .134
28. In This Part We will go to a journey around probability & statistics & Method
used in system science applications & Computations we will take a look about
probability computations & how to analyze data on statistics & classify them by
different method & how we can deal with different types of data & variables
(Continuous & discrete) also we will learn about markov chain & its application on
dependability methods, also we will talk about principal component analysis method
and its role in researches area, we will talk about how we can get another dimensions
by an illustrative example
Importance
Statistics & Probability are very important in this course to simplify the
analysis of the data & help us to improve performance of the system. we can measure
system performance, availability, reliability, dependability, usability and all the
abilities of the system using probability & statistics method which will be illustrate in
this chapter, this chapter will help us to practically apply these definitions & concepts
on any system we want.
Applications
Applications of these chapter varies in many science & situations we meet in
our life, we will learn some concepts must be understood well, for solving problems
in our life, we will learn how to measure the meaning by a different methods, we can
benchmarking systems by these methods, these methods is the practical view for
system science & Engineering course, in which we can develop ourselves & practice
these in our field.
30. Basic Definitions:-
Probability
It is the likelihood—or chances—of something to happened
Do we have a better chance of it occurring or do we have a better chance of it
not occurring?
Types Of Probability:-
- Empirical Probability
It is determined from repeated experimentation and observation, recording
results.
- Theoretical Probability
It is determined using mathematical computations based on possible results,
or outcomes.
Statistics
Analysis and Interpretation of numerical data
A number summarizing a bunch of values
Data
Collection and compilation of relevant information
Data are a bunch of values of one or more variables.
Variable
A variable is something that has different values
Discreet variable
Continuous variable
Independent Events
Two events are called independent if the occurrence of one event does not in
any way affect the probability of the other event
Random Variable
A variable is called a random variable if it takes one of a specified set of
values with a specified probability.
31. Measure of Dispersions:-
Measure By Central Tendency :-
The Mean*
• Arithmetic Average Value
The Mode
• Most frequently Used Value
The Median
• Middle value after arranging data
Figure .221
* The Mean Types:-
Arithmetic Geometric Harmonic
Figure .222
32. When We Use Each Of The Central Tendency Measures???
Figure .223
33. Measure By Dispersion :-
Range - (minimum, maximum)
Variance and Standard deviation
xi x
1 2
- Variance =
n 1 n
- Standard deviation ( x ) = Variance (Measure of spread)
- Standard error = n
o Causes of not knowing things precisely
Figure .224
34. Probability & Cumulative Density Functions:-
The Sample Space:-
The space of all possible outcomes of a given process or situation is called the sample
space S
Figure .225
An event:-
An event A is a subset of the sample space.
Figure .226
The Laws of Probability:-
The probability of the sample space S is 1, P(S) = 1
The probability of any event A is such that
0 <= P (A) <= 1.
Law of Addition
If A and B are mutually exclusive events, then
P (A or B) = P (A) + P (B)
If A and B are not mutually exclusive:
P (A or B) = P (A) + P (B) – P (A and B)
Union:-
Elements in at least one of the two sets:
AB = { x | x A x B }
Figure .227
35. Intersection:-
Elements in exactly one of the two sets:
Figure .228
Disjoint Sets
DEF: If A and B have no common elements, they are said to be disjoint,
i.e. A B = .(Mutual Exclusive)
Figure .229
Disjoint Union
When A and B are disjoint, the disjoint union operation is well defined. The
circle above the union symbol indicates disjointedness.
Figure .2210
36. Set Difference
Elements in first set but not second:
A-B = { x | x A x B }
Figure .2211
Symmetric Difference
Elements in exactly one of the two sets:
AB = { x | x A x B }
Figure .2212
Complement
Elements not in the set (unary operator):
A = {x | x A}
Figure .2213
37. Conditional Probabilities:-
It means that what is the probability of occurring A if B has been already
happened.
The conditional probability of A given B is
P (A|B) = P (A, B) / P (B)
If A and B are independent then
P (A, B) =P (A)*P (B) P (A|B) =P (A)
In general:
min(P(A),P(B) P(A)*P(B) max(0,1-P(A)-P(B))
For example:-
If P (A) =0.7 and P (B) =0.5 then P (A, B) has to be between 0.2 and
0.5, but not necessarily be 0.35.
Probability Density function:-
a probability function that maps the possible values of x against their respective
probabilities of occurrence, p(x)
P(x) is a number from 0 to 1.0.
p X ( x) P[ X x]
Figure .2214
38. Cumulative Distribution function:-
For a given x, there is a fixed possibility that the random variable will not
exceed certain value x, it is non-decreasing in x
FX ( x) P[ X x]
x1 x2 F ( x1 ) F ( x2 )
Figure .2215
Permutation & Combination:-
Permutation: How many different sets of r objects can be chosen from n objects
prn nn 1n 2...n r 1
n!
prn
n r !
39. Combination: Without regard to order of drawing.
• Number of n things taken r at a time.
crn r!nn r !
n
r
!
Distributions:
Some Examples on Distributions:-
Bernouli Binomial Poisson
Distribution distribution distribution
Negative
Normal
binomial
Distribution
distribution
Figure .2216
Normal distribution:
A continuous random variable X is said to have a normal distribution with parameters
and , where and
0 , if the pdf of X is
1 2 2
f ( x) e ( x ) /(2 ) x
2
40. Figure .2217
Figure .2218
Mean or Expected Value
Variance: The expected value of the square of distance between x and its
mean
41. Coefficient of Variation
Covariance
Measures the strength of the linear relationship between two variables
E[(x x )( y y )]
N
σ xy ( xi x )( yi y ) P( xi , yi )
i 1
cov(X,Y) > 0 X and Y are positively correlated
cov(X,Y) < 0 X and Y are inversely correlated
cov(X,Y) = 0 X and Y are independent
Correlation Coefficient: normalized value of covariance
The correlation always lies between -1 and +1
Joint Probability:-
The joint CDF of X and Y is:
43. Stochastic Process
Is a Series of variables represent a process that goes through time and has some
random component
To model any variable over time, we need an algorithm or formula that tells us how
the variable changes from one period to the next.
We calculate the variable by applying the formula to an initial value to get the second
value, applying it to the second value to get the third, etc.
Start with a deterministic process:
0, 2, 4, 6, 8…
The deterministic process is to add the value of 2 to the previous value., we could
describe this algorithm as:
X t 1 X t 2, X0 0
Stochastic Process is similar to deterministic process, except that they add a chance element
to each change.
A simple example:
Flip a coin.
If (heads) add 1 & If (tails) subtract 1.
Here are the results from my home experiment:
T, H, T, H, H, H, H, T, H, H which produces -1, 0, -1, 0, 1, 2, 3, 2, 3, 4
Coin Toss Process
6
4
Value
2
0
-2 1 2 3 4 5 6 7 8 9 10
Flips
Figure .231
44. So We Can Define stochastic process as an another definition as a collection of random
variables indexed on a set;
Usually the index denotes time.
Continuous-time stochastic process:
Discrete-time stochastic process:
First order to n-order distribution can characterize the stochastic process.
First order:
Second order:
Strict stationary
For all n, k and N
45. Markov Chain
Is an example of mathematical model to model a system
Probability P(t, t+1)
State State
t t+1
Figure .232
Convenient to give transition probabilities in matrix form
As an Example:-
The Following markov chain with a representation on
matrix form of state A, B, C, D
0.95
0.2
0.5
0.2
0.05 0.3
0.8
1
Figure .234 Figure .233
46. Another Example on Markov Chain:-
This is an illustrative example of markov chain for CPU in
which the states & the process are illustrated with their
probability and the corresponding representation using
matrix form.
WAIT
0.99
IDLE LOOP
STATE
S0
USER
SYSTEM 0.01
SUPERVISOR
SUPERVISOR
0.02 0.90
SUPERVISOR 0.02
STATES
S1 S2
0.01
0.92 0.01 0.01
0.04 0.09
PROBLEM
STATE S3
USER
0.98 PROGRAMS
Figure .235
Figure .236
48. Principal Component Analysis ( PCA)
The Principal components method summarizes data by finding the major correlations
in linear combinations of the observations.
Reduce the dimensionality of a data set by finding a new set of variables, smaller than
the original set of variables
—
— PCA is a statistical method to transform the data to a new coordinate system.
* Little information lost in process, usually
Applications
Used Scientifically in Compression & Classification of data in this Application:
◦ Face Recognition
◦ Voice Recognition
◦ Image Compression
◦ Pattern Recognition
◦ Handwriting Analysis
◦ Lip Reading
◦ Marketing
◦ Social Science Researches
◦ And many more other fields.
49. Graphical Model
Figure .241
Complete Example
1- Get Some Data: First we will gather some data that can be represented in 2
dimensions.
Figure .242
50. 2- Substract The mean: we have to subtract the mean from all the data
3- Calculate the covariance matrix
4- Calculate Eigenvector and Eigen values of the covariance matrix
51. 5- Choosing components and forming a feature vector
Figure .243
We then choose the eigenvector with the highest eigenvalue
6- Deriving the new dataset
52. Final Data = Feature Vector x Data Adjusted.
Figure .244
Shown Example by using Matlab Function:-
Figure .245
55. Definition Of Dependability:-
• Is a value showing the reliability of a person to others because of his/her integrity,
truthfulness, and trustfulness, traits that can encourage someone to depend on
him/her.
• The collective term used to describe the availability performance and its influencing
factors: reliability performance, maintainability performance and maintenance
support performance. [Belcher][1]
• Is the system property that integrates such attributes as reliability, availability,
safety, security, survivability, maintainability.
Performance Concept Diagram:-
This Diagram illustrate the relation between the Quality Of Service (QOS) &
The Dependability (which depend on Availability, Reliability, Maintainability)
Figure .311
56. Dependability and Survivability are the same as shown that The
Goals of Each others are common:
Dependability Goal
1) Ability to deliver service that can justifiably be trusted
2) Ability of a system to avoid failures that are more frequent or more severe, and
outage durations that are longer, than is acceptable to the user(s)
Survivability Goal
Capability of a system to fulfill its mission in a timely manner
Also As we will see in the threats we will find that Dependability &
survivability has same meaning also:
Dependability Threats:
1) Design faults (e.g., software flaws, hardware errata, malicious logics)
2) Physical faults (e.g., production defects, physical deterioration)
3) Interaction faults (e.g., physical interference, input mistakes, attacks, including
viruses, worms, intrusions)
Survivability Threats:
1) Attacks (e.g., intrusions, probes, denials of service)
2) Failures (internally generated events due to, e.g., software design errors, hardware
degradation, human errors, corrupted data)
3) Accidents (externally generated events such as natural disasters)
58. Dependability can be thought of as being composed of three elements:-
Attributes
•A way to asses (to measure) the Dependability of a system
Threats
•An understanding of the things that can affect the Dependability of a system
Means
•Ways to increase the Dependability of a system prevention, fault tolerance, fault
removal and fault forecasting.
Figure .321
Collecting Together As A Tree Called (Dependability Tree) :-
Figure .322
59. Attributes Of Dependability:-
Attributes are the qualities of a system. Which can be assessed to determine
its overall dependability using Qualitative or Quantitative measures.
The following is The Dependability Attributes:-
Availability
• readiness for correct service.
Reliability
• continuity of correct service.
Safety
• absence of catastrophic consequences on the user(s) and the environment .
Integrity
• absence of improper system alteration
Maintainability
• ability to undergo modifications and repairs .
Confidentiality
• i.e. the absence of unauthorized disclosure of information
Figure .323
60. Availability:-
Will be up and running and able to deliver useful services at
any given time?
The availability of a system is the probability that it.
Reliability:-
The reliability of a system is the probability, over a given
period of time, that the system will correctly deliver services
as expected by the user.
continuity of correct service
Safety:-
The safety of a system is a judgment of how likely it is that
the system will cause damage to people or its environment?
Absence of catastrophic consequences on the user(s) and the
environment
Confidentiality-
Absence of unauthorized disclosure of information.
Integrity:-
absence of improper system alteration
Integrity is a pre-requisite for availability, reliability and
safety
Maintainability:-
ability to undergo modifications and repairs
61. Threats Of Dependability:-
Are things that can affect a system and cause a drop in Dependability
There are three main terms that must be clearly understood:
Figure .324
62. Fault: A fault is a defect in a system. The presence of a fault in a
system may or may not lead to a failure, for instance although a
system may contain a fault its input and state conditions may never
cause this fault to be executed so that an error occurs and thus
never exhibits as a failure.
Fault
Activation
Error
Figure .325
* Activation of Fault Leads to Error
63. Error: An error is a discrepancy between the intended behavior of
a system and its actual behavior inside the system boundary. Errors
occur at runtime when some part of the system enters an
unexpected state due to the activation of a fault. Since errors are
generated from invalid states they are hard to observe without
special mechanisms, such as debuggers or debug output to logs.
1 2 3 4
Fault Activated
Error
Observer
Figure .326
* Assume (1, 2, 3&4) is The Processes of the System.
* If a Fault has happened (Activated) The Process will go to the
Error State (invalid State).
* An Observer inside the Boundary of the System (e.g: Debugger)
64. Failure: A failure is an instance in time when a system displays
behavior that is contrary to its specification. An error may not
necessarily cause a failure, for instance an exception may be
thrown by a system but this may be caught and handled using fault
tolerance techniques so the overall operation of the system will
conform to the specification.
Error
Propagate
Failure
Figure .327
* When the Error propagate it will causes Failure
65. It is important to note that Failures are recorded at the system boundary.
They are basically Errors that have propagated to the system boundary
and have become observable. Faults, Errors and Failures operate
according to a mechanism. This mechanism is sometimes known as a
Fault-Error-Failure chain. As a general rule a fault, when activated, can
lead to an error (which is an invalid state) and the invalid state generated
by an error may lead to another error or a failure (which is an observable
deviation from the specified behavior at the system boundary).
Once a fault is activated an error is created. An error may act in the same
way as a fault in that it can create further error conditions, therefore an
error may propagate multiple times within a system boundary without
causing an observable failure. If an error propagates outside the system
boundary a failure is said to occur.
* A failure is basically the point at which it can be said that a service is
failing to meet its specification. Since the output data from one service
may be fed into another, a failure in one service may propagate into
another service as a fault so a chain can be formed of the form: Fault
leading to Error leading to Failure leading to Error, etc.
Figure .328
66. Means Of Dependability:-
Since the mechanism of a Fault-Error-Chain is understood, it is possible to
construct means to break these chains and thereby increase the dependability of a
system.
Four means have been identified so far:
Fault Removal
• How Can Be Removed?
Fault Preventation
• How Can We Prevent?
Fault Forecasting
• How Can We Forecast?
Fault Tolerance
• How Can We Tolerant?
Figure .329
67. Fault Removal: - can be sub-divided into two sub-categories:
Removal During Development
Removal During Use.
-Removal during development: requires verification so that
faults can be detected and removed before a system is put
into production. Once systems have been put into production
a system is needed to record failures and remove them via a
maintenance cycle.
-Removal during Use: happen after system put into
production.
Fault Prevention: - deals with preventing faults being
incorporated into a system. This can be accomplished by use of
development methodologies and good implementation techniques.
Fault Forecasting: - predicts likely faults so that they can be
removed or their effects can be circumvented.
Fault Tolerance: - deals with putting mechanisms in place that
will allow a system to still deliver the required service in the
presence of faults, although that service may be at a degraded level.
*Dependability means are intended to reduce the number of failures
presented to the user of a system. Failures are traditionally recorded over
time and it is useful to understand how their frequency is measured so
that the effectiveness of means can be assessed.
69. Fault Classes:-
Represented as follow:-
Figure .331
Persistence Domain:-
• Transient fault:
– E.g. hardware components which have an adverse reaction to radioactivity.
• Permanent fault:
– E.g., a broken wire or a software design error.
• Intermittent fault:
– E.g. a hardware component that is heat sensitive, it works for a time, stops
working, cools down and then starts to work again.
70. Phenomenological Cause
• physical faults
- Which are due to adverse physical phenomena,
• human-made faults
- Which result from human imperfections.
Nature of fault
• accidental faults
- Which appear or are created fortuitously;
• intentional faults
- Which are created deliberately, with or without a malicious intention.
Phase of creation
• Development faults
- Which result from imperfections arising either
a) During the development of the system (from requirement
specification to implementation) or during subsequent modifications
b) During the establishment of the procedures for operating or
maintaining the system
• Operational faults
- Which appear during the system’s exploitation.
System boundaries
• internal faults
- Which are those parts of the state of a system which, when invoked by the
computation activity, will produce an error,
71. • external faults
- Which result from interference or from interaction with its physical
(Electromagnet perturbations, radiation, temperature, vibration, etc.) Or
human Environment.
Combined Fault
It's Used by Laprie, he used to make the faults classes in which we can represent any
type of faults, in which may faults has many types of classes so he illustrate this faults
by the intersection of the classes with each other
Matrix Representation:-
Figure .332
72. More combinations may be identified in the future. The combined fault classes as
shown belong to three major partially overlapping groupings:
• Development faults that include all fault classes occurring during development.
• Physical faults that include all fault classes that affect hardware.
• Interaction faults that include all external faults.
As An illustrative example on this Diagram as shown in:
Natural Fault: The fault caused by nature must be hardware fault (physical fault), it can't be
software fault.
Human-made Fault: The fault caused by human-made may be software, hardware or
external
------------------------------------------------------
Error:
An error is detected if its presence is indicated by an error message or error
signal. Errors that are present but not detected are latent errors.
Whether or not an error will actually lead to a service failure depends on two
factors:
1. The structure of the system, and especially the nature of any redundancy that
exists in it:
• Protective redundancy, introduced to provide fault tolerance, that is
explicitly intended to prevent an error from leading to service failure;
• Unintentional redundancy (it is in practice difficult if not impossible to
build a system without any form of redundancy) that may have the same
presumably unexpected — result as intentional redundancy.
2. The behavior of the system: the part of the state that contains an error may
never be needed for service, or an error may be eliminated (e.g., when
overwritten) before it leads to a failure.
Error Classifications:
A convenient classification of errors is to describe them in terms of the
elementary service failures that they cause:
o content vs. timing errors
73. o detected vs. latent errors
o consistent vs. inconsistent errors when the service goes to two or more
users
o Minor vs. catastrophic errors. In the field of error control codes
Content errors are further classified according to the damage pattern: single,
double, triple, byte, burst, erasure, arithmetic, track, etc., errors.
Some faults (e.g., a burst of electromagnetic radiation) can simultaneously
cause errors in more than one component. Such errors are called multiple
related errors. Single errors are errors that affect one component only.
-----------------------------------------------------
Failure classes:
Figure .333
Domain Classes
Content (value) failures:
- The content of the information delivered at the service interface (i.e., the
service content) deviates from implementing the system function;
Timing failures:
-The time of arrival or the duration of the information delivered at the service
interface (i.e., the timing of service delivery) deviates from implementing the
system function.
74. Figure .334
Perception by several users
Consistent failures:
- The incorrect service is perceived identically by all system users.
Inconsistent failures:
- Some or all system users perceive differently incorrect service (some users
may actually perceive correct service).
Consequence of environment
Minor failures
- Where the harmful consequences are of similar cost to the benefits provided
by correct service delivery;
Catastrophic failures
- Where the cost of harmful consequences is orders of magnitude, or even
incommensurably, higher than the benefit provided by correct service
delivery.
76. Measuring Dependability Varies by the type of system & What used for,
many types of methods used to measuring dependability, As an example:-
Measuring By Attributes of Dependability ( Reliability, Maintainability,
Availability, safety, Confidentiality …etc)
Measuring Using Fault Tree Analysis Method.
Measuring Using Stochastic Petri-nets Method & Markov Chain.
I will talk in this chapter about some methods & concepts used in Dependability
Measurements.
-------------------------------------------
Some Concepts we use to measure dependability:-
As we learn in the dependability elements is the dependability attribute
which we try to use them to find equations for computing dependability of a
system.
Only Availability and Reliability are quantifiable by direct measurements
whilst others are more subjective.
77. Safety cannot be measured directly via metrics but is a subjective assessment
that requires judgmental information to be applied to give a level of
confidence; while Reliability can be measured as failures over time.
While Reliability can be measured as failures over time.
Reliability = Failure / Time
Applying security measures to the appliances of a system generally improves
the dependability by limiting the number of externally-originated errors.
When Measuring Reliability and Availability Time from an initial instant to
the next failure event Typical measures:
– MTTF: mean time to failure
– MTBF: mean time between failures
– MTTR: mean time to repair
– MFC: mean failure cost
Availability = MTTF / MTBF
Ratio of service time to elapsed time
Computing Mean Time Between Failure:-
MTBF = MTTF + MTTR
As it is usually true that MTTR is a small fraction of MTTF, it is
usually allowed to assume that MTBF ≈ MTTF.
78. Measuring Maintainability which is a function of time representing the
probability that a failed system will be repaired in a time less than or equal to
(t). Which can be estimated as:
M (t) = 1 - exp-μt (Where μ being the repair rate)
Applying security measures to the appliances of a system generally improves
the dependability by limiting the number of externally-originated errors.
Security is the concurrent existence of:-
a) Availability: for authorized users only,
b) Confidentiality
c) Integrity: with ‘improper’ meaning ‘unauthorized’.
Informally, the security of a system is a judgment of how likely it is that
the system can resist accidental or deliberate intrusion.
Measuring Security by :
MTTD (Mean Time to Detection)
MTTE (Mean Time to Exploitation)
79. Fault Tree Analysis Method:-
• Developed in 1962 by Bell Labs
• Using probabilities in analysis:-
- Assignment of probabilities to specific events
- Computation of probabilities for compound events
• Basic Structure Of The Fault Tree is :
And Gate
OR Gate
Basic Event
Compound Event
Transfer
Figure .341
80. Fault Tree Structure As Shown In Figure:-
Figure .342
Fault Tree Calculation As An Example:-
Figure .343
81. An Example Of Analysis Of Dual-Core Computer:-
Figure .344
An Example Of Heart – Pulse Mechanism:-
Figure .345
82. When Trigger Fails: it Fails As The Following Tree:-
Figure .346
* Fault trees can be used to analyze security issues and it called attack
trees
Software Tools Using For Measuring Dependability:
Figure .347
84. Benchmarking:-
Is the act of running a computer program, a set of programs, or other operations, in
order to assess the relative performance of an object, normally by running a number of
standard tests and trials against it. The term 'benchmark' is also mostly utilized for the
purposes of elaborately-designed benchmarking programs themselves. Benchmarking is
usually associated with assessing performance characteristics of the System.
Dependability Benchmarking:-
Is a specification of all elements required to assess certain measures related to
the behavior of a System in the presence of faults.
Is performance benchmarking extended to dependability aspects.
The main elements of a Performance benchmark are:
SUT: System under Test
WL: Workload
PM: Performance Measures
Performance Benchmark Dependability Extensions Dependability Benchmark
Workload Faultload Workload
Faultload
Performance Measures + Dependability Measures =
Performance Measures
Pricing Information Pricing Information Dependability Measures
Pricing Information
Full Disclosure Rules
Full Disclosure Rules
Full Disclosure Rules
Figure .351
85. To extend a performance benchmarking into the dependability domain, a
fault load has to be provided.
The main elements of Dependability benchmark are:
SUB: System under Benchmark
IFL: Interaction Fault Load
DM: Dependability Measures
FMD: Failure Mode Detector
FMC: Failure Mode Classes
WL: Workload
PM: Performance Measures
Figure .352
Some Definitions:-
FMD: Failure Mode Detector:-
Identifies and classifies the failure modes in a
dependability benchmark experiment,
SUB: System under Benchmark:-
Is a system where the measures apply
DBT: Dependability benchmark target:-
The SUB could be larger than the component or subsystem
that the benchmark user wants to characterize
DBE: Dependability Benchmarking Experiments:-
Benchmarking is performing tests on the SUB.
DBC (dependability benchmark configuration):-
Is the implementation of the benchmark elements and the
experimental setup.
86. Workload:
Is the entire load explicitly or implicitly applied to the System
under Benchmarking.
• User Workload
• Operator Load
• Background Load
* The workload must be:
Scope Oriented
Portable Representative
Figure .353
Performance Measures
• tmpC: transactions per minute
• $/ tmpC
• Response Time
Dependability Measures
• Conditional probability of occurrence of failure mode
classes.
• Availability
• Mean Down Time
88. In These Document We Take a Tour around Definition of System
what it means, by different definitions, its structure, patters we know
now what is system science we imagine the word of system we can
distinguish its component, their relations, the interaction with the
environment also we knows the types of models & visualizations we can
distinguish when we use each model to describe something, we knows
the type of feedbacks and the importance of feedbacks, we know more
about thinking process types, also the probability part has a very high
priority in our documentation because this part serves the course well
by learning probability & statistics methods , deterministic & stochastic
process, principal component analysis & how to simplify data , markov
chain also has a big role in my documentation because also it's one of
methods that used for measuring dependability which is my case study
which I illustrate its importance, elements which used for measuring it
which called attributes , threats which used to less its performance like
fault, error & failure ,also its means which used for preventing less of
dependability like fault tolerance, forecasting, removing & preventive ,
in measuring dependability, illustrating some concepts of measuring & a
method like fault tree analysis was so important for the reader to know
a practical method for representing dependability , all of us now knows
the importance of system science & Engineering course, I'm very proud
that I make a documentation to this course that all of my friends can
read and can understand the importance of this course in all practical
fields in our life,
90. Conclusion of This Documentation That I introduced a good topic
which is good for a beginner engineer who has no idea about system
science to know this field which will benefit him much in his life & will
make him has an imagination & measurements more that anybody for
knowing the performance of the system & this will help him for
designing a new better system or maintain this system , I thought that
this value is the most important in this book beside a new knowledge in
a field which is not known enough in our Arab country, So I hope that
this documentation will be good enough to benefit all the engineers &
can found themselves developed in that field , Also another conclusion
that I knew more in this course when I write this documentation & revise
my information that I have been read for a five months, I really benefit a
lot from this course,& I hope that I will continue learning & researches In
this area of science.
92. References
[1] "Processes for Engineering a System", ANSI/EIA-632-1999, ANSI/EIA, 1999
[2] "Standard for Application and Management of the Systems Engineering Process -
Description", IEEE Std 1220-1998, IEEE, 1998.
[3] "Systems and software engineering - System life cycle processes", ISO/IEC, 2008.
[4] "NASA Systems Engineering Handbook", Revision 1, NASA, 2007
*5+ “System engineering principles & practice” , William Sweet - & Kossiakoff
*6+ “System Engineering & Analysis” , Blanchard & Walter 1998.
*7+ “The web of life: a new scientific understanding of living systems” ,Capra (1996).
*8+ “GENERAL SYSTEMATICS” , J.G. Bennett (1963) .
[9] “Introduction to Cybernetics”, W. Ross Ashby (1956).
*10+” A world of systems” , Mario Bunge (1979).
*11+ “What is Ergonomics” , International Ergonomics Association(2008).
[12] "System Engineering", Erik Aslaksin & Rod belcher , 1992
[13] Muller, "System Modeling and Analysis: a Practical Approach", 2009
[14] wikipedia, "Systems_Modeling_Language"
[15] Lecture Notes , "http://www.ict.kth.se/courses/IL2202/Slides/lec-01-intro.pdf"
[16] Periodic Tabe Of Visualization, "http://www.visual-
literacy.org/periodic_table/periodic_table.html"
[17] Frank Bushmann , Regine Meunier , Hans Rohnert , Peter Sommerland ,Michael Stal
:"Asystem of Patterns" ,John Wiley &Sons , 1996
[18] Erich Gamma,Richard Helm , Ralph Johnson , John Vlissides, "Design Patterns" ,
Addison- Wisely , 1995
[19] Patterns, http://hillside.net/patterns/patterns.html
[20] www.tml.tkk.fi/Opinnot/Tik-109.450/1998/niska/
[21] http://www.cmcrossroads.com/bradapp/docs/patterns-intro.html
[22] Wasson, "System Analysis, Design, and Development - Concepts, Principles, and
Practices" - 0471393339
93. [23] Methodology, http://www.merriam-webster.com/dictionary/methodology
[24] Principal Component Analysis,
"http://en.wikipedia.org/wiki/Principal_component_analysis"
[25] Lindsay I Smith, “A tutorial on Principal Components Analysis”, 2002
[26] Jonathon Shlens, “A tutorial on Principal Component Analysis”, April, 2009.
[27] Signals and Systems group, Uppsala Univ., “Instruction for Image Compression using
PCA”, 2005.
[28] M. Mudrova et al., “Principal Component Analysis In Image Processing”.
[29] I.T. Jolliffe, “Principal Component Analysis”, Springer, 2002.
[30] Mendenhall, Beaver , Introduction To Probability & statistics ,2009
[31] Laprie, Randell, & Landwehr, "Basic Concepts and Taxonomy of Dependable and Secure
Computing," IEEE Transactions on Dependable and Secure Computing(2004)
[32] Randell,"Software Dependability: A Personal View", in the Proc of the 25th International
Symposium on Fault-Tolerant Computing(1995)
[33] Laprie. "Dependable Computing and Fault Tolerance: Concepts and terminology”(1985)
[34+ Randell, Laprie “Fundamental Concepts of Dependability”(2001)
[35] Xing, "Dependability Analysis of Hierarchical Systems with Modular Imperfect Coverage"
[36] Baquero, "PETRI NET WORKFLOW MODELING FOR DIGITAL PUBLISHING MEASURING
QUANTITATIVE DEPENDABILITY ATTRIBUTES", 2006
[37] Mikael Asplund, "Lecture Notes: Dependability and fault tolerance"
[38] Robert Brill, "MEADEP and Its Application in Dependability Analysis for A Nuclear Power
Plant Safety System", 1997
[39] Lorenzo Strigini, "Resilience assessment and dependability benchmarking: challenges of
prediction", 2008
[40] Mili, ": Measuring Dependability as a Mean Failure Cost", 2007
[41] Tang, "MEADEP and Its Applications in Evaluating Dependability for Air Traffic Control
Systems" ,1998
[42] Laprie, Avizˇienis, "Fundamental Concepts of Computer System Dependability", 2001
[43] IPLU team, "The dependability of an IP network – what is it?", 2006
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Systems", 1997
[45] Hossam A. Ramadan, "Towards More Comprehensive Measurable Dependability",2008
[46] Laprie, "Basic Concepts and Taxonomy of Dependable and Secure Computing", 2004
[47] Eusgeld, "Introduction to Dependability Metrics",2008
[48] Oliver Tschache , "Dependability Benchmarking of Linux based Systems"
[49] Dependability Management "CONCEPT OF DEPENDABILITY",2009
[50] Sommerville, " Software Engineering: Ch16.Dependability",2000
[51] Performance and Dependability Benchmarking Slides.
[52] IGI Global, "Chapter I: Dependability and Fault-Tolerance: Basic Concepts and
Terminology" , 2009
[53] Knapskog, Sallhammar, "A Framework for Predicting Security and Dependability
Measures in Real-time", 2007
[54] Chaparro, "Measuring quantitative dependability attributes in Digital Publishing using
Petri Net Workflow Modeling",
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(PRA)", 2009
95. Figures
Part(1)
Figure Represent
Figure .121 System Definition Representation
Figure .122 System Pattern Classifications
Figure .123 System Characteristics types
Figure .124 System Life cycle
Figure .125 System Development Life cycle
Figure .131 Modeling Methods
Figure .132 Periodic Table Of Visualization
Figure .133 Category Of Visualization
Figure .134 Example : Physical Modeling
Part(2)
Figure Represent
Figure .221 Measure by Central tendency
Figure .222 The Mean Types
Figure .223 When We Use Each Of The Central Tendency Measures
Figure .224 Causes of not knowing things precisely
Figure .225 Sample space
Figure .226 An Event
Figure .227 Union
Figure .228 Intersection
Figure .229 Disjoint sets
Figure .2210 Disjoint Union
Figure .2211 Set Differences
Figure .2212 Symmetric Differences
Figure .2213 Complement
Figure .2214 Example: Probability density function
Figure .2215 Example: Cumulative Distribution function
Figure .2216 Distribution Types as examples
Figure .2217 Standard Deviation Showing The Mean
Figure .2218 Standard Deviation Showing The sigma
Figure .231 Coin Toss Process
Figure .232 State Transition on markov chain
Figure .233 Example: State transition with weight
Figure .234 Example: Matrix representation
Figure .235 Example2: state transition with weight
Figure .236 Example2: Matrix Representation
Figure .241 PCA: Graphical Model
Figure .242 Example : Data
Figure .243 Example: Choosing Component
Figure .244 Example: New Data
Figure .245 Example: Matlab function
Part(3)
Figure .311 Performance Concept
Figure .321 Dependability Elements
96. Figure .322 Dependability Tree
Figure .323 Dependability Attributes
Figure .324 Dependability threats
Figure .325 Fault – Error Relation
Figure .326 Error State
Figure .327 Error-Failure Relation
Figure .328 Fault-Error-Failure Chain
Figure .329 Dependability Means
Figure .331 Elementary Fault Classes
Figure .332 Combined Fault Matrix Representation
Figure .333 The Failure Classes
Figure .334 Failure With respect to domain mode
Figure .341 Basic Structure of The fault Tree
Figure .342 Fault Tree Structure
Figure .343 Fault Tree Calculations
Figure .344 Example: Analysis Of Dual-Core Computer
Figure .345 Example: Heart Pulse Mechanism
Figure .346 Example: Heart Pulse Mechanism – Trigger Pulse
Figure .347 Software Tools For Measuring Dependability
Figure .351 Dependability Benchmark Elements Extraction
Figure .352 Dependability Benchmark Elements
Figure .353 Workload Essential Elements
