Wu / Buyya Cloud Data Centers and Cost Modeling

A Complete Guide To Planning, Designing and Building a Cloud Data Center
1. Auflage 2015
ISBN: 978-0-12-801688-6
Verlag: Elsevier Science & Techn.
Format: EPUB
Kopierschutz: 6 - ePub Watermark

E-Book, Englisch, 848 Seiten

Cloud Data Centers and Cost Modeling establishes a framework for strategic decision-makers to facilitate the development of cloud data centers. Just as building a house requires a clear understanding of the blueprints, architecture, and costs of the project; building a cloud-based data center requires similar knowledge. The authors take a theoretical and practical approach, starting with the key questions to help uncover needs and clarify project scope. They then demonstrate probability tools to test and support decisions, and provide processes that resolve key issues. After laying a foundation of cloud concepts and definitions, the book addresses data center creation, infrastructure development, cost modeling, and simulations in decision-making, each part building on the previous. In this way the authors bridge technology, management, and infrastructure as a service, in one complete guide to data centers that facilitates educated decision making. - Explains how to balance cloud computing functionality with data center efficiency - Covers key requirements for power management, cooling, server planning, virtualization, and storage management - Describes advanced methods for modeling cloud computing cost including Real Option Theory and Monte Carlo Simulations - Blends theoretical and practical discussions with insights for developers, consultants, and analysts considering data center development

Caesar Wu is a Senior Domain Specialist on Cloud Computing and Data Centers at Telstra, as well as a Principle Research Fellow, at The University of Melbourne, Australia. He has over 18 years' of experience in ICT architecture, solution design, services delivery and operation management, IT data center lifecycle and transformation. For the past five years he has been responsible for cost modeling of all Telstra cloud computing projects, for both enterprise and government clients, and designed and managed eight data centers in Australia. In 2012, Wu supervised three University of Melbourne PhD students in cloud computing strategic investment decision making.

Autoren/Hrsg.

Wu, Caesar

Buyya, Rajkumar

Inhaltsverzeichnis

1;Front Cover;1
2;Cloud Data Centers and Cost Modeling;4
3;Copyright Page;5
4;Contents;6
5;Preface;18
5.1;Organisation of the Book;21
6;Acknowledgments;22
7;I. Cloud Computing Foundations and Business Requirements;24
7.1;1 Cloud Computing;26
7.1.1;1.1 Introduction;26
7.1.1.1;1.1.1 Operation Cost Rationalization;26
7.1.1.2;1.1.2 Revenue Estimation for Emerging Products;28
7.1.2;1.2 Cloud Computing at a Glance;30
7.1.3;1.3 Right Approach to Definition;31
7.1.4;1.4 A Brief History of Cloud Computing Definitions;32
7.1.5;1.5 Parallel Computing;39
7.1.5.1;1.5.1 Hardware Parallelism;40
7.1.5.1.1;1.5.1.1 Processor parallelism;40
7.1.5.1.2;1.5.1.2 Memory parallelism;40
7.1.5.2;1.5.2 Software Parallelism;41
7.1.5.2.1;1.5.2.1 Algorithm parallelism;41
7.1.5.2.2;1.5.2.2 Programming parallelism;42
7.1.5.2.3;1.5.2.3 Data parallelism;42
7.1.5.2.4;1.5.2.4 Architecture balance parallelism;42
7.1.5.3;1.5.3 Different Types of Parallel Models;43
7.1.6;1.6 Distributed Computing;47
7.1.7;1.7 Grid Computing;48
7.1.8;1.8 Utility Computing;50
7.1.9;1.9 Cloud Computing;53
7.1.10;1.10 Summary;62
7.1.10.1;1.10.1 Software (Applications);62
7.1.10.2;1.10.2 IT Infrastructure (Hardware);63
7.1.11;1.11 Review Questions;64
7.2;2 Business Needs;66
7.2.1;2.1 Introduction;66
7.2.2;2.2 Project Contents and Processes;71
7.2.3;2.3 Allocate the Right People for the Right Job;72
7.2.4;2.4 Business Analyst Role;74
7.2.5;2.5 Defining Business;80
7.2.6;2.6 Business Variables;82
7.2.6.1;2.6.1 Business Entity;82
7.2.6.2;2.6.2 Business Strategy;83
7.2.6.3;2.6.3 Business Profile (Variety);85
7.2.6.4;2.6.4 Business Size (Volume);85
7.2.6.5;2.6.5 Business Variation;87
7.2.7;2.7 Classification of Business Requirements;88
7.2.7.1;2.7.1 Business Requirements;89
7.2.7.2;2.7.2 Stakeholder requirements;89
7.2.7.3;2.7.3 Solution Requirements;89
7.2.7.3.1;2.7.3.1 Functional requirements;91
7.2.7.3.2;2.7.3.2 Nonfunctional requirements;91
7.2.7.4;2.7.4 Transition Requirements;91
7.2.8;2.8 E2E Process of Business Problem Solving;91
7.2.8.1;2.8.1 Business Problem Definition;94
7.2.8.1.1;2.8.1.1 Preliminary definition;95
7.2.8.1.2;2.8.1.2 Analysis process;96
7.2.8.1.3;2.8.1.3 Confirmation and documentation of the real problem;97
7.2.8.1.4;2.8.1.4 Challenges of problem definition;99
7.2.8.1.4.1;2.8.1.4.1 Barking up the wrong tree;99
7.2.8.1.4.2;2.8.1.4.2 Solution side effects;99
7.2.8.1.4.3;2.8.1.4.3 Complex problems;99
7.2.8.1.4.4;2.8.1.4.4 Hidden or avoided problems;100
7.2.8.1.4.5;2.8.1.4.5 Sensitive problems;100
7.2.8.1.4.6;2.8.1.4.6 Presenting the wrong information;100
7.2.8.1.4.7;2.8.1.4.7 No single solution for the problem;100
7.2.8.2;2.8.2 Goals of Defining Business Problems;100
7.2.8.3;2.8.3 Techniques for Identifying Real Problems;101
7.2.8.4;2.8.4 Business Requirements Gathering Phase;101
7.2.8.4.1;2.8.4.1 Preparation;102
7.2.8.4.2;2.8.4.2 Conducting eliciting;103
7.2.8.4.3;2.8.4.3 Documenting;103
7.2.8.4.4;2.8.4.4 Updating;104
7.2.8.5;2.8.5 Provide the Right Solution;104
7.2.8.5.1;2.8.5.1 Information processing;105
7.2.8.5.1.1;2.8.5.1.1 Information classification;105
7.2.8.5.1.2;2.8.5.1.2 Information prioritization;105
7.2.8.5.1.3;2.8.5.1.3 Current process analysis;107
7.2.8.5.1.4;2.8.5.1.4 Historic event analysis;107
7.2.8.5.2;2.8.5.2 Modeling process;107
7.2.8.5.2.1;2.8.5.2.1 Assumptions;108
7.2.8.5.2.2;2.8.5.2.2 Data modeling;108
7.2.8.5.2.3;2.8.5.2.3 Process modeling;110
7.2.8.5.2.4;2.8.5.2.4 Behavior modeling;110
7.2.8.5.3;2.8.5.3 Solution process;111
7.2.8.5.3.1;2.8.5.3.1 Solution assessment;111
7.2.8.5.3.2;2.8.5.3.2 Gap analysis;111
7.2.8.5.3.3;2.8.5.3.3 Determining the best solution;112
7.2.8.5.3.4;2.8.5.3.4 Understanding the constraints of the solution;113
7.2.8.5.4;2.8.5.4 Communication process;113
7.2.8.5.4.1;2.8.5.4.1 Presentation and walking through the solution;113
7.2.8.5.4.2;2.8.5.4.2 Interpretation;113
7.2.8.5.4.3;2.8.5.4.3 Confirmation;114
7.2.8.5.4.4;2.8.5.4.4 Confirmation upgrading;114
7.2.9;2.9 Managing Expectations;114
7.2.10;2.10 Summary;117
7.2.11;2.11 Review Questions;118
7.3;3 Identifying Business Problems: A Case Study;120
7.3.1;3.1 Case Information Briefing;120
7.3.1.1;3.1.1 Servers;122
7.3.1.1.1;3.1.1.1 x86 servers: HP;122
7.3.1.1.2;3.1.1.2 RISC servers: Oracle/Sun E25K;122
7.3.1.1.2.1;3.1.1.2.1 E25K RISC server details;122
7.3.1.1.2.2;3.1.1.2.2 Maintenance and support requirements for E25K;124
7.3.1.1.2.3;3.1.1.2.3 Space requirements for E25K frame;126
7.3.1.1.2.4;3.1.1.2.4 Power and cooling requirements of E25K;126
7.3.1.1.2.5;3.1.1.2.5 Application requirements of E25K;129
7.3.1.1.3;3.1.1.3 Service contract for all RISC servers;129
7.3.1.2;3.1.2 Storage;132
7.3.1.2.1;3.1.2.1 NAS;132
7.3.1.2.2;3.1.2.2 SAN;132
7.3.1.3;3.1.3 Storage Switches;134
7.3.2;3.2 Define the Problems;136
7.3.2.1;3.2.1 Elicit Multiple Issues;139
7.3.2.1.1;3.2.1.1 Decision making;139
7.3.2.1.1.1;3.2.1.1.1 Decision motivation;140
7.3.2.1.1.2;3.2.1.1.2 Decision information and knowledge;141
7.3.2.1.1.3;3.2.1.1.3 Decision process;142
7.3.2.1.2;3.2.1.2 Cost transparency issue;146
7.3.2.1.3;3.2.1.3 Application migration issue;146
7.3.2.2;3.2.2 IT Asset Operation Practice;147
7.3.2.2.1;3.2.2.1 Horizontal brick wall effects;147
7.3.2.2.2;3.2.2.2 Vertical filtering effect;148
7.3.2.3;3.2.3 IT Operational Structure;150
7.3.2.3.1;3.2.3.1 Too many management layers;150
7.3.2.3.2;3.2.3.2 Too many IT organization changes;151
7.3.2.4;3.2.4 Misguided Incentive System;151
7.3.2.4.1;3.2.4.1 Wrong reason for promotion;152
7.3.2.4.2;3.2.4.2 IT contractors managing permanent employees;152
7.3.2.4.3;3.2.4.3 Salary bottleneck;153
7.3.3;3.3 Requirements;153
7.3.3.1;3.3.1 Business Application Requirements;155
7.3.3.2;3.3.2 Architecture Requirements;158
7.3.3.3;3.3.3 Operational Requirements;160
7.3.3.3.1;3.3.3.1 Shared infrastructure requirements (constraints);160
7.3.3.3.2;3.3.3.2 System integration or transition requirements;160
7.3.3.3.3;3.3.3.3 Service monitoring requirements;160
7.3.3.3.4;3.3.3.4 Service maintenance and support requirements;161
7.3.3.4;3.3.4 Vendor Requirements;161
7.3.3.5;3.3.5 Other Stakeholder Requirements;164
7.3.3.6;3.3.6 Identify Hidden Requirements;164
7.3.4;3.4 Solution;164
7.3.4.1;3.4.1 Organizational Perspective;164
7.3.4.2;3.4.2 Technical Perspective;166
7.3.4.2.1;3.4.2.1 Problem statement;168
7.3.4.2.2;3.4.2.2 ICT’s IT strategy or business requirements;168
7.3.4.2.3;3.4.2.3 Assumptions;168
7.3.4.2.4;3.4.2.4 Proposed interim solution;168
7.3.4.2.5;3.4.2.5 Issues with the proposed solution;169
7.3.5;3.5 Summary;171
7.3.6;3.6 Review Questions;173
8;II. Data Center Facilities and Cost;174
8.1;4 Data Center Facilities;176
8.1.1;4.1 Basic Understanding of a Data Center;176
8.1.1.1;4.1.1 Definition of Data Center;176
8.1.1.2;4.1.2 Data Center Architecture;179
8.1.2;4.2 Data Center Capacity Planning;180
8.1.2.1;4.2.1 Data Center Site Selection;185
8.1.2.1.1;4.2.1.1 The environment;187
8.1.2.1.2;4.2.1.2 The power;189
8.1.2.1.3;4.2.1.3 The payload and IT workload;191
8.1.2.1.4;4.2.1.4 The policy;191
8.1.2.1.5;4.2.1.5 The human factor;191
8.1.2.1.6;4.2.1.6 The network;191
8.1.2.2;4.2.2 Data Center Performance;195
8.1.2.2.1;4.2.2.1 Site availability;196
8.1.2.2.2;4.2.2.2 Problem response and resolution time;197
8.1.2.2.3;4.2.2.3 Scalability;197
8.1.2.2.4;4.2.2.4 Utilization;198
8.1.2.2.5;4.2.2.5 Latency and throughput;198
8.1.2.3;4.2.3 Data Center Resource Celling;201
8.1.3;4.3 Data Center Space;203
8.1.3.1;4.3.1 Five Types of Space;204
8.1.3.1.1;4.3.1.1 Total space (building shell);204
8.1.3.1.2;4.3.1.2 Total adjacent lot size (raw lot size);206
8.1.3.1.3;4.3.1.3 Whitespace (raised floor);206
8.1.3.1.4;4.3.1.4 Effective usable space (rack space);207
8.1.3.1.5;4.3.1.5 General space;207
8.1.3.2;4.3.2 Data Center Functional Rooms;208
8.1.3.2.1;4.3.2.1 Utility support functions;209
8.1.3.2.1.1;4.3.2.1.1 Mechanical rooms;209
8.1.3.2.1.2;4.3.2.1.2 Electrical rooms;209
8.1.3.2.1.3;4.3.2.1.3 Staging area;209
8.1.3.2.2;4.3.2.2 Computing functions;210
8.1.3.2.2.1;4.3.2.2.1 Entrance rooms;210
8.1.3.2.2.2;4.3.2.2.2 Computer rooms;210
8.1.3.2.2.3;4.3.2.2.3 Telecommunication rooms;211
8.1.3.2.3;4.3.2.3 Operational functions;211
8.1.3.2.3.1;4.3.2.3.1 Network operation rooms;211
8.1.3.2.3.2;4.3.2.3.2 Common area;211
8.1.3.2.3.3;4.3.2.3.3 General office space;211
8.1.4;4.4 How to Estimate Cost of Space;212
8.1.5;4.5 Summary;213
8.1.6;4.6 Review Questions;214
8.2;5 Data Center Power;216
8.2.1;5.1 Introduction;216
8.2.2;5.2 Fundamentals of Power;218
8.2.2.1;5.2.1 Three Basic Power Metrics;218
8.2.2.2;5.2.2 Power Factor for AC Power;219
8.2.3;5.3 Power Panel (Circuit Breaker);221
8.2.3.1;5.3.1 Type of Circuit Breaker and Selection;221
8.2.3.2;5.3.2 Circuit Breaker Coordination;223
8.2.4;5.4 Transfer Switches and Generators;223
8.2.4.1;5.4.1 Static Transfer Switch (STS);225
8.2.4.2;5.4.2 Automatic transfer switch (ATS);225
8.2.4.3;5.4.3 Generator;225
8.2.5;5.5 Uninterruptible Power Supply (UPS);230
8.2.5.1;5.5.1 Different Types of UPS Topologies;233
8.2.5.1.1;5.5.1.1 Standby or offline single UPS topology;234
8.2.5.1.2;5.5.1.2 Line interactive UPS topology;235
8.2.5.1.3;5.5.1.3 Online double conversion;235
8.2.5.1.4;5.5.1.4 Delta conversion topology;235
8.2.5.1.5;5.5.1.5 Rotary UPS topology;236
8.2.6;5.6 How to Select UPS Topologies;236
8.2.6.1;5.6.1 UPS Redundancy and Cost Efficiency;238
8.2.6.1.1;5.6.1.1 Configuration of UPS redundancy;238
8.2.6.1.2;5.6.1.2 Single module system (SMS);239
8.2.6.1.3;5.6.1.3 1+1 redundancy or two module system;239
8.2.6.1.4;5.6.1.4 N+1 redundancy;239
8.2.6.1.5;5.6.1.5 2(N+1) redundancy;241
8.2.6.1.6;5.6.1.6 How to balance UPS availability and cost;242
8.2.7;5.7 UPS Batteries;243
8.2.7.1;5.7.1 Vented (Flooded or Wet Cell) UPS Batteries;243
8.2.7.2;5.7.2 Valve Regulated (VRLA) UPS Batteries;244
8.2.7.3;5.7.3 Modular Battery Cartridge (MBC) UPS Batteries;245
8.2.7.4;5.7.4 Comparison of Three Common UPS Battery Technologies;245
8.2.7.5;5.7.5 Battery Monitoring;245
8.2.8;5.8 Summary;247
8.2.9;5.9 Review Questions;247
8.3;6 Power Distribution Unit and Cabling;250
8.3.1;6.1 Introduction;250
8.3.1.1;6.1.1 Basic PDU;250
8.3.1.2;6.1.2 Metered PDU;251
8.3.1.3;6.1.3 Switched PDU;251
8.3.2;6.2 Rack Power Distribution Unit and Redundancy;251
8.3.3;6.3 Power Feed to 42RU Rack;254
8.3.4;6.4 Data Center Power Cabling Installation;255
8.3.4.1;6.4.1 Transformation of the Data Center;255
8.3.4.2;6.4.2 Under the Floor Cabling;256
8.3.4.3;6.4.3 Overhead Cabling;257
8.3.5;6.5 Power Cable Layout Architectures;257
8.3.5.1;6.5.1 Star Topology Cabling Architecture;257
8.3.5.2;6.5.2 Bus Topology Cabling;258
8.3.6;6.6 Data Center Power Calculation;258
8.3.6.1;6.6.1 Process of Calculating Data Center Power Requirements;260
8.3.7;6.7 Strategies for Power Saving;265
8.3.7.1;6.7.1 Improve Efficiency of UPS or Remove Redundant Power Equipment;265
8.3.7.2;6.7.2 Improve Power Configuration;265
8.3.7.3;6.7.3 Reducing Data Center Capacity;267
8.3.8;6.8 Summary;269
8.3.9;6.9 Review Questions;269
8.4;7 Data Center Cooling;272
8.4.1;7.1 Introduction;272
8.4.2;7.2 Understanding Cooling, Comfort, and Precision Cooling;272
8.4.2.1;7.2.1 Understanding Cooling;272
8.4.2.2;7.2.2 Comfort Cooling;273
8.4.2.3;7.2.3 Precision Cooling;273
8.4.2.4;7.2.4 Issues with Not Using Precision Cooling;274
8.4.2.5;7.2.5 Heat Sources in a Data Center;274
8.4.3;7.3 Temperature, Pressure, and Volume;275
8.4.3.1;7.3.1 Heat;275
8.4.3.2;7.3.2 Temperature;276
8.4.3.2.1;7.3.2.1 Dry-Bulb Temperature (DBT);276
8.4.3.2.2;7.3.2.2 Wet-Bulb Temperature (WBT);277
8.4.3.2.3;7.3.2.3 Dew-Point Temperature (DPT);277
8.4.3.3;7.3.3 Humidity;277
8.4.3.3.1;7.3.3.1 Relative humidity;278
8.4.3.3.2;7.3.3.2 Absolute humidity;278
8.4.3.3.3;7.3.3.3 Humidity ratio;278
8.4.3.4;7.3.4 Relationship between Temperature and Humidity;278
8.4.3.5;7.3.5 The Psychometric Chart (Humidity Chart);280
8.4.3.6;7.3.6 Refrigeration;281
8.4.3.7;7.3.7 Refrigeration Unit;282
8.4.3.8;7.3.8 Refrigeration Cycle;282
8.4.3.8.1;7.3.8.1 Evaporation (state 1);283
8.4.3.8.2;7.3.8.2 Compression (state 2);283
8.4.3.8.3;7.3.8.3 Condensation (state 3);285
8.4.3.8.4;7.3.8.4 Expansion (state 4);285
8.4.3.9;7.3.9 Airflow and Airfow Rate;285
8.4.3.9.1;7.3.9.1 Gas laws;286
8.4.3.9.2;7.3.9.2 Boyle’s law;286
8.4.3.9.3;7.3.9.3 Charles’ law;287
8.4.3.9.4;7.3.9.4 Gay-Lussac’s law;287
8.4.3.10;7.3.10 Fan Types and Fan Laws;287
8.4.3.10.1;7.3.10.1 Axial and propeller fans;288
8.4.3.10.2;7.3.10.2 Centrifugal and radial fans;289
8.4.3.10.3;7.3.10.3 Fan laws;289
8.4.4;7.4 Data Center Cooling Components;290
8.4.4.1;7.4.1 CRAC;290
8.4.4.2;7.4.2 CRAH;290
8.4.4.3;7.4.3 Chiller;290
8.4.4.4;7.4.4 Humidifier and Dehumidifier;290
8.4.5;7.5 Data Center Cooling Control;291
8.4.5.1;7.5.1 Demand Fighting among Different CRAC Units;292
8.4.5.2;7.5.2 Adopting a Dew Point and Avoiding Relative Humidity Control;293
8.4.5.3;7.5.3 How to Control Humidity and Temperature;294
8.4.5.4;7.5.4 Consequences of Under- or Overhumidification;294
8.4.5.5;7.5.5 Managing the Data Center Temperature;295
8.4.5.5.1;7.5.5.1 Rack temperature measurement;296
8.4.5.5.2;7.5.5.2 CRAC temperature measurement;296
8.4.5.5.3;7.5.5.3 ASHRAE thermal guidelines for controlling temperature;298
8.4.5.6;7.5.6 Making Temperature Changes Based on the Heat Transfer Equation;299
8.4.5.7;7.5.7 Five Different Technologies for Removal of Data Center Heat;300
8.4.5.7.1;7.5.7.1 Air cooled DX system (two piece);300
8.4.5.7.2;7.5.7.2 Air-cooled self-contained system (one piece);301
8.4.5.7.3;7.5.7.3 Ceiling mounted system;302
8.4.5.7.4;7.5.7.4 Glycol-cooled system;302
8.4.5.7.5;7.5.7.5 Water-cooled system;302
8.4.5.7.6;7.5.7.6 Chilled water system;303
8.4.6;7.6 Summary;304
8.4.7;7.7 Review Questions;307
8.5;8 Effective Air Distribution in Data Centers;308
8.5.1;8.1 Introduction;308
8.5.2;8.2 Methods of Air Distribution;309
8.5.2.1;8.2.1 Flooded Approach for Hard Floor;309
8.5.2.2;8.2.2 Targeted or Locally Ducted Approach for Hard Floor;310
8.5.2.3;8.2.3 Fully Ducted or Contained Approach for Hard Floor;310
8.5.2.4;8.2.4 Locally Ducted for Supply Air with Hard Floor;311
8.5.2.5;8.2.5 Fully Ducted for Both Supply and Return Air with Hard Floor;312
8.5.2.6;8.2.6 Locally Ducted or Targeted Approach with Raised Floor;313
8.5.2.7;8.2.7 Fully Ducted Return Air with Raised Floor;314
8.5.2.8;8.2.8 Fully Ducted Supply Air with Raised Floor;314
8.5.2.9;8.2.9 Fully Ducted Supply Air and Locally Ducted Return Air with Raised Floor;314
8.5.2.10;8.2.10 Fully Ducted Supply and Return Air with Raised Floor;315
8.5.3;8.3 Guidelines for Air Distribution Methods;316
8.5.4;8.4 Computational Fluid Dynamics (CFD) Analysis;317
8.5.4.1;8.4.1 What Is Data Center CFD Analysis and Simulation?;318
8.5.4.2;8.4.2 The Process of CFD Modeling and Simulation;319
8.5.5;8.5 Data Center Cooling Calculations;321
8.5.5.1;8.5.1 Converting Energy in kW to Tons of Ice Cooling Equivalent;321
8.5.5.2;8.5.2 IT Load Calculations;321
8.5.5.2.1;8.5.2.1 Assumptions;321
8.5.5.2.2;8.5.2.2 Cooling load calculations;322
8.5.5.3;8.5.3 Total Cooling Requirement Calculation;322
8.5.5.3.1;8.5.3.1 UPS heat output calculation;322
8.5.5.3.2;8.5.3.2 PDU heat output calculation;323
8.5.5.3.3;8.5.3.3 Light heat output calculation;323
8.5.5.3.4;8.5.3.4 People heat output;323
8.5.5.3.5;8.5.3.5 Summary of all heat outputs;324
8.5.5.3.6;8.5.3.6 Other consideration for cooling requirements;324
8.5.5.3.7;8.5.3.7 High density blade server cooling considerations;324
8.5.6;8.6 Managing and Optimizing Cooling Systems;326
8.5.6.1;8.6.1 Resolve Easy Issues Immediately to Improve Cooling Efficiency;326
8.5.6.1.1;8.6.1.1 Install blank panel;326
8.5.6.1.2;8.6.1.2 Manage racks and cables properly;326
8.5.6.1.3;8.6.1.3 Optimizing raised floor height for cooling;327
8.5.6.2;8.6.2 Guidelines to Manage Perforated Tiles and Racks;328
8.5.6.2.1;8.6.2.1 Avoid the Venturi effect;330
8.5.6.2.2;8.6.2.2 Avoid the supply short circuit;331
8.5.6.3;8.6.3 Conditional Monitoring for Cooling System;332
8.5.6.4;8.6.4 Handling High-Density Rack Cooling;332
8.5.6.4.1;8.6.4.1 Row-based and rack-based cooling;333
8.5.6.4.2;8.6.4.2 Cold and hot aisle containment;334
8.5.6.4.3;8.6.4.3 Summary of pros and cons of different containment approaches;335
8.5.6.4.4;8.6.4.4 Which one is better?;337
8.5.7;8.7 Summary;338
8.5.8;8.8 Review Questions;338
8.6;9 Cooling Strategy;340
8.6.1;9.1 Cooling Control for Wiring Closets;340
8.6.1.1;9.1.1 Sharing Comfort Cooling System;340
8.6.1.2;9.1.2 Conduction Cooling;341
8.6.1.3;9.1.3 Conduction, Passive, and Fan-Assisted Ventilation;342
8.6.2;9.2 Room-Based Cooling;342
8.6.3;9.3 Row-Based Cooling;343
8.6.4;9.4 Rack-Based Cooling;344
8.6.5;9.5 Comparison of Room-, Row-, and Rack-Based Cooling;344
8.6.5.1;9.5.1 Mixing with Room and Row Based Cooling;345
8.6.5.2;9.5.2 Hot Aisle and Rack Containment for High-Density Zone;347
8.6.5.3;9.5.3 Uncontained;348
8.6.6;9.6 Rack Rear Door–Based Cooling Strategy;348
8.6.7;9.7 Raising the Data Center Temperature;349
8.6.8;9.8 Free Cooling Using Economizers;351
8.6.8.1;9.8.1 Airside Economizer;353
8.6.8.2;9.8.2 Waterside Economizer;353
8.6.9;9.9 Summary;357
8.6.10;9.10 Review Questions;362
8.7;10 Fire Suppression and On-Site Security;364
8.7.1;10.1 Introduction;364
8.7.2;10.2 Issues with Traditional Fire Suppression Systems;365
8.7.3;10.3 Fire Classification and Standards;366
8.7.3.1;10.3.1 Fire Detection;366
8.7.3.1.1;10.3.1.1 Computer room detection;367
8.7.3.1.2;10.3.1.2 Power room detection;367
8.7.3.1.3;10.3.1.3 Fire detection system;368
8.7.4;10.4 Fire Suppression Solution Selection;368
8.7.4.1;10.4.1 Traditional Fire Suppression Solutions;370
8.7.4.1.1;10.4.1.1 Carbon dioxide (CO2) fire suppression;370
8.7.4.1.2;10.4.1.2 Water-based (or water mist) fire suppression;370
8.7.4.1.3;10.4.1.3 Halon;372
8.7.5;10.5 Inert Gases, Halocarbons, and Aerosol;373
8.7.5.1;10.5.1 Inert Gases;373
8.7.5.2;10.5.2 Halocarbons;373
8.7.5.3;10.5.3 Aerosol;373
8.7.5.4;10.5.4 Fluorinated Ketone (Liquid) (Novec 1230);374
8.7.5.5;10.5.5 Most Commonly Used Agents in Today’s Data Center;374
8.7.6;10.6 Fire Suppression System Cost for Data Centers;374
8.7.7;10.7 Summary of Fire Suppression Selection;375
8.7.8;10.8 On-Site or Physical Security;377
8.7.9;10.9 Physical Layers;379
8.7.9.1;10.9.1 Protecting Data Center Perimeters;379
8.7.9.2;10.9.2 Security Envelope;381
8.7.9.3;10.9.3 Access Points and Door Control;381
8.7.9.4;10.9.4 Camera or CCTV Control;382
8.7.9.5;10.9.5 Security Guards;382
8.7.10;10.10 Organizational Layer;383
8.7.10.1;10.10.1 People;383
8.7.10.2;10.10.2 Organizational Structure and Policy;385
8.7.10.3;10.10.3 Security Process;386
8.7.11;10.11 Establishing Physical Security;386
8.7.11.1;10.11.1 Cost Calculations for Physical Security Systems;387
8.7.11.1.1;10.11.1.1 Proportion of data center infrastructure cost;388
8.7.11.1.2;10.11.1.2 Cost per watt per month (opex)+capex;388
8.7.11.1.3;10.11.1.3 Cost per terabytes data storage (opex)+capex;389
8.7.11.1.4;10.11.1.4 Baseline cost plus incremental opex per square meter of computer room;389
8.7.11.2;10.11.2 Summary of physical security;389
8.7.12;10.12 Summary;389
8.7.13;10.13 Review Questions;390
9;III. Cloud Infrastructure and Management;392
9.1;11 Cloud Infrastructure Servers: CISC, RISC, Rack-Mounted, and Blade Servers;394
9.1.1;11.1 Cloud Servers;394
9.1.1.1;11.1.1 A Client/Server Architecture;398
9.1.2;11.2 x86 Server;400
9.1.2.1;11.2.1 CPU;404
9.1.2.1.1;11.2.1.1 Socket;404
9.1.2.1.2;11.2.1.2 Chip;405
9.1.2.1.3;11.2.1.3 Core, multicore, processor, and CPU;405
9.1.2.1.4;11.2.1.4 N-way servers;407
9.1.2.1.5;11.2.1.5 Multithreading and processes;408
9.1.2.1.6;11.2.1.6 Hyperthreading;409
9.1.2.2;11.2.2 Server CPU Cache;410
9.1.2.3;11.2.3 RAM;410
9.1.2.4;11.2.4 NUMA;411
9.1.2.5;11.2.5 Server PCI Cards;413
9.1.2.6;11.2.6 Server Storage;414
9.1.2.7;11.2.7 Server Network;415
9.1.2.8;11.2.8 Server Motherboard;415
9.1.3;11.3 Rack-Mounted Servers and Vendors;415
9.1.4;11.4 Blade Servers;418
9.1.4.1;11.4.1 What Is a Blade Server?;418
9.1.4.2;11.4.2 History of Blade Servers;420
9.1.4.3;11.4.3 Rack vs. Blade Server;424
9.1.5;11.5 RISC Server;425
9.1.5.1;11.5.1 History of RISC Servers;426
9.1.5.2;11.5.2 CISC vs. RISC;427
9.1.5.3;11.5.3 RISC Server Market Share;431
9.1.6;11.6 Oracle/Sun SPARC Servers;432
9.1.6.1;11.6.1 Oracle/Sun M-Series RISC Servers;437
9.1.6.2;11.6.2 Oracle/Sun T-Series RISC Servers;440
9.1.6.3;11.6.3 SPARC Logical Domain and Virtual Machine (VM);441
9.1.7;11.7 Summary;446
9.1.8;11.8 Review Questions;447
9.2;12 Cloud Storage Basics;448
9.2.1;12.1 Storage Hierarchy;448
9.2.1.1;12.1.1 Hard Disk Drive (HDD) Fundamentals;449
9.2.1.1.1;12.1.1.1 HDD physical metrics;450
9.2.1.1.2;12.1.1.2 HDD evolution;453
9.2.1.2;12.1.2 Storage SLA and RAID Architecture;456
9.2.1.2.1;12.1.2.1 The common definition of an SLA;457
9.2.1.2.2;12.1.2.2 RAID techniques;458
9.2.1.2.2.1;12.1.2.2.1 Striping;459
9.2.1.2.2.2;12.1.2.2.2 Mirroring;460
9.2.1.2.2.3;12.1.2.2.3 Parity;460
9.2.1.2.3;12.1.2.3 RAID configurations;460
9.2.1.2.3.1;12.1.2.3.1 RAID-0;460
9.2.1.2.3.2;12.1.2.3.2 RAID-1;461
9.2.1.2.3.3;12.1.2.3.3 RAID-5 (distributed parity with N+1);461
9.2.1.2.3.4;12.1.2.3.4 RAID-6 (distributed parity with double parity redundancy);462
9.2.1.2.3.5;12.1.2.3.5 RAID-10 or RAID-01 (nested RAID-1 and RAID-0 or RAID1+0);462
9.2.1.2.4;12.1.2.4 Comparison of RAID options;463
9.2.1.2.4.1;12.1.2.4.1 Summary of common RAID characteristics, cost and write penalties;464
9.2.1.2.4.2;12.1.2.4.2 RAID options and application IOPS;465
9.2.1.3;12.1.3 Storage LUN;466
9.2.1.3.1;12.1.3.1 LUN capacity expansion;467
9.2.1.3.1.1;12.1.3.1.1 Meta LUN concatenation;467
9.2.1.3.1.2;12.1.3.1.2 Meta LUN striping;468
9.2.1.3.2;12.1.3.2 LUN masking;468
9.2.2;12.2 Solid State Disk or Flash SSD;468
9.2.2.1;12.2.1 What Is an SSD?;471
9.2.2.2;12.2.2 SSD versus HDD;473
9.2.2.3;12.2.3 Total Cost of Ownership of SSD;474
9.2.3;12.3 Storage Topologies and Connections;476
9.2.3.1;12.3.1 Direct Attached Storage (DAS);476
9.2.3.1.1;12.3.1.1 Internal DAS;476
9.2.3.1.2;12.3.1.2 External DAS;476
9.2.3.2;12.3.2 Storage Area Network (SAN);477
9.2.3.3;12.3.3 Network Attached Storage (NAS) and File Storage Protocols;482
9.2.3.3.1;12.3.3.1 The idea of NAS;482
9.2.3.3.2;12.3.3.2 Elements of a NAS device;483
9.2.3.3.2.1;12.3.3.2.1 Special server and network elements;484
9.2.3.3.2.2;12.3.3.2.2 Storage elements;484
9.2.3.3.2.3;12.3.3.2.3 Software elements and file system;485
9.2.3.3.2.4;12.3.3.2.4 Integrated NAS;485
9.2.3.3.2.5;12.3.3.2.5 Gateway NAS;487
9.2.4;12.4 Storage Protocols;487
9.2.4.1;12.4.1 File-Oriented Protocols;487
9.2.4.1.1;12.4.1.1 Server Message Blocks (SMB)/Common Internet File System (CIFS);488
9.2.4.1.2;12.4.1.2 Network File System (NFS);488
9.2.4.2;12.4.2 Block-Oriented Protocols;490
9.2.4.2.1;12.4.2.1 IDE/ATA/parallel ATA or PATA;490
9.2.4.2.2;12.4.2.2 Serial ATA or SATA;491
9.2.4.2.3;12.4.2.3 SCSI;493
9.2.4.2.4;12.4.2.4 ISCSI;495
9.2.4.2.5;12.4.2.5 Fibre Channel Protocol (FCP);497
9.2.4.2.6;12.4.2.6 Fibre Channel IP (FCIP);499
9.2.4.2.7;12.4.2.7 Internet Fibre Channel Protocol (iFCP);500
9.2.4.2.8;12.4.2.8 Fibre Channel over Ethernet (FCoE) Protocol;500
9.2.4.2.8.1;12.4.2.8.1 Converged Network Adapter (CNA);503
9.2.4.2.8.2;12.4.2.8.2 Fibre Channel over Ethernet (FCoE) Switch;503
9.2.4.3;12.4.3 Storage Interface Protocols Summary;503
9.2.5;12.5 Pros and Cons for Different Storage Topologies;506
9.2.6;12.6 Traditional Storage vs. Cloud Storage;509
9.2.7;12.7 Major Storage Vendors and Market Trends;513
9.2.8;12.8 Summary;516
9.2.9;12.9 Review Questions;517
9.3;13 Data Center Networks;520
9.3.1;13.1 Key Network Terms and Components;520
9.3.1.1;13.1.1 Network Hardware;521
9.3.1.1.1;13.1.1.1 Hub;522
9.3.1.1.2;13.1.1.2 Switch;525
9.3.1.1.3;13.1.1.3 Bridge;527
9.3.1.1.4;13.1.1.4 Router;532
9.3.1.1.4.1;13.1.1.4.1 Principles of routing;532
9.3.1.1.4.2;13.1.1.4.2 Router size;534
9.3.1.1.4.3;13.1.1.4.3 Router types;534
9.3.1.1.4.4;13.1.1.4.4 Routing protocols;536
9.3.1.1.5;13.1.1.5 Gateway;538
9.3.2;13.2 Data Center Network Terms and Jargon;539
9.3.2.1;13.2.1 DCN Terms, Jargon, and Definitions;539
9.3.2.1.1;13.2.1.1 Topology;540
9.3.2.1.2;13.2.1.2 Network topology;540
9.3.2.1.3;13.2.1.3 Data center network topology;540
9.3.2.1.4;13.2.1.4 Node;541
9.3.2.1.5;13.2.1.5 Node degree;541
9.3.2.1.6;13.2.1.6 Neighbor nodes;541
9.3.2.1.7;13.2.1.7 Diameter;541
9.3.2.1.8;13.2.1.8 Dimension;541
9.3.2.1.9;13.2.1.9 Radix;541
9.3.2.1.10;13.2.1.10 Regular topology;542
9.3.2.1.11;13.2.1.11 Irregular topology;542
9.3.2.1.12;13.2.1.12 Nonblocking and blocking;542
9.3.2.1.13;13.2.1.13 Direct network;542
9.3.2.1.14;13.2.1.14 Indirect network;542
9.3.3;13.3 Metrics of DCN Topology;543
9.3.4;13.4 Types of Network Topology;544
9.3.4.1;13.4.1 Common DCN Topologies;550
9.3.4.1.1;13.4.1.1 Basic trees;551
9.3.4.1.2;13.4.1.2 Fat tree;552
9.3.4.1.3;13.4.1.3 Commodity switch fabric-based fat tree (Al-Fares);553
9.3.4.1.4;13.4.1.4 Top of Rack (ToR) solution;555
9.3.4.1.5;13.4.1.5 End of Row (EoR) and middle of ROW (MoR) solutions;556
9.3.4.2;13.4.2 Recursive DCN Topologies;558
9.3.4.2.1;13.4.2.1 DCell;558
9.3.4.2.1.1;13.4.2.1.1 Principles of DCell;558
9.3.4.2.1.2;13.4.2.1.2 Structure of DCell;559
9.3.4.2.1.3;13.4.2.1.3 DCell formula;561
9.3.4.2.1.4;13.4.2.1.4 DCell summary;561
9.3.4.2.2;13.4.2.2 BCube;562
9.3.4.2.2.1;13.4.2.2.1 Principles of BCube;562
9.3.4.2.2.2;13.4.2.2.2 Structure of BCube;563
9.3.4.2.2.3;13.4.2.2.3 BCube formula;564
9.3.4.2.2.4;13.4.2.2.4 BCube summary;566
9.3.4.3;13.4.3 Other DCN Topologies;567
9.3.4.3.1;13.4.3.1 Virtual layer 2 (VL2);567
9.3.4.3.1.1;13.4.3.1.1 Principles of VL2;568
9.3.4.3.1.2;13.4.3.1.2 Structure of VL2;570
9.3.4.3.1.3;13.4.3.1.3 Summary of VL2;571
9.3.4.3.2;13.4.3.2 Conventional butterfly and flattened butterfly;572
9.3.4.3.2.1;13.4.3.2.1 Principle of flattened butterfly;573
9.3.4.3.2.2;13.4.3.2.2 Structure of flattened butterfly;574
9.3.4.3.2.3;13.4.3.2.3 Flattened butterfly formula;575
9.3.4.3.2.4;13.4.3.2.4 Flattened butterfly summary;576
9.3.4.3.2.5;13.4.3.2.5 2-Dilated Flattened Butterfly (2DFB);578
9.3.4.3.3;13.4.3.3 Dragonfly topology;578
9.3.4.3.3.1;13.4.3.3.1 Principle of dragonfly solution;580
9.3.4.3.3.2;13.4.3.3.2 Structure of dragonfly;580
9.3.4.3.3.3;13.4.3.3.3 Dragonfly formula;581
9.3.4.3.3.4;13.4.3.3.4 Dragonfly summary;582
9.3.4.4;13.4.4 Characteristics of Different DCN Topologies;583
9.3.5;13.5 Characteristics of Cloud Data Center Network;583
9.3.5.1;13.5.1 Management Network;583
9.3.5.2;13.5.2 Kernel Network;584
9.3.5.3;13.5.3 Virtual Machine Network;588
9.3.5.4;13.5.4 Virtualized Storage Network;588
9.3.5.5;13.5.5 Example of Connection Details;589
9.3.6;13.6 Cloud DCN Summary;593
9.3.6.1;13.6.1 DCN Component Summary;593
9.3.6.2;13.6.2 Terms and Definitions Summary;596
9.3.6.3;13.6.3 Metrics Summary;596
9.3.6.4;13.6.4 DCN Topology Summary;596
9.3.6.5;13.6.5 Cloud DCN;598
9.3.7;13.7 Review Questions;599
10;IV. Cloud Computing Cost Models and Framework;600
10.1;14 Cost Modeling: Terms and Definitions;602
10.1.1;14.1 Concept of Cost Model;603
10.1.1.1;14.1.1 Definition of Cost;603
10.1.1.1.1;14.1.1.1 Tangible costs;604
10.1.1.1.2;14.1.1.2 Intangible costs;604
10.1.1.1.3;14.1.1.3 Cost parameters;604
10.1.1.1.4;14.1.1.4 Sunk cost;606
10.1.1.1.5;14.1.1.5 Direct Variable Cost (DVC);607
10.1.1.1.6;14.1.1.6 Capital Expenditure (Capex);608
10.1.1.1.7;14.1.1.7 Operational Cost or Operational Expenditure (Opex);608
10.1.1.2;14.1.2 Capex and Opex Shift in a Cloud Environment;608
10.1.1.3;14.1.3 Benefits;612
10.1.1.4;14.1.4 Risks and Opportunity;612
10.1.1.5;14.1.5 Definition of Model;613
10.1.1.5.1;14.1.5.1 Objective cost model;616
10.1.1.5.2;14.1.5.2 Subjective cost model;617
10.1.1.6;14.1.6 Model Measurement or Metrics;618
10.1.1.7;14.1.7 Analysis;620
10.1.1.8;14.1.8 Framework and Methodology;621
10.1.1.9;14.1.9 Formulating a Cost Model;622
10.1.2;14.2 Purposes of Cost Modeling for Cloud Computing;623
10.1.2.1;14.2.1 Visualize Abstract Structure of the Complex World;623
10.1.2.2;14.2.2 Organize Concepts, Thoughts, and Ideas;624
10.1.2.3;14.2.3 Communicate with Other People;625
10.1.3;14.3 Challenges of Cloud Cost Modeling;625
10.1.3.1;14.3.1 Not All Factors Are within the Framework;629
10.1.3.2;14.3.2 Limitation of Framework Size;630
10.1.3.3;14.3.3 Objective or Subjective Process of Cost Modeling;630
10.1.3.4;14.3.4 Limitation of Individual Knowledge and Experience;630
10.1.3.5;14.3.5 A Time Stamp on the Model;631
10.1.4;14.4 Summary;631
10.1.5;14.5 Review Questions;632
10.2;15 Cost Model Categories;634
10.2.1;15.1 Review of Cost Models;634
10.2.1.1;15.1.1 The Cost Model of the First CPU;638
10.2.1.2;15.1.2 Recent Cloud Computing Cost Models;639
10.2.1.2.1;15.1.2.1 Hybrid solution for cloud computing cost model;641
10.2.1.2.2;15.1.2.2 Cloud service provider’s cost model;645
10.2.1.2.3;15.1.2.3 Optimizing cost models;646
10.2.1.2.4;15.1.2.4 Cost model using the method of traditional economic mapping;648
10.2.1.2.5;15.1.2.5 Cost model oriented by service level agreement (SLA);648
10.2.1.2.6;15.1.2.6 Cost model from a TCO perspective;652
10.2.1.2.7;15.1.2.7 Computable general equilibrium (CEG) model;655
10.2.2;15.2 Cloud Computing Issues, Impacts, the Right Questions for the Cost Model;655
10.2.2.1;15.2.1 Cloud Service Consumers;656
10.2.2.2;15.2.2 Cloud Service Providers;656
10.2.3;15.3 Cost Models over the Last 50 Years;656
10.2.4;15.4 Common Financial Cost Models;659
10.2.4.1;15.4.1 Accounting Rate of Return (ARR);660
10.2.4.2;15.4.2 Breakeven Point (BEP);661
10.2.4.3;15.4.3 Cost/Benefit and Cost/Benefit Ratio;662
10.2.4.4;15.4.4 Cost of Revenue Model;663
10.2.4.5;15.4.5 Internal Rate of Return (IRR);663
10.2.4.5.1;15.4.5.1 What are the pros and cons of IRR?;664
10.2.4.6;15.4.6 Net Present Value (NPV);664
10.2.4.6.1;15.4.6.1 What are the pros and cons of the NPV model?;665
10.2.4.7;15.4.7 Simple Payback Period (SPP);665
10.2.4.8;15.4.8 Discounted Payback Period;666
10.2.4.9;15.4.9 Profitability index;666
10.2.4.10;15.4.10 Return on Investment (ROI) Model;667
10.2.4.11;15.4.11 Total Cost of Ownership;668
10.2.4.12;15.4.12 TCO/ROI Model;668
10.2.5;15.5 Summary;669
10.2.6;15.6 Review Questions;670
10.3;16 Chargeback;672
10.3.1;16.1 Introduction to Chargebacks;672
10.3.1.1;16.1.1 Understanding Enterprise IT Operations;674
10.3.2;16.2 No IT Cost Allocation;679
10.3.3;16.3 Non-IT-Based Cost Allocation;681
10.3.4;16.4 IT Domain–Based Cost Allocation;681
10.3.4.1;16.4.1 Direct Cost;682
10.3.4.2;16.4.2 Measured Resource Usage;682
10.3.4.3;16.4.3 Subscription-Based Cost Allocation;684
10.3.4.4;16.4.4 High-Level Allocation;684
10.3.4.5;16.4.5 Low-Level Allocation;685
10.3.4.6;16.4.6 Hardware-Based Cost;685
10.3.4.7;16.4.7 Static Capacity–Based Cost;685
10.3.4.8;16.4.8 Ticket-Based Cost;685
10.3.4.9;16.4.9 Peak Level–Based Cost;686
10.3.4.10;16.4.10 Virtual Server– or VM Account–Based Cost;686
10.3.5;16.5 Fee-Based Cost Allocation;687
10.3.5.1;16.5.1 Negotiated Flat Rate;687
10.3.5.2;16.5.2 Tiered Flat Rate;688
10.3.5.3;16.5.3 Transaction Ratio–Based Cost;688
10.3.5.4;16.5.4 Activity-Based Cost;689
10.3.5.5;16.5.5 SLA Performance Metrics;690
10.3.6;16.6 Business-Based Cost Allocation;691
10.3.6.1;16.6.1 Fixed Revenue-Based Cost;691
10.3.6.2;16.6.2 Fixed Revenue with Predefined Range;692
10.3.6.3;16.6.3 Profit-Oriented Cost Model;692
10.3.6.3.1;16.6.3.1 Capacity reservation–based rate;693
10.3.6.3.2;16.6.3.2 Bidding instance (market base rate);693
10.3.7;16.7 Summary;693
10.3.8;16.8 Review Questions;694
11;V. Cloud Strategy and Critical Decision Making;696
11.1;17 Cost Model Calculation;698
11.1.1;17.1 Case Study;698
11.1.1.1;17.1.1 Company History;698
11.1.1.2;17.1.2 Basic Business Profile;699
11.1.1.3;17.1.3 Current IT Assets and Operation;702
11.1.1.4;17.1.4 Strategic IT Investment Decision Options;702
11.1.1.4.1;17.1.4.1 Data center facility capex;703
11.1.1.4.2;17.1.4.2 IT hardware expenses;704
11.1.1.4.3;17.1.4.3 Software licenses;704
11.1.1.4.4;17.1.4.4 Other implementation costs;704
11.1.1.4.5;17.1.4.5 Building an E2E cost framework;704
11.1.1.5;17.1.5 Model Assumption Details;708
11.1.1.5.1;17.1.5.1 Server workload assumptions;708
11.1.1.5.2;17.1.5.2 Server cost assumptions and vendor selection decision;708
11.1.1.5.3;17.1.5.3 Network cost assumptions;709
11.1.1.5.4;17.1.5.4 Storage cost assumptions;709
11.1.1.5.5;17.1.5.5 Data center facility cost assumptions;711
11.1.1.5.6;17.1.5.6 VMware hypervisor license cost assumptions;712
11.1.1.5.7;17.1.5.7 Operation system and other middleware assumptions;715
11.1.1.5.8;17.1.5.8 Amazon EC2 and S3 cost assumptions;715
11.1.2;17.2 Calculation Steps and Results;716
11.1.2.1;17.2.1 Calculate Growth Rate;718
11.1.2.2;17.2.2 Calculate Dedicated and Virtualized Workload;720
11.1.2.3;17.2.3 Calculate Static Net Present Value (NPV);722
11.1.3;17.3 Conclusion of Case Study;724
11.1.4;17.4 Summary;726
11.1.5;17.5 Review Questions;728
11.2;18 Real Option Theory and Monte Carlo Simulation;730
11.2.1;18.1 Overview of Real Option Theory;730
11.2.2;18.2 History of Real Options;731
11.2.3;18.3 What Are Real Options?;733
11.2.3.1;18.3.1 Equations of Real Option Theory;735
11.2.3.2;18.3.2 Criteria of Real Options from a Project Perspective;735
11.2.3.3;18.3.3 Real Options for Investment Decision;737
11.2.3.3.1;18.3.3.1 Learning option;737
11.2.3.3.2;18.3.3.2 Modular or discrete option;737
11.2.3.3.3;18.3.3.3 Insurance option;738
11.2.3.3.4;18.3.3.4 Irreversible option;738
11.2.3.3.5;18.3.3.5 Flexible option;738
11.2.3.3.6;18.3.3.6 Platform option;738
11.2.4;18.4 Possible Real Options;739
11.2.4.1;18.4.1 Growth or Expansion or Leveraging Option;739
11.2.4.2;18.4.2 Time-to-Build or Open Option;740
11.2.4.3;18.4.3 Multiple Interacting Options;740
11.2.4.4;18.4.4 Option to Switch;740
11.2.4.5;18.4.5 Option to Defer;741
11.2.4.6;18.4.6 Option to Alter the Operating Scale;741
11.2.4.7;18.4.7 Option to Abandon (Put Option);741
11.2.4.8;18.4.8 Different Terms for Real Options;741
11.2.5;18.5 Real Options versus Financial Options;742
11.2.6;18.6 Real Options versus Traditional Approaches;743
11.2.7;18.7 What Is Monte Carlo Simulation (MCS)?;747
11.2.7.1;18.7.1 Probability of Probability (Monte Carlo Tests);749
11.2.7.2;18.7.2 Simulation Process;750
11.2.7.3;18.7.3 Different Types of Monte Carlo Simulation;753
11.2.7.3.1;18.7.3.1 Linear MCS;753
11.2.7.3.2;18.7.3.2 Nonlinear MCS;754
11.2.7.4;18.7.4 Pros and Cons of Monte Carlo Simulation (MCS);755
11.2.7.4.1;18.7.4.1 What is MCS good at? (Pros);755
11.2.7.4.2;18.7.4.2 What is MCS not good at? (Cons);755
11.2.7.4.3;18.7.4.3 Good applications for MCS;756
11.2.7.4.4;18.7.4.4 Bad applications for MCS;757
11.2.8;18.8 Random Numbers and Brownian Motion;758
11.2.8.1;18.8.1 Pseudorandom versus Random Numbers;758
11.2.8.2;18.8.2 Brownian Motion (BM) and Geometric BM;759
11.2.8.2.1;18.8.2.1 Brownian motion;759
11.2.8.2.2;18.8.2.2 Wiener process or standard brownian motion;761
11.2.8.2.2.1;18.8.2.2.1 Levy Processes;761
11.2.8.2.2.2;18.8.2.2.2 Mathematical Terms of Standard Brownian Motion;762
11.2.8.2.2.3;18.8.2.2.3 Brownian Motion with Drift;763
11.2.8.2.2.4;18.8.2.2.4 Geometric Brownian motion;764
11.2.9;18.9 MCS and ROT Process;764
11.2.9.1;18.9.1 Calculation Process;764
11.2.9.2;18.9.2 Tactical Level of Analysis;766
11.2.9.2.1;18.9.2.1 Project prioritization and listing target projects for analysis;766
11.2.9.2.2;18.9.2.2 Static NPV calculation (Traditional Analysis);767
11.2.9.2.3;18.9.2.3 Verifying business criteria;767
11.2.9.2.4;18.9.2.4 Monte carlo simulation for revenue forecasting;767
11.2.9.2.5;18.9.2.5 Checking that everything makes sense and MCS input calibration;768
11.2.9.3;18.9.3 Strategic Level of Analysis;769
11.2.9.3.1;18.9.3.1 Strategic level of real options problem;769
11.2.9.3.2;18.9.3.2 Real option modeling;769
11.2.9.3.3;18.9.3.3 Portfolio and resource optimization;770
11.2.9.3.4;18.9.3.4 Documenting conclusions and recommendations;771
11.2.9.3.5;18.9.3.5 Update and revise;772
11.2.10;18.10 Summary of MCS and ROT Concepts;772
11.2.11;18.11 MCS Analysis Process Details;774
11.2.11.1;18.11.1 Sensitivity Analysis with DCF (Five-Year Revenue);774
11.2.11.1.1;18.11.1.1 Change Discount Cash Flow (DCF) ±10%;775
11.2.11.1.2;18.11.1.2 Change initial capex ±10%;775
11.2.11.1.3;18.11.1.3 Change interest rate ±10%;775
11.2.11.2;18.11.2 Sensitivity Analysis with Different Scenarios;775
11.2.11.3;18.11.3 Monte Carlo Simulation (MCS) Analysis;777
11.2.11.3.1;18.11.3.1 Scenario MCS (Radical Sensitivity);777
11.2.11.3.2;18.11.3.2 Normal case MCS (General Sensitivity);777
11.2.12;18.12 Real Option Theory Analysis Process Details;781
11.2.13;18.13 Real Option Theory Process Equations;782
11.2.13.1;18.13.1 Implement the Real Option Value Calculation Process;783
11.2.13.1.1;18.13.1.1 Step 1: Binomial lattice process;785
11.2.13.1.2;18.13.1.2 Step 2: forward process;785
11.2.13.1.3;18.13.1.3 Step 3: backward induction process;785
11.2.14;18.14 Summary;791
11.2.15;18.15 Review Questions;793
12;Appendices;796
12.1;Appendix A. Catalogue of Major IT Project Catastrophe;796
12.2;Appendix B. An Example of BRD Template;805
12.2.1;B.1 Business Requirements Document Table of Contents;806
12.3;Appendix C. Global Data Center Map (100 Countries and 3236 Data Centers for Colocation in 2014 Based on datacentermap.com);808
12.4;Appendix D. Comparison of Different Cost Models;809
12.5;Appendix E. Nineteen Free Cloud Storage Options (2013 Data);810
12.6;Appendix F. List of Different Cost Model Analysis;811
12.7;Appendix G. Server Products Provided by Major Server Different Vendors;812
12.7.1;G.1 IBM Rack-Mounted Server;812
12.7.2;G.2 Dell Rack-Mounted Server;813
12.7.3;G.3 Lenovo Rack-Mounted Server;813
12.7.4;G.4 Huawei Rack-Mounted Server;813
12.7.5;G.5 Oracle/Sun ×86 Rack-Mounted Server;814
12.7.6;G.6 Fujitsu Rack-Mounted Server;814
12.7.7;G.7 Cisco Rack-Mounted Server;815
12.8;Appendix H. TIA-942 Telecommunication Infrastructure Standard for Data Center Tier;815
13;References;818
14;Index;832

Leseproben

Preface
Caesar Wu and Rajkumar Buyya, Melbourne, Australia, 2014 How can we measure the sky? This question sometimes refers to how to measure the cost of cloud computing. For many people, it is a very challenging and tough question. And yet, many C-class senior executives (CEO, CFO, and CIO), stakeholders, and cloud investors would not only want to know “how” (cost model assumptions and calculations), but also want to know “why” (logic behind these assumptions). Why is this so important? The simple answer is it is too big to be ignored. We have heard many stories about how some decision makers just throw big money into cloud projects without proper understanding of cloud technology and expect to catch up to the “wind” (win). This book will lay out the basic concepts and foundation of cloud computing and data center facilities and then provide tools and practical approaches for decision makers to make the right strategic investment decisions. It will help the decision maker to not only rely on “gut feelings” or previous experiences but also count on the scientific method. One of the goals of this book is to establish a practical framework to enable IT executives to make a rational choice when they are facing a multimillion-dollar investment decision for a cloud project, which is to determine whether IT workloads should stay local or fly to a cloud. (inhouse or cloud computing). Almost five years ago, this challenging task was assigned to us because a senior IT executive wanted to justify a multimillion investment decision that he had already made but he was not sure whether the decision was a rational choice or not. The original idea of this exercise was to check his intuition, estimate the strategic value, communicate with all the stakeholders, and change the scope of the cloud investment project if necessary. At that time, many trial projects of cloud computing, server virtualization, and software multitenancy had just taken off. Various companies made different investment decisions in order to test the water or get a foothold on the cloud market. With these intentions in our mind plus many years’ practical experience in cost modeling of utilities and grid computing, hosting services management, network design, construction, operation, lifecycles, and service delivery, we elicited eight basic questions about this cost modeling exercise: • What is the ultimate goal of measuring the sky? • How many cost models are there? • How can we make a logical and rational comparison with different models? • Why is the TCO/ROI model is so popular? If we use TCO/ROI, would it be the right choice? • What are the assumptions of these models? • How can I select the right model to fit a particular business need? • How can we establish both revenue- and nonrevenue-based cost models? • What are the risks of keeping the IT workload in house versus migrating to the cloud? We believe that most people, whether they are cloud service providers or cloud service consumers, will also face similar questions if they are asked to measure “the sky” or to prepare a business case for a cloud investment project. From this perspective, this book is also targeted for IT business analysts and MBA students as reference material. In essence, the core objective of this book is to demonstrate how to build a cloud cost model. It will illustrate the process of establishing the cost framework and calculating the costs. One of the main reasons to address the cloud cost modeling issue is that many ordinary people have two popular misconceptions: 1. The cloud is free. 2. My data is stored anyway up in the air. If this is so, why should we bother to measure the sky? The answer is dependent on who you are. If you are just an individual consumer and require very limited cloud resources, it is quite clear that you can obtain nearly free cloud resources. However, if you are a business consumer, especially for medium- and large-scale businesses, there will be no free lunch. You have to pay for what you have consumed. This leads to the issue of how to make the rational investment decision for the usage of IT resources. For most small or medium size companies, the investment decision would be relatively simple. The decision criteria could be mainly based on financial or economic returns plus a decision maker’s intuition or personal satisfaction. However, for a large enterprise, the strategic investment decision (very often involving millions of dollars) is not a simple intellectual exercise but rather than process of negotiation and compromise among different Line of Business (LoB) units. However, to some degree, all models are subjective because cost modeling involves many subjective assumptions and selection of raw data and material. It would be impossible to avoid subjective assumptions and personal opinions. Strictly speaking, any data selected and assumption made are subjective. It is based on personal experiences and intuition or perhaps, a gut feeling. Many people think a gut feeling is negative or nonscientific. As a matter of fact, a gut feeling is kind of a super-logic or sixth sense or recognition of a subconscious pattern. It gives us a shortcut to quickly reach a solution. Sometimes, this shortcut serves us quite well, especially if we do not have enough time to analyze the circumstances surrounding us or do not have enough information available. In this case, the sixth sense would be the only choice for us to reach a self-satisfactory conclusion. It is not purely arbitrary or an illogical guess but rather meta–knowledge built upon the subconscious mind. Actually, people’s minds are always searching for a recognised pattern based on available information, knowledge, experiences and most importantly, wisdom. Perhaps that is why a gut feeling is very often called an “educated guess,” self-learning, working experience, or armchair thinking. Many strategic investment decisions made by IT legends such as Steve Jobs and Marc R. Benioff [1] led to great success for their companies. Why did they achieve what most people cannot achieve? Is it because they not only have years of working experiences and cumulative knowledge, but also have “gut feelings” or wisdom? People speculate that they may have absorbed wisdom from Eastern philosophy and religion because they both went to India for enlightenment. In Steve Jobs’ own words, “Trust in destiny” and “Follow your heart.” Walter Isaacson, the exclusive biographer of Steve Jobs, wrote it this way: Jobs’s interest in Eastern spirituality, Hinduism (Krishna/God Consciousness), Zen Buddhism, and the search for enlightenment was not merely the passing phase of a nineteen-year-old. Throughout his life he would seek to follow many of the basic precepts of Eastern religions, such as the emphasis on experiential prajña, wisdom or cognitive understanding that is intuitively experienced through concentration of the mind. Years later, sitting in his Palo Alto garden, he reflected on the lasting influence of his trip to India [2]. For the East, it is the soul. The soul did not come with body nor die with the body. The body is just a temporary home for the soul. The soul can be enlightened by many sophisticated methodologies and practices that have been developed by Eastern philosophy, religion, and culture for many thousands of years or by messages delivered by the Supreme God personally (e.g., Lord Krishna’s teachings compiled as Bhagavad Gita) or his incarnations. For the West, it is subconsciousness. In Sigmund Freud’s teachings, it is the unconscious mind beneath consciousness and awareness. It is a repository of idea, desire, memories, and emotion. It consists of any information and data the mind collects from five senses but cannot consciously process to make meaningful sense of. However, it can be retrieved or recalled to consciousness by the simple direction of attention. In order to make the right decision at the right time, the spiritual mind constantly needs not only information and knowledge but also wisdom. Without that, a strategic decision may just be a tactical one. Long-term success would be dependent on pure luck rather than a strategy. Here, wisdom means abstract pattern recognition at hierarchical level. It is the experience of cumulative knowledge. Cumulative knowledge has four different levels: • Level 1: You do not know what you do not know (ignorance). • Level 2: You know what you do not know (know unknowns). • Level 3: You know what you know and what you do not know (know your boundaries). • Level 4: You know all – knowledge of knowledge or meta-knowledge, wisdom (wizard). For many people and under many circumstances, they are just wandering around atknowledge level 1. If we borrow the Indian philosophy term, it is so-called “ignorance.” There are two different scenarios when people face the unknown. One is either leaving to chance or pretending to know. The other is to wonder about the unknown and continuously search for knowledge and wisdom. That is why people often say wondering is the beginning of wisdom. Unfortunately, we have witnessed many IT strategic decisions made by some wayward people subject to purely static...