Analytical Network and System Administration: Managing Human-Computer Networks

368 Pages·2004·2.68 MB·English

by  Mark Burgess(auth.)

Analytical Network and System
Administration
AnalyticalNetworkandSystemAdministration.ManagingHuman–ComputerNetworks MarkBurgess
(cid:1)c 2004JohnWiley&Sons,Ltd ISBN0-470-86100-2
Analytical Network and System
Administration
Managing Human–Computer Networks
Mark Burgess
Oslo University College, Norway
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Contents
Foreword xi
Preface xiv
1 Introduction 1
1.1 What is system administration? . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 What is a system? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.3 What is administration? . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.4 Studying systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.5 What’s in a theory? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.6 How to use the text . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
1.7 Some notation used . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2 Science and its methods 13
2.1 The aim of science . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.2 Causality, superposition and dependency . . . . . . . . . . . . . . . . . . . 16
2.3 Controversies and philosophies of science . . . . . . . . . . . . . . . . . . 17
2.4 Technology. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
2.5 Hypotheses. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
2.6 The science of technology . . . . . . . . . . . . . . . . . . . . . . . . . . 21
2.7 Evaluating a system—dependencies . . . . . . . . . . . . . . . . . . . . . 22
2.8 Abuses of science . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
3 Experiment and observation 25
3.1 Data plots and time series . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
3.2 Constancy of environment during measurement . . . . . . . . . . . . . . . 27
3.3 Experimental design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
3.4 Stochastic (random) variables . . . . . . . . . . . . . . . . . . . . . . . . . 29
3.5 Actual values or characteristic values . . . . . . . . . . . . . . . . . . . . 30
3.6 Observational errors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
3.7 The mean and standard deviation . . . . . . . . . . . . . . . . . . . . . . . 31
3.8 Probability distributions and measurement . . . . . . . . . . . . . . . . . . 32
3.8.1 Scatter and jitter . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
3.8.2 The ‘normal’ distribution . . . . . . . . . . . . . . . . . . . . . . . 35
vi CONTENTS
3.8.3 Standard error of the mean . . . . . . . . . . . . . . . . . . . . . . 36
3.8.4 Other distributions . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
3.9 Uncertainty in general formulae . . . . . . . . . . . . . . . . . . . . . . . 38
3.10 Fourier analysis and periodic behaviour . . . . . . . . . . . . . . . . . . . 39
3.11 Local averaging procedures . . . . . . . . . . . . . . . . . . . . . . . . . . 41
3.12 Reminder. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
4 Simple systems 45
4.1 The concept of a system . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
4.2 Data structures and processes . . . . . . . . . . . . . . . . . . . . . . . . . 46
4.3 Representation of variables . . . . . . . . . . . . . . . . . . . . . . . . . . 47
4.4 The simplest dynamical systems . . . . . . . . . . . . . . . . . . . . . . . 48
4.5 More complex systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
4.6 Freedoms and constraints . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
4.7 Symmetries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
4.8 Algorithms, protocols and standard ‘methods’ . . . . . . . . . . . . . . . . 52
4.9 Currencies and value systems . . . . . . . . . . . . . . . . . . . . . . . . . 53
4.9.1 Energy and power . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
4.9.2 Money . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
4.9.3 Social currency and the notion of responsibility . . . . . . . . . . . 54
4.10 Open and closed systems: the environment . . . . . . . . . . . . . . . . . 56
4.11 Reliable and unreliable systems. . . . . . . . . . . . . . . . . . . . . . . . 58
5 Sets, states and logic 59
5.1 Sets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
5.2 A system as a set of sets . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
5.3 Addresses and mappings . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
5.4 Chains and states . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
5.5 Configurations and macrostates . . . . . . . . . . . . . . . . . . . . . . . . 64
5.6 Continuum approximation . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
5.7 Theory of computation and machine language . . . . . . . . . . . . . . . . 65
5.7.1 Automata or State Machines . . . . . . . . . . . . . . . . . . . . . 66
5.7.2 Operators and operands . . . . . . . . . . . . . . . . . . . . . . . . 68
5.7.3 Pattern matching and operational grammars . . . . . . . . . . . . . 69
5.7.4 Pathway analysis and distributed algorithms . . . . . . . . . . . . . 70
5.8 A policy-defined state . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
6 Diagrammatical representations 73
6.1 Diagrams as systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
6.2 The concept of a graph . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
6.3 Connectivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
6.4 Centrality: maxima and minima in graphs . . . . . . . . . . . . . . . . . . 77
6.5 Ranking in directed graphs . . . . . . . . . . . . . . . . . . . . . . . . . . 80
6.6 Applied diagrammatical methods . . . . . . . . . . . . . . . . . . . . . . . 84
CONTENTS vii
7 System variables 91
7.1 Information systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
7.2 Addresses, labels, keys and other resource locators . . . . . . . . . . . . . 92
7.3 Continuous relationships . . . . . . . . . . . . . . . . . . . . . . . . . . . 94
7.4 Digital comparison . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94
8 Change in systems 97
8.1 Renditions of change . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
8.2 Determinism and predictability . . . . . . . . . . . . . . . . . . . . . . . . 98
8.3 Oscillations and fluctuations . . . . . . . . . . . . . . . . . . . . . . . . . 99
8.4 Rate of change . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102
8.5 Applications of the continuum approximation . . . . . . . . . . . . . . . . 103
8.6 Uncertainty in the continuum approximation . . . . . . . . . . . . . . . . . 105
9 Information 109
9.1 What is information? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109
9.2 Transmission . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110
9.3 Information and control . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111
9.4 Classification and resolution . . . . . . . . . . . . . . . . . . . . . . . . . 111
9.5 Statistical uncertainty and entropy . . . . . . . . . . . . . . . . . . . . . . 114
9.6 Properties of the entropy . . . . . . . . . . . . . . . . . . . . . . . . . . . 118
9.7 Uncertainty in communication . . . . . . . . . . . . . . . . . . . . . . . . 119
9.8 A geometrical interpretation of information . . . . . . . . . . . . . . . . . 123
9.9 Compressibility and size of information . . . . . . . . . . . . . . . . . . . 127
9.10 Information and state . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128
9.11 Maximum entropy principle. . . . . . . . . . . . . . . . . . . . . . . . . . 129
9.12 Fluctuation spectra. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133
10 Stability 135
10.1 Basic notions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135
10.2 Types of stability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135
10.3 Constancy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136
10.4 Convergence of behaviour . . . . . . . . . . . . . . . . . . . . . . . . . . 137
10.5 Maxima and minima . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138
10.6 Regions of stability in a graph . . . . . . . . . . . . . . . . . . . . . . . . 139
10.7 Graph stability under random node removal . . . . . . . . . . . . . . . . . 141
10.8 Dynamical equilibria: compromise . . . . . . . . . . . . . . . . . . . . . . 142
10.9 Statistical stability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143
10.10 Scaling stability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145
10.11 Maximum entropy distributions . . . . . . . . . . . . . . . . . . . . . . . . 148
10.12 Eigenstates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148
10.13 Fixed points of maps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151
10.14 Metastable alternatives and adaptability . . . . . . . . . . . . . . . . . . . 155
10.15 Final remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 156
viii CONTENTS
11 Resource networks 159
11.1 What is a system resource? . . . . . . . . . . . . . . . . . . . . . . . . . . 159
11.2 Representation of resources . . . . . . . . . . . . . . . . . . . . . . . . . . 160
11.3 Resource currency relationships . . . . . . . . . . . . . . . . . . . . . . . 161
11.4 Resource allocation, consumption and conservation . . . . . . . . . . . . . 162
11.5 Where to attach resources? . . . . . . . . . . . . . . . . . . . . . . . . . . 163
11.6 Access to resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165
11.7 Methods of resource allocation . . . . . . . . . . . . . . . . . . . . . . . . 167
11.7.1 Logical regions of systems . . . . . . . . . . . . . . . . . . . . . . 167
11.7.2 Using centrality to identify resource bottlenecks. . . . . . . . . . . 168
11.8 Directed resources: flow asymmetries . . . . . . . . . . . . . . . . . . . . 170
12 Task management and services 173
12.1 Task list scheduling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173
12.2 Deterministic and non-deterministic schedules . . . . . . . . . . . . . . . . 174
12.3 Human–computer scheduling . . . . . . . . . . . . . . . . . . . . . . . . . 176
12.4 Service provision and policy . . . . . . . . . . . . . . . . . . . . . . . . . 176
12.5 Queue processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177
12.6 Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 178
12.7 The prototype queue M/M/1 . . . . . . . . . . . . . . . . . . . . . . . . . 179
12.8 Queue relationships or basic ‘laws’. . . . . . . . . . . . . . . . . . . . . . 181
12.9 Expediting tasks with multiple servers M/M/k . . . . . . . . . . . . . . . . 186
12.10 Maximum entropy input events in periodic systems . . . . . . . . . . . . . 188
12.11 Miscellaneous issues in scheduling . . . . . . . . . . . . . . . . . . . . . . 189
13 System architectures 191
13.1 Policy for organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . 191
13.2 Informative and procedural flows . . . . . . . . . . . . . . . . . . . . . . . 192
13.3 Structured systems and adhoc systems . . . . . . . . . . . . . . . . . . . 193
13.4 Dependence policy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193
13.5 System design strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . 195
13.6 Event-driven systems and functional systems . . . . . . . . . . . . . . . . 200
13.7 The organization of human resources. . . . . . . . . . . . . . . . . . . . . 201
13.8 Principle of minimal dependency . . . . . . . . . . . . . . . . . . . . . . . 202
13.9 Decision-making within a system. . . . . . . . . . . . . . . . . . . . . . . 202
13.9.1 Layered systems: Managers and workers. . . . . . . . . . . . . . . 202
13.9.2 Efficiency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203
13.10 Prediction, verification and their limitations . . . . . . . . . . . . . . . . . 204
13.11 Graphical methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 205
14 System normalization 207
14.1 Dependency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 207
14.2 The database model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 209
14.3 Normalized forms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 210
CONTENTS ix
15 System integrity 215
15.1 System administration as communication? . . . . . . . . . . . . . . . . . . 215
15.2 Extensive or strategic instruction . . . . . . . . . . . . . . . . . . . . . . . 219
15.3 Stochastic semi-groups and martingales . . . . . . . . . . . . . . . . . . . 223
15.4 Characterizing probable or average error . . . . . . . . . . . . . . . . . . . 224
15.5 Correcting errors of propagation . . . . . . . . . . . . . . . . . . . . . . . 226
15.6 Gaussian continuum approximation formula . . . . . . . . . . . . . . . . . 228
16 Policy and maintenance 231
16.1 What is maintenance? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 231
16.2 Average changes in configuration . . . . . . . . . . . . . . . . . . . . . . . 231
16.3 The reason for random fluctuations . . . . . . . . . . . . . . . . . . . . . . 234
16.4 Huge fluctuations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 235
16.5 Equivalent configurations and policy . . . . . . . . . . . . . . . . . . . . . 236
16.6 Policy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 237
16.7 Convergent maintenance . . . . . . . . . . . . . . . . . . . . . . . . . . . 237
16.8 The maintenance theorem . . . . . . . . . . . . . . . . . . . . . . . . . . . 240
16.9 Theory of back-up and error correction . . . . . . . . . . . . . . . . . . . 241
17 Knowledge, learning and training 249
17.1 Information and knowledge . . . . . . . . . . . . . . . . . . . . . . . . . . 250
17.2 Knowledge as classification . . . . . . . . . . . . . . . . . . . . . . . . . . 250
17.3 Bayes’ theorem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 252
17.4 Belief versus truth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 254
17.5 Decisions based on expert knowledge . . . . . . . . . . . . . . . . . . . . 255
17.6 Knowledge out of date . . . . . . . . . . . . . . . . . . . . . . . . . . . . 259
17.7 Convergence of the learning process . . . . . . . . . . . . . . . . . . . . . 260
18 Policy transgressions and fault modelling 263
18.1 Faults and failures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 263
18.2 Deterministic system approximation . . . . . . . . . . . . . . . . . . . . . 265
18.3 Stochastic system models . . . . . . . . . . . . . . . . . . . . . . . . . . . 269
18.4 Approximate information flow reliability . . . . . . . . . . . . . . . . . . . 273
18.5 Fault correction by monitoring and instruction. . . . . . . . . . . . . . . . 275
18.6 Policy maintenance architectures . . . . . . . . . . . . . . . . . . . . . . . 279
18.7 Diagnostic cause trees . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 286
18.8 Probabilistic fault trees . . . . . . . . . . . . . . . . . . . . . . . . . . . . 290
18.8.1 Faults . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 290
18.8.2 Conditions and set logic . . . . . . . . . . . . . . . . . . . . . . . 291
18.8.3 Construction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 293
19 Decision and strategy 295
19.1 Causal analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 295
19.2 Decision-making . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 296
19.3 Game theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 297
x CONTENTS
19.4 The strategic form of a game . . . . . . . . . . . . . . . . . . . . . . . . . 301
19.5 The extensive form of a game . . . . . . . . . . . . . . . . . . . . . . . . 302
19.6 Solving zero-sum games . . . . . . . . . . . . . . . . . . . . . . . . . . . 303
19.7 Dominated strategies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 304
19.8 Nash equilibria. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 305
19.9 A security game . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 309
19.9.1 Zero-sum approximation . . . . . . . . . . . . . . . . . . . . . . . 310
19.9.2 Non-zero sum approximation . . . . . . . . . . . . . . . . . . . . . 313
19.10 The garbage collection game . . . . . . . . . . . . . . . . . . . . . . . . . 315
19.11 A social engineering game . . . . . . . . . . . . . . . . . . . . . . . . . . 321
19.12 Human elements of policy decision . . . . . . . . . . . . . . . . . . . . . 328
19.13 Coda: extensive versus strategic configuration management. . . . . . . . . 328
20 Conclusions 331
A Some Boolean formulae 335
A.1 Conditional probability . . . . . . . . . . . . . . . . . . . . . . . . . . . . 335
A.2 Boolean algebra and logic. . . . . . . . . . . . . . . . . . . . . . . . . . . 336
B Statistical and scaling properties of time-series data 339
B.1 Local averaging procedure . . . . . . . . . . . . . . . . . . . . . . . . . . 339
B.2 Scaling and self-similarity. . . . . . . . . . . . . . . . . . . . . . . . . . . 343
B.3 Scaling of continuous functions. . . . . . . . . . . . . . . . . . . . . . . . 344
C Percolation conditions 347
C.1 Random graph condition . . . . . . . . . . . . . . . . . . . . . . . . . . . 347
C.2 Bi-partite form . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 350
C.3 Small-graph corrections . . . . . . . . . . . . . . . . . . . . . . . . . . . . 351
Bibliography 353
Index 359
Foreword
It is my great honor to introduce a landmark book in the field of network and system
administration. For the first time, in one place, one can study the components of network
and system administration as an evolving and emerging discipline and science, rather than
as a set of recipes, practices or principles. This book represents the step from ‘mastery of
the practice’ and ‘scientific understanding’, a step very similar to that between historical
alchemy and chemistry.
As recentlyas tenyears ago,manypeople considered‘networkandsystemadministra-
tion’tocompriserememberingandfollowingcomplexrecipesforbuildingandmaintaining
systems and networks. The complexity of many of these recipes—and the difficulty of
explainingthemtonon-practitionersinsimpleandunderstandableterms—encouragedprac-
titioners to treat system administration as an ‘art’ or ‘guild craft’ into which practitioners
are initiated through apprenticeship.
Current master practitioners of network and system administration are perhaps best
compared with historical master alchemists at the dawn of chemistry as a science. In
contrastto the distortedpopular image ofalchemyasseekingrichesthrough transmutation
of base metals, historical research portrays alchemists as master practitioners of the subtle
art of combining chemicals towards particular results or ends. Practitioners of alchemy
oftenpossessedbothprecisetechniqueandhighlydevelopedobservationalskills.Likewise,
current master practitioners of network and system administration craft highly reliable
networks from a mix of precise practice, observational skills and the intuition that comes
fromcarefulobservationofnetworkbehaviouroverlongtimeperiods.Butbothalchemists
andmasterpractitionerslackthecommonlanguagethatmakesiteasytoexchangevaluable
information with others: the language of science.
Alas, the alchemy by which we have so far managed our networks is no longer suffi-
cient. When networks were simple in structure, it was possible to maintain them through
the use of relatively straightforward recipes, procedures and practices. In the post-Internet
world, the administrator is now faced with managing and controlling networks that can
dynamically adapt to changing conditions and requirements quickly and, perhaps, even
unpredictably. These adaptive networks can exhibit ‘emergent properties’ that are not
predictable in advance. In concert with adapting networks to serve human needs, future
administratorsmustadaptthemselvestothetaskofmanagementbydevelopinganongoing,
perpetually evolving, and shared understanding.
Inthepast,itwasreasonabletoconsideracomputernetworkasacollectionofcooperat-
ingmachinesfunctioninginisolation.Adaptivenetworkscannotbeanalysedinthisfashion;
their human components must also be considered. Modern networks are not communities
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