2018云计算权威指南：理论与实践第2版

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《云计算-理论与实践》第二版（Cloud Computing-Theory and Practice, 2ed），由Dan C. Marinescu编著，是Morgan Kaufmann出版社于2018年发布的一本权威著作。这本书是云计算领域的经典之作，全面探讨了云计算的核心概念、理论以及实际应用。该书涵盖了以下几个关键知识点： 1. 云计算基础：书中首先介绍了云计算的基本概念，包括其定义、历史发展、主要类型（公有云、私有云、混合云和社区云）以及云计算的主要服务模型（IaaS、PaaS和SaaS）。它阐述了云计算如何通过互联网提供按需、弹性和可扩展的计算资源。 2. 架构和设计原则：作者深入剖析了云计算平台的设计原理，包括数据中心的构建、虚拟化技术（如虚拟机、容器化等）、资源调度和负载均衡，以及安全性措施，如数据加密、身份验证和访问控制。 3. 云服务管理：涵盖了服务级别协议(SLA)、计费模型、监控和优化，以及如何选择合适的云服务提供商，以及迁移和整合既有IT基础设施到云环境的过程。 4. 云安全与隐私：书中着重讨论了云环境中的数据保护、隐私策略以及合规性问题，包括法规遵从性、安全漏洞管理和灾难恢复计划。 5. 案例研究与实践应用：通过真实案例分析，读者可以了解到云计算在不同行业的实际应用，如电子商务、大数据分析、人工智能等，以及遇到的问题和解决方案。 6. 动态更新与变化：鉴于云计算领域的快速发展，书中强调了知识和最佳实践的不断更新，提醒读者随着新技术和行业标准的变化，持续学习和适应是必要的。 7. 版权和许可：最后，书中强调了版权保护，并提供了获取出版商关于版权使用许可的详细信息，确保遵循合法的使用规范。《云计算-理论与实践》第二版是一本实用且深入的参考书籍，不仅适合云计算领域的专业人士，也是对云计算感兴趣的学生和研究人员的重要参考资料。随着科技的进步，阅读此书可以帮助读者保持对云计算最新趋势和技术的理解，提升自身的专业素养。

CHAPTER

INTRODUCTION

Conceptually, computing can be viewed as another utility, like electricity, water, or gas, accessible to

every household in many countries of the world. Computer clouds are the utilities providing computing

services. In utility computing the hardware and the software resources are concentrated in large data

centers. The users of computing services pay as they consume computing, storage, and communica-

tion resources. While utility computing often requires a cloud-like infrastructure, the focus of cloud

computing is on the business model for providing computing services.

More than half a century ago, at the centennial anniversary of MIT, John McCarthy, the 1971 Turing

Award recipient for his work in Artiﬁcial Intelligence, prophetically stated: “If computers of the type I

have advocated become the computers of the future, then computing may someday be organized as a

public utility, just as the telephone system is a public utility... The computer utility could become the

basis of a new and important industry.” The prediction of McCarthy is now a technological and social

reality.

Cloud computing is a disruptive computing paradigm and, as such, it required major changes in

many areas of computer science and computer engineering including data storage, computer archi-

tecture, networking, resource management, scheduling, and last but not least, computer security. The

chapters of this book cover the most signiﬁcant challenges posed by the scale of the cloud infrastructure

and the very large population of cloud users with diverse applications and requirements.

The Internet made cloud computing possible; we could not even dream of using computing and stor-

age resources from distant data centers without fast communication. The evolution of cloud computing

is organically tied to the future of the Internet. The Internet of Things (IoT) has already planted some

of its early seeds in computer clouds. For example, Amazon already offers services such as Lambda

and Kinesis discussed in Section 2.4.

The number of Internet users has increased tenfold from 1999 to 2013; the ﬁrst billion was reached

in 2005, the second in 2010, and the third in 2014. This number is even larger now, see Figure 1.1.

Many Internet users have discovered the appeal of cloud computing either directly or indirectly through

a variety of services, without knowing the role the clouds play in their life. In the years to come the

vast computational resources provided by the cloud infrastructure will be used for the design and

engineering of complex systems, scientiﬁc discovery, education, business, analytics, art, and virtually

all other aspects of human endeavor. Exabytes of data stored in the clouds are streamed, downloaded,

and accessed by millions of cloud users.

This chapter introduces basic cloud computing concepts in Section 1.1. The broader context of

network-centric computing and network-centric content is discussed in Section 1.2. Why cloud com-

puting became a reality in the last years after a long struggle to design large-scale distributed systems

and computational grids? This question is addressed in Section 1.3, while Section 1.4 coveres the deﬁn-

ing attributes of computer clouds and the cloud delivery models. Ethical issues and cloud vulnerability

are discussed in Sections 1.5 and 1.6, respectively.

Cloud Computing. DOI: 10.1016/B978-0-12-812810-7.00001-7

2 CHAPTER 1 INTRODUCTION

FIGURE 1.1

The number of Internet users in different regions of the world as of March 25, 2017 (in millions), according to

http://www.internetworldstats.com/stats.htm.

1.1 CLOUD COMPUTING

In 2011, NIST, the US National Institute of Standards and Technology, deﬁned cloud computing as “a

model for enabling ubiquitous, convenient, on-demand network access to a shared pool of conﬁgurable

computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly

provisioned and released with minimal management effort or service provider interaction.”

Cloud computing is characterized by ﬁve attributes: on-demand self-service, broad network access,

resource pooling, rapid elasticity, and measured service. DBaaS – database as a service is a more recent

addition to the three cloud service delivery models: SaaS – software as a service, PaaS – platform as

a service, and IaaS – infrastructure as a service. Private cloud, community cloud, public cloud, and

hybrid cloud are the four deployment models shown in Figure 1.2, Section 1.4.

Cloud computing era started in 2006 when Amazon offered the Elastic Cloud Computing (EC2)

and the Simple Storage Service (S3), the ﬁrst services provided by Amazon Web Services (AWS).

Five years later, in 2012, EC2 was used by businesses in 200 countries. S3 has surpassed two trillion

objects and routinely runs more than 1.1 million peak requests per second. The Elastic MapReduce

has launched 5.5 million clusters since the start of the service in May 2010 (ZDNet 2013). The range

of services offered by Cloud Service Providers (CSPs), and the number of cloud users have increased

dramatically during the last few years.

The cloud computing movement is motivated by the idea that data processing and storage can be

done more efﬁciently on large farms of computing and storage systems accessible via the Internet.

Computer clouds support a paradigm shift from local to network-centric computing and network-

centric content where distant data centers provide the computing and storage resources. In this new

paradigm users relinquish control of their data and code to Cloud Service Providers.

1.1 CLOUD COMPUTING 3

Cloud computing offers scalable and elastic computing and storage services. The resources used

for these services can be metered and the users can be charged only for the resources they used. Cloud

computing is a business reality as a large number of organizations have adopted this paradigm.

Cloud computing is cost-effective because of resource multiplexing. Application data is stored

closer to the site where it is used in a manner that is device and location-independent; potentially,

this data storage strategy increases reliability, as well as security. The maintenance and the security

are ensured by service providers. Organizations using computer clouds are relieved of supporting large

IT teams, acquiring and maintaining costly hardware and software, and paying large electricity bills.

CSPs can operate more efﬁciently due to economy of scale.

Data analytics, data mining, computational ﬁnancing, scientiﬁc and engineering applications, gam-

ing and social networking, as well as other computational and data-intensive activities beneﬁt from

cloud computing. Storing information on the cloud has signiﬁcant advantages. Content previously con-

ﬁned to personal devices such as workstations, laptops, tablets, and smart phones need no longer be

stored locally. Data stored on computer clouds can be shared among all these devices and it is accessi-

ble whenever a device is connected to the Internet. For example, in 2011 Apple announced the iCloud,

a network-centric alternative for content including music, videos, movies, and personal information. In

February 2017 iCloud had 782 million subscribers according to http://appleinsider.com/.

Cloud computing represents a dramatic shift in the design of systems capable of providing vast

amounts of computing cycles and storage space. Computer clouds use off-the shelf, low-cost compo-

nents. During the previous four decades powerful, one-of-a-kind supercomputers, were built at a high

cost, with the most advanced components available at the time.

In early 1990s Gordon Bell argued that one-of-a-kind systems are not only expensive to build, but

the cost of rewriting applications for them is prohibitive. He anticipated that sooner or later massively

parallel computing will evolve into computing for the masses [59].

There are virtually no bounds on composition of digital systems controlled by software, so we are

tempted to build increasingly more complex systems including systems of systems [335]. The behav-

ior and the properties of such systems are not always well understood. We should not be surprised

that computing clouds will occasionally exhibit an unexpected behavior and large-scale systems will

occasionally fail.

The architecture, the coordination mechanisms, the design methodology, and the analysis tech-

niques for large-scale complex systems such as computing clouds will evolve in response to changes

in technology, the environment, and the social impact of cloud computing. Some of these changes will

reﬂect changes in communication, in the Internet itself in terms of speed, reliability, security, capacity

to accommodate a larger addressing space by migration to IPv6, and so on.

Cloud computing reinforces the idea that computing and communication are deeply intertwined.

Advances in one ﬁeld are critical for the other. Indeed, cloud computing could not emerge as a feasible

alternative to the traditional paradigms for high-performance computing before the Internet was able

to support high-bandwidth, low-latency, reliable, low-cost communication. At the same time, mod-

ern networks could not function without powerful computing systems to manage the network. High

performance switches are critical elements of both networks and computer clouds.

The complexity of the cloud computing infrastructure is unquestionable and raises questions such

as: How can we manage such systems? Do we have to consider radically new ideas, such as self-

management and self-repair for future clouds consisting of millions of servers? Should we migrate from

4 CHAPTER 1 INTRODUCTION

a strictly deterministic view of such complex systems to a non-deterministic one? Answers to these

questions provide a rich set of research topics for the computer science and engineering community.

The cloud movement is not without skeptics and critics. The critics argue that cloud computing is

just a marketing ploy, that users may become dependent on proprietary systems, that the failure of a

large system such as the cloud could have signiﬁcant consequences for a very large group of users who

depend on the cloud for their computing and storage needs. Security and privacy are major concerns

for cloud computing users.

A very important question is if under pressure from the user community the current standardization

efforts will succeed. The alternative, the continuing dominance of proprietary cloud computing envi-

ronments is likely to have a negative impact on the ﬁeld. The cloud delivery models, SaaS, PaaS, IaaS,

together with DBaaS discussed in depth in Chapter 2 will continue to coexist for the foreseeable future.

Services based on SaaS will probably be increasingly more popular because they are more accessi-

ble to lay people, while services based on the IaaS will be the domain of computer savvy individuals,

large organizations, and the government. If the standardization effort succeeds, then we may see IaaS

designed to migrate from one infrastructure to another and overcome the concerns related to vendor

lock-in. The popularity of DBaaS services is likely to grow.

1.2 NETWORK-CENTRIC COMPUTING AND NETWORK-CENTRIC CONTENT

Network-centric computing and network-centric content concepts reﬂect the fact that data processing

and data storage takes place on remote computer systems accessed via the ubiquitous Internet, rather

than locally. The term content refers to any type or volume of media, be it static or dynamic, monolithic

or modular, live or stored, produced by aggregation, or mixed.

The two network-centric paradigms share a number of characteristics:

• Most network-centric applications are data intensive. For example, data analytics allow enterprises

to optimize their operations; computer simulation is a powerful tool for scientiﬁc research in virtu-

ally all areas of science from physics, biology, and chemistry, to archeology. Sophisticated tools for

computer-aided design such as Catia (Computer Aided Three-dimensional Interactive Application)

are widely used in aerospace and automotive industries. The widespread use of sensors generate a

large volume of data. Multimedia applications are increasingly more popular; the larger footprint of

the media data increases the load placed on storage, networking, and processing systems.

• Virtually all applications are network-intensive. Transferring large volumes of data requires high-

bandwidth networks. Parallel computing, computation steering, and data streaming are examples of

applications that can only run efﬁciently on low latency networks. Computation steering in numeri-

cal simulation means to interactively guide a computational experiment towards a region of interest.

• Computing and communication resources (CPU cycles, storage, network bandwidth) are shared and

resources can be aggregated to support data-intensive applications. Multiplexing leads to a higher

resource utilization; indeed, when multiple applications share a system their peak demands for

resources are not synchronized and the average system utilization increases.

• Data sharing facilitates collaborative activities. Indeed, many applications in science, engineer-

ing, as well as industrial, ﬁnancial, governmental applications require multiple types of analysis

1.2 NETWORK-CENTRIC COMPUTING AND NETWORK-CENTRIC CONTENT 5

of shared data sets and multiple decisions carried out by groups scattered around the globe. Open

software development sites are another example of such collaborative activities.

• The systems are accessed using thin clients running on systems with limited resources. In June 2011

Google released Google Chrome OS designed to run on primitive devices and based on the browser

with the same name.

• The infrastructure supports some form of workﬂow management. Indeed, complex computational

tasks require coordination of several applications; composition of services is a basic tenet of

Web 2.0.

There are sources of concern and beneﬁts of the paradigm shift from local to network-centric data

processing and storage:

• The management of large pools of resources poses new challenges as such systems are vulnerable

to malicious attacks that can affect a large user population.

• Large-scale systems are affected by phenomena characteristic to complex systems such as phase

transitions when a relatively small change of environment could lead to an undesirable system

state [328]. Alternative resource management strategies, such as self-organization, and decisions

based on approximate knowledge of the system state must be considered.

• Ensuring Quality of Service (QoS) guarantees is extremely challenging in such environments, as

total performance isolation is elusive.

• Data sharing poses not only security and privacy challenges but also requires mechanisms for access

control for authorized users and for detailed logs of the history of data changes.

• Cost reduction. Concentration of resources creates the opportunity to pay-as-you-go for computing

and thus, eliminates the initial investment and reduces signiﬁcantly the maintenance and operation

costs of the local computing infrastructure.

• User convenience and elasticity, the ability to accommodate workloads with very large peak-to-

average ratios.

The creation and consumption of audio and visual content is likely to transform the Internet. It is

expected that the Internet will support increased quality in terms of resolution, frame rate, color depth,

stereoscopic information. It seems reasonable to assume that the Future Internet

will be content-

centric. Information is the result of functions applied to content.

The content should be treated as having meaningful semantic connotations rather than a string of

bytes; the focus will be on the information that can be extracted by content mining when users request

named data and content providers publish data objects. Content-centric routing will allow users to fetch

the desired data from the most suitable location in terms of network latency or download time. There

are also some challenges, such as providing secure services for content manipulation, ensuring global

rights-management, control over unsuitable content, and reputation management.

The term “Future Internet” is a generic concept referring to all research and development activities involved in development of

new architectures and protocols for the Internet.

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