Analytical Network and System Administration: Managing Human-Computer Networks

Network and system administration usually refers to the skill of keeping computers and networks running properly.  But in truth, the skill needed is that of managing complexity.  This book describes the science behind these complex systems, independent of the actual operating systems they work on.  It provides a theoretical approach to systems administration that: saves time in performing common system administration tasks. allows safe utilization of untrained and trained help in maintaining mission-critical systems. allows efficient and safe centralized network administration. Managing Human-Computer Networks: Will show how to make informed analyses and decisions about systems, how to diagnose faults and weaknesses Gives advice/guidance as to how to determine optimal policies for system management Includes exercises that illustrate the key points of the book The book provides a unique approach to an old problem and will become a classic for researchers and graduate students in Networking and Computer Science, as well as practicing system managers and system administrators.

Table of 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 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 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 Informationandcontrol 111 9.4 Classification and resolution 111 9.5 Statistical uncertainty and entropy 114 9.6 Propertiesoftheentropy 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 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 ad hoc 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 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 Knowledgeasclassification 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 Stochasticsystemmodels 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 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

About the Author :
Mark Burgess is an Associate Professor at University College in Oslo, Norway,