Mesosphere DC/OS 应用程序交付与现代企业架构

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Application Delivery with Mesosphere DCOS Mesosphere DC/OS 是一个基于Apache Mesos的分布式操作系统，旨在帮助企业构建和操作软件应用程序和服务。DC/OS 提供了一种现代化的企业架构，能够满足快速变化的业务需求。 ** Mesosphere DC/OS 的架构** Mesosphere DC/OS 的架构基于 Apache Mesos，Mesos 是一个开源的分布式系统 kernel，能够管理大规模的计算资源。DC/OS 在 Mesos 之上提供了一层抽象，简化了应用程序的部署、管理和扩展。 ** DC/OS 的组件** DC/OS 由多个组件组成，包括： * Marathon：一个分布式的应用程序管理器，负责应用程序的部署、扩展和管理。 * Mesos：一个分布式的系统 kernel，负责管理计算资源。 * ZooKeeper：一个分布式的配置管理器，负责存储和管理应用程序的配置信息。 ** DC/OS 的特点** DC/OS 具有以下特点： * 高度可扩展性：DC/OS 能够自动扩展应用程序，以满足业务的增长需求。 * 高度可靠性：DC/OS 能够自动检测和恢复应用程序的故障，确保业务的连续性。 * 高度灵活性：DC/OS 支持多种编程语言和框架，能够满足不同的业务需求。 ** 使用 DC/OS 的优点** 使用 DC/OS 能够带来以下优点： * 加快应用程序的开发和部署速度 * 提高应用程序的可扩展性和可靠性 * 降低应用程序的维护成本 * 提高企业的竞争力 ** DC/OS 在企业架构中的角色** DC/OS 在企业架构中扮演着核心角色，能够帮助企业： * 实现数字化转型 * 提高业务的敏捷性和灵活性 * 降低 IT 成本 * 提高企业的竞争力 ** 总结** Mesosphere DC/OS 是一个基于 Apache Mesos 的分布式操作系统，旨在帮助企业构建和操作软件应用程序和服务。DC/OS 提供了一种现代化的企业架构，能够满足快速变化的业务需求。DC/OS 具有高度的可扩展性、可靠性和灵活性，能够帮助企业实现数字化转型、提高业务敏捷性和降低 IT 成本。

2 https://dcos.io/blog/2016/join-the-dc-os-day-2-operations-working-group/index.html

3 https://landing.google.com/sre/book.html

software development methodology). In the next sections, we’ll

investigate some of the different areas that are affected by the MEA.

Operations

It takes more to run an application in production than installing

some software and starting applications. For operators, their job

truly begins on day two—maintaining, upgrading, and debugging a

running cluster without downtime.

In this report, I am using “operations” as a term to refer to all the

tasks that arise to keep applications and services up and running.

Traditionally, system administration has involved routine manual

intervention to keep systems functioning correctly. These opera‐

tional approaches have had to evolve to meet the needs of always-

on, highly connected modern systems. Advanced operational

approaches have been developed coining terms such as Day 2 Ops,

DevOps, and the aforementioned SRE. These approaches use soft‐

ware to define system configuration and automate operational tasks.

SRE is a term that originates from Google, and the SRE approach is

set out in an excellent book that is available online for free.

The aim

of SRE is to deliver an optimal combination of feature velocity and

system reliability. The responsibilities of SRE, as defined by Google,

are availability, latency, performance, efficiency, change manage‐

ment, monitoring, emergency response, and capacity planning.

That provides a good summary of the typical concerns of an opera‐

tions team. Operations is highly technical, and the efficiency and

effectiveness of the operational team is dependent on many details

of the systems that it uses and maintains. It is essential that an MEA

addresses operational requirements and supports a range of opera‐

tional approaches. Here are key operational tools and practices:

•

Containerization

• Orchestration

• Dynamic service discovery

•

Infrastructure as code

8 | Chapter 2: Why Do We Need Modern Enterprise Architecture?

• Data processing (map-reduce)

By using high-level abstractions, software engineers can develop

new applications more quickly and efficiently. Using well-known

and well-tested systems for underlying services can also contribute

to the reliability and scalability of the resulting application.

Having access to a wide range of sophisticated abstractions

improves both software development and system operation. For

example, if software engineers have access to a graph database, a

transactional relational database, and a highly concurrent key-value

database, they can make use of each database for appropriate tasks.

Choosing the right tool for the job makes both development and

subsequent operation much more efficient than attempting to force

tasks onto an unsuitable service.

To allow fast and versatile application development, the MEA

should allow us to easily use a range of high-level service abstrac‐

tions provided by well-known, reliable, and scalable implementa‐

tions.

Hardware and Infrastructure

Any organization deploying an enterprise application needs to con‐

sider what computing infrastructure it will use—predominantly, this

decision is focused on computing and network hardware but can

include many other concerns. Deciding on what infrastructure to

use is an extremely significant and difficult decision to make for

many businesses, and choices typically have long-lasting conse‐

quences.

Before we go further into this topic, it is important to stress that

DC/OS can run on a wide range of computing infrastructures,

including on-premises datacenters and cloud platforms; it does not

require you to use a particular infrastructure.

Cloud computing platforms provide a spectrum of services, from

bare-metal servers to high-level abstractions like databases and mes‐

sage queues, as described in the previous section. Examples of com‐

panies that provide these services include Amazon Web Services

(AWS), Google Cloud, Microsoft Azure, RapidSwitch, and Heroku.

The major cloud providers are widely used; have extremely good

Service-Level Agreements (SLAs); provide a range of sophisticated

10 | Chapter 2: Why Do We Need Modern Enterprise Architecture?

4 https://medium.com/@Zetta/the-intelligence-era-and-the-virtuous-loop-784e9928f51b

5 http://techblog.netix.com/2013/08/deploying-netix-api.html

6 https://deepmind.com/blog/deepmind-ai-reduces-google-data-centre-cooling-bill-40/

Effective collection and exploitation of data from software systems is

being used by businesses to build significant competitive advan‐

tages. To make the most from the opportunities requires systems to

have the capacity to collect, store, and analyze large volumes of data.

Subsequent to analysis, organizations need to incorporate the results

of that analysis into the operation and decision-making process.

Real-time analytics is most commonly associated with advertising,

sales, and the financial industries, but it is now finding uses in an

entire range of applications; for example, to provide system admin‐

istrators with Canary metrics

or using machine learning and pre‐

dictive analytics to automatically scale infrastructure and services in

datacenters.

An ideal machine learning system automatically analyzes informa‐

tion from live systems and uses the results to make predictions and

decisions in real time. To realize the value from data, an MEA must

treat data collection, storage, and analytics as principal concerns

fully supported by the system architecture and incorporated into

software development and system operation.

Many existing application architectures such as the 12-factor app

were developed to address the needs of applications that run as serv‐

ices and use localized, transactional data architectures (such as SQL

databases) for storing data. In these data architectures, analysis is

performed as a separate function, typically one removed from live

systems requiring Extract, Transform, and Load (ETL) processes

and separate data warehouse infrastructure. These systems are

costly, difficult to adapt to changing data models (slowing develop‐

ment), and, most important, take a long time to close the loop

between data collection, analysis, and action. A data-driven service

architecture still has all of the requirements of an architecture such

as the 12-factor app, but it has additional requirements related to the

automation of collection and analysis of data.

The requirement that we have for the MEA is that it will support the

collections, storage, and analysis of large amounts of data and that it

will allow us to easily use the tools and techniques of modern data

12 | Chapter 2: Why Do We Need Modern Enterprise Architecture?

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