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E-raamat: Cloud Computing: Theory and Practice

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(Professor, Computer Science, University of Central Florida, USA)
  • Formaat: EPUB+DRM
  • Ilmumisaeg: 15-Feb-2022
  • Kirjastus: Morgan Kaufmann
  • Keel: eng
  • ISBN-13: 9780323910477
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Cloud Computing: Theory and Practice provides students and IT professionals with an in-depth analysis of the cloud from the ground up. The third edition updates content throughout the book while retaining the popular features and organization of the second edition. After an introduction to network-centric computing and network-centric content in Chapter One, the book is organized into four sections. Section One reviews basic concepts of concurrency and parallel and distributed systems. Section Two presents such critical components of the cloud ecosystem as cloud service providers, cloud access, cloud data storage, and cloud hardware and software. Section Three covers cloud applications and cloud security, while Section Four presents research topics in cloud computing.

Specific topics covered include resource virtualization, resource management and scheduling, and advanced topics like the impact of scale on efficiency, cloud scheduling subject to deadlines, alternative cloud architectures, and vehicular clouds. An included glossary covers terms grouped in several categories, from general to services, virtualization, desirable attributes and security.

  • Includes updated content throughout chapters on concurrency, cloud hardware and software, challenges posed by big data and mobile applications and advanced topics
  • Expanded appendix that presents several cloud computing projects
  • Presents more than 400 references in the text, including recent research results in several areas related to cloud computing
Preface to third edition xiii
Abbreviations xv
Chapter 1 Introduction
1(12)
1.1 Cloud computing, an old idea whose time has come
3(4)
1.2 Energy use and ecological impact of cloud computing
7(1)
1.3 Ethical issues in cloud computing
8(1)
1.4 Factors affecting cloud service availability
9(1)
1.5 Network-centric computing and network-centric content
10(3)
Chapter 2 The cloud ecosystem
13(28)
2.1 Cloud computing delivery models and services
14(4)
2.2 Amazon Web Services
18(7)
2.3 Google Clouds
25(3)
2.4 Microsoft Windows Azure and online services
28(2)
2.5 IBM clouds
30(1)
2.6 Cloud storage diversity and vendor lock-in
30(1)
2.7 Cloud interoperability
31(2)
2.8 Service-level Agreements and Compliance-level Agreements
33(1)
2.9 Responsibility sharing between user and service provider
34(1)
2.10 User challenges and experience
34(2)
2.11 Software licensing
36(1)
2.12 Challenges faced by cloud computing
37(2)
2.13 Cloud computing as a disruptive technology
39(1)
2.14 Exercises and problems
40(1)
Chapter 3 Parallel processing and distributed computing
41(54)
3.1 Computer architecture concepts
42(6)
3.2 Grand architectural complications
48(7)
3.3 ARM architecture
55(3)
3.4 SIMD architectures
58(2)
3.5 Graphics processing units
60(2)
3.6 Tensor processing units
62(3)
3.7 Systems on a chip
65(1)
3.8 Data, thread-level, and task-level parallelism
66(2)
3.9 Speedup, Amdhal's law, and scaled speedup
68(2)
3.10 Multicore processor speedup
70(2)
3.11 From supercomputers to distributed systems
72(2)
3.12 Modularity. Soft modularity versus enforced modularity
74(5)
3.13 Layering and hierarchy
79(3)
3.14 Peer-to-peer systems
82(2)
3.15 Large-scale systems
84(2)
3.16 Composability bounds and scalability (R)
86(2)
3.17 Distributed computing fallacies and the CAP theorem
88(1)
3.18 Blockchain technology and applications
89(2)
3.19 History notes and further readings
91(3)
3.20 Exercises and problems
94(1)
Chapter 4 Cloud hardware and software
95(40)
4.1 Cloud infrastructure challenges
96(2)
4.2 Cloud hardware; warehouse-scale computer (WSC)
98(2)
4.3 WSC performance
100(4)
4.4 Hypervisors
104(1)
4.5 Execution of coarse-grained data-parallel applications
104(2)
4.6 Fine-grained cluster resource sharing in Mesos
106(1)
4.7 Cluster management with Borg
107(3)
4.8 Evolution of a cluster management system
110(1)
4.9 Shared state cluster management
111(2)
4.10 QoS-aware cluster management
113(3)
4.11 Resource isolation
116(4)
4.12 In-memory cluster computing for Big Data
120(8)
4.13 Containers; Docker containers
128(2)
4.14 Kubernetes
130(1)
4.15 Further readings
131(1)
4.16 Exercises and problems
132(3)
Chapter 5 Cloud resource virtualization
135(40)
5.1 Resource virtualization
136(1)
5.2 Performance and security isolation in computer clouds
137(1)
5.3 Virtual machines
138(2)
5.4 Full virtualization and paravirtualization
140(1)
5.5 Hardware support for virtualization
141(3)
5.6 QEMU
144(1)
5.7 Kernel-based Virtual Machine
145(3)
5.8 Xen---a hypervisor based on paravirtualization
148(6)
5.9 Optimization of network virtualization in Xen 2.0
154(2)
5.10 Nested virtualization
156(3)
5.11 A trusted kernel-based virtual machine for ARMv8
159(2)
5.12 Paravirtualization of Itanium architecture
161(3)
5.13 A performance comparison of virtual machines
164(3)
5.14 Open-source software platforms for private clouds
167(2)
5.15 The darker side of virtualization
169(1)
5.16 Virtualization software
170(1)
5.17 History notes and further readings
171(1)
5.18 Exercises and problems
172(3)
Chapter 6 Cloud access and cloud interconnection networks
175(40)
6.1 Packet-switched networks and the Internet
176(5)
6.2 Internet evolution
181(2)
6.3 TCP congestion control
183(2)
6.4 Content-centric networks; named data networks (R)
185(2)
6.5 Software-defined networks; SD-WAN
187(2)
6.6 Interconnection networks for computer clouds
189(4)
6.7 Multistage interconnection networks
193(1)
6.8 InfiniBand and Myrinet
194(3)
6.9 Storage area networks and the Fibre Channel
197(3)
6.10 Scalable data center communication architectures
200(4)
6.11 Network resource management algorithms (R)
204(3)
6.12 Content delivery networks
207(4)
6.13 Vehicular ad hoc networks
211(1)
6.14 Further readings
212(1)
6.15 Exercises and problems
212(3)
Chapter 7 Cloud data storage
215(42)
7.1 Dynamic random access memories and hard disk drives
216(1)
7.2 Solid-state disks
217(3)
7.3 Storage models, file systems, and databases
220(3)
7.4 Distributed file systems; the precursors
223(5)
7.5 General parallel file system
228(3)
7.6 Google file system
231(2)
7.7 Locks; Chubby---a locking service
233(5)
7.8 RDBMS---cloud mismatch
238(1)
7.9 NoSQL databases
239(2)
7.10 Data storage for online transaction processing systems
241(2)
7.11 BigTable
243(2)
7.12 Megastore
245(1)
7.13 Storage reliability at scale
246(4)
7.14 Disk locality versus data locality in computer clouds
250(2)
7.15 Database provenance
252(2)
7.16 History notes and further readings
254(1)
7.17 Exercises and problems
255(2)
Chapter 8 Cloud security
257(36)
8.1 Security---the top concern for cloud users
258(1)
8.2 Cloud security risks
259(5)
8.3 Security as a service (SecaaS)
264(1)
8.4 Privacy and privacy impact assessment
264(3)
8.5 Trust
267(1)
8.6 Cloud data encryption
268(2)
8.7 Security of database services
270(2)
8.8 Operating system security
272(1)
8.9 Virtual machine security
273(2)
8.10 Security of virtualization
275(3)
8.11 Security risks posed by shared images
278(3)
8.12 Security risks posed by a management OS
281(2)
8.13 Xoar---breaking the monolithic design of the TCB
283(3)
8.14 Mobile devices and cloud security
286(1)
8.15 Mitigating cloud vulnerabilities in the age of ransomware
287(2)
8.16 AWS security
289(1)
8.17 Further readings
290(1)
8.18 Exercises and problems
291(2)
Chapter 9 Cloud resource management and scheduling
293(56)
9.1 Policies and mechanisms for resource management
294(2)
9.2 Scheduling algorithms for computer clouds
296(2)
9.3 Delay scheduling (R)
298(5)
9.4 Data-aware scheduling (R)
303(3)
9.5 Apache capacity scheduler
306(1)
9.6 Start-time fair queuing (R)
307(4)
9.7 Borrowed virtual time (R)
311(4)
9.8 Cloud scheduling subject to deadlines (R)
315(5)
9.9 MapReduce application scheduling subject to deadlines (R)
320(2)
9.10 Resource bundling; combinatorial auctions for cloud resources
322(3)
9.11 Cloud resource utilization and energy efficiency
325(3)
9.12 Resource management and dynamic application scaling
328(1)
9.13 Control theory and optimal resource management (R)
329(4)
9.14 Stability of two-level resource allocation strategy (R)
333(1)
9.15 Feedback control based on dynamic thresholds (R)
334(2)
9.16 Coordination of autonomic performance managers (R)
336(2)
9.17 A utility model for cloud-based web services (R)
338(4)
9.18 Cloud self-organization
342(2)
9.19 Cloud interoperability
344(2)
9.20 Further readings
346(1)
9.21 Exercises and problems
346(3)
Chapter 10 Concurrency and cloud computing
349(62)
10.1 Enduring challenges
350(3)
10.2 Communication and concurrency
353(5)
10.3 Computational models; communicating sequential processes
358(2)
10.4 The bulk synchronous parallel model
360(1)
10.5 A model for multicore computing
361(2)
10.6 Modeling concurrency with Petri nets
363(6)
10.7 Process state; global state of a process or thread group
369(5)
10.8 Communication protocols and process coordination
374(2)
10.9 Communication, logical clocks, and message delivery rules
376(5)
10.10 Runs and cuts; causal history
381(4)
10.11 Threads and activity coordination
385(7)
10.12 Critical sections, locks, deadlocks, and atomic actions
392(5)
10.13 Consensus protocols
397(2)
10.14 Load balancing
399(6)
10.15 Multithreading in Java; FlumeJava; Apache Crunch
405(2)
10.16 History notes and further readings
407(1)
10.17 Exercises and problems
408(3)
Chapter 11 Cloud applications
411(42)
11.1 Cloud application development and architectural styles
412(3)
11.2 Coordination of multiple activities
415(4)
11.3 Workflow patterns
419(3)
11.4 Coordination based on a state machine model---zookeeper
422(3)
11.5 MapReduce programming model
425(3)
11.6 Case study: the GrepTheWeb application
428(3)
11.7 Hadoop, Yarn, and Tez
431(4)
11.8 SQL on Hadoop: Pig, Hive, and Impala
435(5)
11.9 Current cloud applications and new applications opportunities
440(2)
11.10 Clouds for science and engineering
442(4)
11.11 Cloud computing and biology research
446(2)
11.12 Social computing, digital content, and cloud computing
448(2)
11.13 Software fault isolation
450(1)
11.14 Further readings
451(1)
11.15 Exercises and problems
452(1)
Chapter 12 Big Data, data streaming, and the mobile cloud
453(48)
12.1 Big Data
454(2)
12.2 Data warehouses and Google databases for Big Data
456(7)
12.3 Dynamic data-driven applications
463(3)
12.4 Data streaming
466(4)
12.5 A dataflow model for data streaming
470(3)
12.6 Joining multiple data streams
473(2)
12.7 Mobile computing and applications
475(3)
12.8 Energy efficiency of mobile computing
478(1)
12.9 Alternative mobile cloud computing models
479(3)
12.10 System availability at scale (R)
482(2)
12.11 Scale and latency (R)
484(4)
12.12 Edge computing and Markov decision processes (R)
488(4)
12.13 Bootstrapping techniques for data analytics (R)
492(3)
12.14 Approximate query processing (R)
495(3)
12.15 Further readings
498(1)
12.16 Exercises and problems
499(2)
Chapter 13 Emerging clouds
501(28)
13.1 A short-term forecast
502(1)
13.2 Machine learning on clouds
503(5)
13.3 Quantum computing on clouds
508(10)
13.4 Vehicular clouds
518(9)
13.5 Final thoughts
527(2)
APPENDIX A Cloud projects
529(42)
A.1 Cloud simulation of a distributed trust algorithm
529(5)
A.2 A trust management service
534(6)
A.3 Simulation of traffic management in a smart city
540(5)
A.4 A cloud service for adaptive data streaming
545(5)
A.5 Optimal FPGA synthesis
550(2)
A.6 Tensor network contraction on AWS
552(7)
A.7 A simulation study of machine-learning scalability
559(2)
A.8 Cloud-based task alert application
561(4)
A.9 Cloud-based health-monitoring application
565(6)
APPENDIX B Cloud application development
571(26)
B.1 AWS EC2 instances
572(3)
B.2 Connecting clients to cloud instances through firewalls
575(2)
B.3 Security rules for application- and transport-layer protocols in EC2
577(4)
B.4 How to launch an EC2 Linux instance and connect to it
581(1)
B.5 How to use S3 in Java
582(3)
B.6 How to manage AWS SQS services in C#
585(1)
B.7 How to install SNS on Ubuntu 10.04
586(2)
B.8 How to create an EC2 placement group and use MPI
588(2)
B.9 StarCluster---a cluster computing toolkit for EC2
590(1)
B.10 An alternative setting of an MPI virtual cluster
590(2)
B.11 How to install hadoop on eclipse on a windows system
592(3)
B.12 Exercises and problems
595(2)
Literature 597(24)
Glossary 621(14)
Index 635
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