Keeping Your Cloud Infrastructure Healthy With the Internet of Things

Table of Contents

Chapter 1What Connected Machine Intelligence Means For Businesses Today

Chapter 2Challenges for Data Centers

Chapter 3Cloud Operations: Better With Predictive Analytics

Chapter 4VMware Can Help

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4

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IntroductionThe Internet of Things is Fast Becoming a Business Reality

1

Imagine if global logistics systems could

precisely track incoming shipments, giving

complete visibility into operations from

receiving raw goods to products leaving

the manufacturing floor. Or if sensors

on retail shelves could notify the local

warehouse when more stock is needed at

a particular store, enabling global inventory

to manage itself.

Welcome to the business Internet of

Things (IoT).

In the not-so-distant future, every object,

many elements in the natural world, and

even every person could have intelligence

embedded in them and be networked.

Everything will be capable of being

monitored, of contributing data to complex

Introduction

User-facing cloud services

Remote storage device (fixed or mobile)

Wide-area link

Service layer, cloud storage, analytics

Sensor Multi-sensor security device

T Local scanning device

Multiple independent remote network switches in

one location

Multiple remote wireless access points

User access via desktop device

User access via mobile device

Server (on-premises)

Local area links

Service provider access

The Anatomy of Machine to Machine Networking

Source: The Executive’s Guide to the Internet of Things, TechRepublic, 2013

2

systems, and of communicating with

everything else to drive a whole range

of possible outcomes.

At the heart of the IoT is data. Lots of

data. Think of all these devices sending

out constant signals. All that data needs

to be captured, stored, and analyzed.

Enterprises seeking to tap into this data

feed require more resources: more servers,

more storage, and greater networking

bandwidth, possibly even more data

centers. Furthermore, data mining and

data analytics techniques become part of

a Big Data strategy aimed at interpreting

these puzzle parts.

Cloud is absolutely necessary to make

sense of the IoT. Given how much data

the IoT will generate, businesses will

simply not be able to harness it without

a cloud infrastructure.

But cloud alone is not enough. The

IoT requires efficient and effective

management of the underlying cloud

infrastructure, as well as predictive

analytics to understand what the data

is telling you.

The solution? Cloud operations

management solutions with built-in

analytics capable of interpreting streams

of both structured and unstructured

data. That is required to make sense

of the IoT as well as keep the cloud

infrastructure running efficiently and

always available.

Introduction

What Connected Machine Intelligence Means for Businesses Today

Chapter 1

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The idea that the things around us would

be embedded with intelligent sensors and

able to communicate with each other—and

us—first appeared in the late 1980s. An

engineer at Procter & Gamble coined the

phrase “the Internet of Things” in 1999.

Today, the IoT generally refers to devices,

systems, and services that are connected

via a broad array of protocols, domains,

and applications.

More than three-quarters of companies

today are either actively exploring or

already using the IoT, according to research

by the Economist Intelligence Unit1, which

also found that within three years, almost

all companies expect to be using the IoT

in some capacity.

The number of connected devices is expected

to grow rapidly. IDC2 predicts the installed

base of things connected will be 212 billion

by the end of 2020. And according to the

McKinsey Global Institute3, the IOT could

have an economic impact of $2.7 trillion to

$6.2 trillion annually by 2025.

The IoT has the potential to transform

a broad range of industries. Here are just

a few of the potential applications:

Smart utilities

By 2025, placing sensors in the power

grid could result in savings of $200 billion

to $500 billion annually by pricing

electricity based on peak usage times,

according to the McKinsey Global Institute4.

Sensors could also be used to monitor

performance of utility networks in real

time, and could enable electric companies

to detect failures immediately and begin

putting fixes in place. One Midwest utility

Chapter 1

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has been able to offer 100-percent uptime

to commercial customers through its

smart grid installation, according to the

McKinsey Global Institute.

Smart transport

Shippers are ahead of the pack when

it comes to using sensors to track raw

materials, parts, and goods through

production and distribution. Using

RFID tags that emit radio signals that

can be used to pinpoint the location

of products, logistics pros can identify

bottlenecks in production lines,

schedule trucks to take away finished

goods, or track the location of shipments

in transit in real time.

Smart jet engines

The jet turbine engine manufacturing sector

has been a leader in developing IoT products.

Because jet turbine engines are so expensive,

and because so much is at stake in ensuring

their safe operations, manufacturers have

built sensors into the engines that transmit

information about the engine’s condition to

the ground in real time. Now, instead of

scheduled maintenance, manufacturers offer

airlines “predictive maintenance,” which is

performed when an engine actually needs it.

Chapter 1

1 The Internet of Things Business Index: A Quiet Revolution Gathers Pace,” Economist Intelligence Unit, 2013.

2 “Internet of things: $8.9 trillion market in 2020, 212 billion connected things,” Larry Dignan, ZDNet, October 3, 2013. [IDC numbers]

3 Disruptive technologies: Advances that will transform life, business, and the global economy,” McKinsey Global Institute, 2013.

4 “All Things Online,” by James Manyika and Michael Chui, McKinsey Global Institute, September 23, 2013.http://www.mckinsey.com/insights/mgi/in_the_news/all_things_online.

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Challenges for Data Centers

Chapter 2

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Today, the IoT has developed to the

point where it is relatively inexpensive

and simple to embed microprocessors

in devices. The problem is, what happens

to all that data?

For the IoT to work, three steps are

necessary:

1. The data must be captured by an

intelligent sensor in a device

2. That data must be transmitted

across a network and aggregated

into a repository

3. The data must be analyzed so action

can be taken on it

Steps 2 and 3 are where today’s

bottlenecks exist. For starters, the sheer

volume of data generated by the IoT is

going to raise challenges for data centers.

It makes sense: the data generated by

the IoT will expand exponentially as the

number of connected devices multiply.

That’s because handling this explosion

of information is going to be both costly

and performance-challenging. Data

security and privacy issues are also likely

to grow in significance as more consumers

engage with IoT-based products. Beyond

storing, securing, and analyzing this data,

companies should also consider how they

manage the commercial sharing of the

data as the IoT becomes a platform for

trading information.

All this points to needing a central

repository of data (i.e. a cloud)—which

in turn requires a robust approach to

cloud operations.

The cloud provides a centralized place to

Chapter 2

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collect and analyze data; however,

unabated demand for resources can

create numerous challenges for data

centers. It will force IT professionals

who manage data centers to “completely

rethink” the way they handle capacity

across all layers of the IT stack, according

to a recent report by Gartner1.

Cloud can bring together disparate data

streams. It can handle the enormous

volumes of data that the IoT will create.

It can scale as needed, providing massive

cost efficiencies compared to the

traditional data center.

But although the cloud might work for

taking care of Step 2 (finding a central

repository to store all this data), what

about Step 3—taking away actionable

insight from all this data?

Advanced analytics—specifically, predictive

analytics—are also needed. Predictive

analytics is a type of advanced analytics

that makes predictions about unknown

future events by analyzing current and

past data. Predictive analytics can also help

companies understand what is happening

from indirect evidence.

Techniques from the fields of data mining,

process modeling, artificial intelligence,

and machine learning are applied to

uncover patterns in both structured and

unstructured data to determine what is

an “anomaly” versus what is “normal.”

The indirect capabilities of predictive

analytics shouldn’t be underestimated.

For example, sensors on an airline’s

turbine engine send in data that

shows the aircraft is burning fuel at

Chapter 2

1 “Gartner Says the Internet of Things Installed Base Will Grow to 26 Billion Units By 2020,” Gartner Inc., December 2013.

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a higher-than-normal rate. Perhaps a jet

engine has a failing part or simply needs

maintenance?

The combination of the IoT and predictive

analytics is a particularly potent one

because it allows enterprises to anticipate

issues. With intelligence embedded in

the everyday objects of business, and

the ability to leverage that intelligence to

project likely future events, enterprises

are transformed from being reactive to

proactive, whether it involves keeping

cloud infrastructure healthy and avoiding

costly outages, anticipating when

equipment needs maintenance before it

breaks down, or foreseeing inventory or

raw goods shortages before that impacts

manufacturing.

Chapter 2

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Cloud Operations: Better With Predictive Analytics

Chapter 3

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As IoT applications become more integral to

businesses, keeping the cloud infrastructure

that supports them healthy becomes vitally

important.

Despite the accelerating popularity of

virtualization and cloud technologies, these

infrastructures have turned out to be much

more complex and difficult to manage than

generally anticipated. IT professionals must

be able to see into all the different layers in

a cloud. Problems can occur in an internal

physical network, or in a virtual network

managed by a virtual switch. CPU utilization

can be so high that it slows down service

on either a physical or virtual machine. But

a high CPU utilization on one application

could be normal for another application,

so you need a way to dynamically make

that determination.

Additionally, each possible root cause to

a problem requires a completely different

type of remediation. Predictive analytics

can ensure higher availability, better

capacity management, and better

compliance by helping IT understand

the complexity, dependencies, and

interrelationships in all these different

layers. Even the most experienced network

or storage engineers find it difficult to do

this with traditional tools.

What’s needed: a best-in-class cloud

operations management solution

with predictive analytics that pulls

together all these disparate elements,

giving businesses a unified view of their

operations. Since operational data—both

structured and unstructured—flows in

from all layers of the cloud, having this

Chapter 3

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centralized view is critical to meeting the

demands of the IoT.

Detecting the cause of problems and

remediating them immediately is also

critical for maintaining a healthy cloud.

A cloud operations management solution

provides the ability to perform root-cause

analyses on any issues that occur in a

broad range of independent resources—

storage, network switches, and the like.

A centralized view provides one diagnosis

of the problem, reducing mean time to

identification (MTTI) and suggesting a fix.

With predictive analytics, such a solution

could even detect potential issues before

they become full-blown problems.

A leading cloud operations management

solution provides the following:

• 360 degrees view into environment.

Businesses get complete visibility

into their cloud environments

without having to manually compile

a list of symptoms from a variety

of different cloud components. A

cloud operations solution provides

self-learning analytics, dynamic

thresholds, and smart alerts, which

improves availability and uptime of

production systems.

• Improved compliance. Cloud

operations make it easy to meet IT

Chapter 3

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policies and regulatory mandates

such as PCI, HIPAA, and SOX. With

this enhanced visibility, you can better

enforce such policies and mandates

across all infrastructure assets. It

also correlates performance with

configuration and compliance

data to have a single operations

console across private, public, and

hybrid clouds.

• Lower costs. By automating many

of the previously manual cloud

management tasks, businesses

improve team efficiency and achieve

overall greater operational efficiency.

This results in improved virtual

administrator-to-infrastructure

ratios, and significantly reduces

OpEx. CapEx can also be cut by

reclaiming overprovisioned capacity,

and by forecasting future needs more

accurately.

Sounds overwhelming, but predictive

analytics in a cloud operations solution

can also help make sense of all the

data coming in from the IoT. Yet, only

13 percent of enterprises surveyed by

Gartner1 in March 2013 had a predictive

analytics system in place. That will change:

73 percent of companies intend to increase

spending on predictive analytics in the

next two years.

Chapter 3

1 “Gartner Researchers: Predictive Analytics to Gain Traction in Business,” by Ian B. Murphy, Data Informed.com, March 19, 2013

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VMware Can HelpChapter 4

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VMware is on the forefront of cloud

operations management solutions, helping

enterprises both large and small manage

their cloud infrastructures in a way that

reduces both OpEx and CapEx, minimizes

downtime, and enhances capacity planning.

The Internet of Things is about feeding

Big Data and using cloud operations

to harvest it. Cloud is required to

manage it cost-effectively at acceptable

performance levels. And predictive

analytics help businesses get the most

out of the data—both structured and

unstructured—that is generated by this

brave new world of connected machines.

Keeping cloud operations healthy

is critical. VMware is developing

solutions that can help companies

get a handle on the operations and

analytics challenges that will inevitably

accompany the growth of the IoT.

These include:

• Managing cloud operations

intelligently, from storage through

applications

• Getting a full 360-degree view

of all data

• Resolving cloud operations problems

faster using all the structured and

unstructured IT data available

The IoT promises to help organizations

reap huge dividends in operational

efficiencies; discovering new revenue-

enhancing business models; and making

customer service improvements, among

other benefits.

Chapter 4

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But these benefits will only be possible with

cloud—and a cloud operations management

solution that enables organizations to

keep their cloud infrastructures healthy.

VMware stands at the vanguard of this

next evolution of the Internet, and is

making the investments designed to help

businesses thrive during the transition.

Chapter 4

To explore cloud operations solutions further, please see the following resources:

VMware vRealizeTM Operations Insight

Forrester Consulting Report: Total Economic Impact of VMware vCenter Operations Management Suite

Management Insight Study: Businesses Experience Significant Operational and Business Benefits from vCenter Operations

Predicting the Health of Your Cloud

Understanding Real-Time Log Analytics