MIT

CHAPTER : 1

• Introduction to IT building blocks:

Information Technology (IT) is the backbone of modern computing
systems, business processes, and digital transformation. The IT building
blocks are foundational components that make up the IT infrastructure,
enabling data processing, storage, communication, and management of
applications. Here's a detailed explanation of the key IT building blocks:

1. Hardware
Definition:
Physical components of a computer system that perform the actual work
of computing.
Key Components:
• Input Devices: Keyboard, mouse, scanner, sensors.
• Output Devices: Monitor, printer, speakers.
• Processing Unit (CPU): Executes instructions and manages tasks.
• Memory (RAM): Temporary storage for currently running applications
and data.
• Storage Devices: Hard drives (HDD), Solid-state drives (SSD), cloud
storage.
• Networking Devices: Routers, switches, modems, network interface
cards (NICs).
• Servers: Powerful computers providing data and services to other
machines on a network.

2. Software
Definition:
Set of instructions or programs used to operate computers and perform
specific tasks.
Categories:
• System Software: Operating Systems (Windows, Linux, macOS) –
manage hardware and software resources.
• Application Software: Word processors, browsers, ERP systems –
designed for end-users.
• Utility Software: Antivirus, file management tools, backup software –
enhance or maintain system functionality.
• Middleware: Software that connects different applications or services.

3. Data
Definition:
Raw facts and figures that are processed to produce meaningful
information.
Types:
• Structured Data: Organized in databases (tables, rows, columns).
• Unstructured Data: Emails, videos, social media posts.
• Semi-structured Data: XML, JSON files.
Importance:
Data is the most valuable asset in IT – used for decision-making,
analytics, customer service, etc.

4. Networks
Definition:
A collection of interconnected devices that share resources and data.
Types:
• LAN (Local Area Network): Connects computers within a small
geographic area.
• WAN (Wide Area Network): Spans large distances (e.g., the Internet).
• VPN (Virtual Private Network): Secure connection over the internet.
Components:
• Protocols (TCP/IP), IP addressing, DNS, firewalls, routers, and switches.

5. Databases
Definition:
An organized collection of data that can be accessed, managed, and
updated easily.
Types:
• Relational Databases (RDBMS): Use SQL (e.g., MySQL, Oracle).
• NoSQL Databases: Designed for unstructured data (e.g., MongoDB,
Cassandra).
Functions:
Data storage, retrieval, querying, and analysis.

6. People (Human Resources)

 Definition:
The users and IT professionals who develop, manage, and use
technology systems.
Roles:
• End Users: Use IT systems for personal or business tasks.
• IT Professionals: Developers, administrators, network engineers,
analysts, support staff.

7. Procedures
Definition:
The rules and instructions for using the hardware, software, and data.
Examples:
• Data entry procedures
• Backup procedures
• Security protocols
• Software update guidelines

8. Security
Definition:
Measures and tools used to protect IT systems from unauthorized
access, damage, or theft.
Components:
• Cybersecurity Tools: Firewalls, antivirus, intrusion detection systems.
• Encryption: Securing data in transit and at rest.
• Authentication and Authorization: Passwords, biometrics, access
control.

9. Cloud Computing & Virtualization (Modern Building Blocks)

Cloud Computing:
On-demand access to IT resources via the Internet (e.g., AWS, Azure,
Google Cloud).
Virtualization:
Creating virtual versions of physical resources (servers, desktops,
networks).
Benefits:
• Scalability
• Cost efficiency
• Remote access
• Business continuity

10. Internet of Things (IoT) and Emerging Technologies

IoT:
Network of physical objects embedded with sensors and software for
data exchange.
Other Emerging Blocks:
• Artificial Intelligence (AI) & Machine Learning (ML)
• Blockchain
• Edge Computing
• Big Data Technologies

Summary Table of IT Building Blocks

Building Block Key Function

Hardware Physical operations

Software Instructions and apps

Data Basis for information

Networks Connectivity and communication

Databases Structured data management

People Use and manage IT

Procedures Standardize operations

Security Protect IT resources

Cloud/Virtualization Scalable infrastructure

IoT & Emerging Tech Real-time automation & insights

• Non-functional attributes, calculating availability, Availability percentages

and intervals:
• 1. Non-Functional Attributes (NFRs)
• ➤ Definition:
 • Non-functional attributes (or non-functional requirements) define how a system
performs its functions. Unlike functional requirements (which describe what the
system should do), non-functional attributes describe quality characteristics.
• ➤ Common Non-Functional Attributes:

Attribute Description
Availability The proportion of time a system is operational and accessible.
Reliability The ability of a system to perform without failure over a period.
How fast or efficiently a system responds under load (e.g., response time,
Performance
throughput).
Scalability The system’s ability to handle increasing workloads without degradation.
Maintainability How easily a system can be modified or updated.
Usability How user-friendly and accessible the system is.
Security The system’s resistance to unauthorized access and data breaches.
Portability The ease with which software can be transferred to different environments.

2. Calculating Availability
➤ Availability Formula:
Availability (%) =(Uptime/(Uptime + Downtime))×100
Where:
• Uptime = Time the system is working properly
• Downtime = Time the system is unavailable due to failure or
maintenance
➤ Example:
• Uptime in a month: 43,000 minutes
• Downtime in a month: 60 minutes
• Availability (%) =(Uptime/(Uptime + Downtime))×100

Key Concepts
➤ MTBF (Mean Time Between Failures):
Average time between two failures of a system.
➤ MTTR (Mean Time to Repair):
Average time to recover or repair the system after a failure.
Availability=MTBF/MTBF + MTTR×100
MTBF = 10,000 minutes
MTTR = 10 minutes

• IT infrastructure performance metrics like Mean Time Between Failures

(MTBR):
 IT Infrastructure Performance Metrics
Performance metrics help organizations monitor, evaluate, and improve the
quality and reliability of IT systems, especially in areas like servers, networks,
and applications.

1. Mean Time Between Failures (MTBF)

➤ Definition:
MTBF is the average time between system failures. It measures system
reliability and is usually expressed in hours.
MTBF=Total Uptime/Number of Failures

➤ Example:
If a server runs for 10,000 hours and fails 5 times:
MTBF=10,000/5=2,000 hours

Higher MTBF = More reliable system

Used mostly for hardware reliability and long-term planning.

2. Mean Time to Repair (MTTR)

➤ Definition:
MTTR is the average time required to fix a system or component after a failure.
MTTR=Total Downtime/Number of Repairs

➤ Example:
If 3 repairs took 30, 45, and 15 minutes:
MTTR=30+45+153/3=30 minutes

Lower MTTR = Faster recovery

A key indicator of maintainability

3. Mean Time to Failure (MTTF)

➤ Definition:
MTTF is similar to MTBF but is used for non-repairable systems (e.g., light
bulbs, SSDs). It measures how long a product lasts before it fails permanently.
MTTF=Total Operating Time/Number of Devices

Applicable to one-time-use hardware components.

4. Mean Time to Detect (MTTD)

➤ Definition:
The average time it takes to detect a failure or incident after it occurs.

Important for security monitoring and incident response.

5. Mean Time to Acknowledge (MTTA)

➤ Definition:
The average time taken for the support team to acknowledge an alert or
failure.

Lower MTTA = faster incident acknowledgment = faster MTTR.

6. Availability Formula (Using MTBF & MTTR)

Availability (%)=MTBF/MTBF + MTTR×100

Quick Comparison Table

Metric Purpose Formula Unit

MTBF Time between failures Total Uptime / Failures Hours

Life of non-repairable
MTTF Total Time / Items Hours
items
Metric Purpose Formula Unit

MTTR Time to fix a failure Total Downtime / Repairs Minutes/Hours

Time to detect a Time from failure to

MTTD Minutes
problem detection

Time to acknowledge Time from alert to

MTTA Minutes
an issue acknowledgment

MTBF / (MTBF + MTTR) ×

Availability Uptime ratio %
100

Real-world Example – Server Uptime

• MTBF = 5,000 hours
• MTTR = 2 hours
Availability=(5000/5000+2)100≈99.96%

Summary:
• MTBF and MTTR are core indicators of IT system reliability and
maintainability.
• MTTF applies to items that can’t be repaired.
• MTTD and MTTA improve response time in incident management.
• These metrics guide decisions in SLA agreements, system design, and risk
management.

➢ 1. Sources of Unavailability
➢ Unavailability means the system is not accessible or functional for users.
Understanding the root causes helps reduce downtime and improve
reliability.
➢ Common Sources:
➢ Source ➢ Explanation

➢ Hardware Failures ➢ Server crashes, disk failures, power outages.

➢ Programming errors, memory leaks,

➢ Software Bugs application crashes.

➢ Latency, DNS failures, ISP outages,

➢ Network Issues misconfigured firewalls.

➢ Planned ➢ System upgrades, patching, maintenance

Downtime tasks.

➢ Unplanned ➢ Sudden failures without notice (e.g., security

Downtime breaches).

➢ Misconfiguration, accidental deletion,

➢ Human Errors improper deployment.

➢ Environmental ➢ Natural disasters, temperature fluctuations,

Factors fire, etc.

➢ DDoS attacks, ransomware, or unauthorized

➢ Security Breaches access that disables services.

➢ Goal: Identify and mitigate these causes using redundancy,

automation, and monitoring.
➢
➢ 2. Availability Patterns
➢ Availability patterns are design strategies or best practices to ensure that
systems remain available and resilient.
➢ Key Patterns:
➢ Pattern ➢ Description

➢ Deploying duplicate hardware or services to

➢ Redundancy take over during failures (e.g., RAID, multiple
servers).

➢ Automatically switching to a backup system if

➢ Failover Systems the primary fails.
➢ Pattern ➢ Description

➢ Distributing workload across multiple systems

➢ Load Balancing to avoid overloading a single one.

➢ Parts of the system remain functional even if

➢ Graceful some components fail (e.g., video streaming at
Degradation lower resolution).

➢ Automatically adjusting system resources

➢ Auto-scaling based on traffic and usage.

➢ Disaster ➢ Backup and restore mechanisms to recover

Recovery from catastrophic failures.

➢ High Availability ➢ Multiple systems grouped to ensure continuous

Clustering service.

➢
➢ 3. Introduction to Performance
➢ Performance in IT refers to how efficiently a system handles tasks, users,
or workloads.
➢ Performance is measured using:
➢ Metric ➢ Meaning

➢ Time taken by a system to respond to a

➢ Response Time request.

➢ Latency ➢ Delay between request and first response byte.

➢ Number of tasks/requests a system can

➢ Throughput process per unit time.

➢ Resource ➢ Usage of CPU, memory, storage, and

Utilization bandwidth.

➢ Number of users/tasks handled

➢ Concurrency simultaneously.

➢ A well-performing system is fast, responsive, efficient, and scalable

under load.
➢
➢ 4. Performance During Infrastructure Design
➢ Performance must be planned from the beginning, not just fixed later.
➢ Key Factors to Consider:
➢ Area ➢ Design Decision

➢ Design for horizontal scaling (adding more

➢ Scalability machines) or vertical scaling (adding more
power).

➢ Capacity ➢ Estimate and allocate resources based on

Planning expected peak load.

➢ Load ➢ Use load balancers to prevent bottlenecks.

Distribution
➢ Ensure low latency, high bandwidth, and
➢ Network Design optimized routing.

➢ Design for failure with redundancy and fallback

➢ Resilience systems.

➢ Use caches (e.g., Redis, CDN) to reduce repeated

➢ Caching processing.

➢ Storage ➢ Select appropriate storage (e.g., SSDs for speed,

Architecture RAID for redundancy).

➢ Tools: Modeling tools, stress-testing frameworks, simulation.

➢
➢ 5. Performance of a Running System
➢ After deployment, performance must be continuously monitored and
optimized.
➢ Monitoring Techniques:
➢ Tool/Technique ➢ Purpose

➢ APM Tools (e.g., New Relic, ➢ Track app performance in real-

Dynatrace) time.

➢ System Monitoring (e.g., ➢ Monitor CPU, memory, disk, and

Nagios, Zabbix) network usage.
➢ Tool/Technique ➢ Purpose

➢ Analyze performance issues from

➢ Log Analysis (e.g., ELK stack) logs.

➢ Measures end-user experience

➢ Real User Monitoring (RUM) directly.

➢ Comparing performance against a

➢ Benchmarking standard.

➢ Simulating multiple users (tools

➢ Load Testing like JMeter, Locust).

➢ Performance Improvement Actions:

➢ Add more servers or containers (scale-out)
➢ Tune database queries and indexes
➢ Upgrade hardware or use SSDs
➢ Optimize code (reduce loops, unnecessary I/O)
➢ Enable caching or compression
➢ Refactor architecture (microservices, queues)
➢ Continuous performance management is vital to meet SLAs and user
expectations.
➢
➢ Summary Table
➢ Topic ➢ Key Point

➢ Sources of ➢ Hardware, software, network, human

Unavailability errors

➢ Availability Patterns ➢ Redundancy, failover, clustering, scaling

➢ Performance ➢ Metrics: response time, throughput,

Introduction latency

➢ Performance in Design ➢ Capacity planning, caching, architecture

➢ Monitoring, tuning, benchmarking,

➢ Performance in Runtime scaling

➢
➢ Would you like this content formatted into a presentation, handout, or
revision chart?
➢ You said:
➢ Sources of unavailability, Availability patterns, Introduction to
performance, Performance during infrastructure design, Performance of
a running system. in detail give in the form of definations
➢ ChatGPT said:
➢ Here are the topics provided in definition format, suitable for notes,
study guides, or presentations:
➢
➢ 1. Sources of Unavailability – Definition
➢ Sources of unavailability refer to the various causes that make a system
or service inaccessible or non-functional for users. These sources may
include hardware failures, software bugs, network issues, human errors,
planned maintenance, or unexpected disasters.
➢
➢ 2. Availability Patterns – Definition
➢ Availability patterns are predefined design strategies used in system
architecture to ensure continuous service availability. These patterns
include methods like redundancy, failover mechanisms, load balancing,
and graceful degradation to minimize downtime and ensure system
reliability.
➢
➢ 3. Introduction to Performance – Definition
➢ Performance in IT refers to the efficiency and speed with which a system
or application completes tasks under specific conditions. It is commonly
measured by metrics such as response time, throughput, latency, and
resource utilization, and is essential for ensuring user satisfaction and
system reliability.
➢
➢ 4. Performance During Infrastructure Design – Definition
➢ Performance during infrastructure design refers to the consideration
and integration of performance-related factors while planning and
building IT systems. It includes estimating workloads, ensuring scalability,
choosing the right hardware and software components, and structuring
the network and storage for optimal efficiency before deployment.
➢
➢ 5. Performance of a Running System – Definition
➢ Performance of a running system refers to the ongoing measurement
and management of how well a deployed IT system handles its workload.
This involves monitoring system metrics, analyzing performance
bottlenecks, applying optimizations, and ensuring the system meets
expected service levels in real-time operation.
 CHAPTER : 2
Trends in IT Infrastructure:
➢ Cloud computing:
Definition: Cloud computing refers to the delivery of computing services over the
internet ("the cloud"). These services include storage, processing power,
networking, databases, software, and analytics, among others, and are provided
by cloud service providers. Instead of owning and maintaining physical data
centers or servers, organizations and individuals can access these resources on-
demand, paying only for what they use.

➢ Key Characteristics of Cloud Computing:

1. On-Demand Self-Service: Users can provision computing resources
automatically without human intervention.
2. Broad Network Access: Services are available over the internet and can be
accessed from various devices.
3. Resource Pooling: Providers pool computing resources to serve multiple
customers using a multi-tenant model.
4. Scalability and Elasticity: Resources can be scaled up or down dynamically
based on demand.
5. Pay-as-You-Go Pricing: Users pay only for the resources they consume.
Types of Cloud Computing:
1. Public Cloud: Resources are shared among multiple users and managed by a
third-party provider (e.g., AWS, Microsoft Azure, Google Cloud).
2. Private Cloud: Resources are used exclusively by a single organization, offering
greater control and security.
3. Hybrid Cloud: Combines public and private clouds, allowing data and
applications to be shared between them.
4. Community Cloud: Shared infrastructure among organizations with common
goals or regulatory requirements.

Cloud Service Models:

1. Infrastructure as a Service (IaaS): Provides virtualized computing resources like
servers, storage, and networks (e.g., Amazon EC2, Microsoft Azure VMs).
2. Platform as a Service (PaaS): Offers platforms for building, testing, and
deploying applications without managing the underlying infrastructure (e.g.,
Google App Engine, Heroku).
3. Software as a Service (SaaS): Delivers software applications over the internet
(e.g., Google Workspace, Salesforce).
 Cloud computing is widely adopted for its flexibility, cost-efficiency, and ability to
support innovation at scale.

➢ Cloud computing Architecture:

1. Frontend
Frontend of the cloud architecture refers to the client side of cloud computing
system. Means it contains all the user interfaces and applications which are used
by the client to access the cloud computing services/resources. For example, use
of a web browser to access the cloud platform.
2. Backend
Backend refers to the cloud itself which is used by the service provider. It contains
the resources as well as manages the resources and provides security mechanisms.
Along with this, it includes huge storage, virtual applications, virtual machines,
traffic control mechanisms, deployment models, etc.
Components of Cloud Computing Architecture

Following are the components of Cloud Computing Architecture:

1. Client Infrastructure: Client Infrastructure is a part of the frontend component. It
contains the applications and user interfaces which are required to access the cloud
platform. In other words, it provides a GUI( Graphical User Interface ) to interact with
the cloud.
2. Application : Application is a part of backend component that refers to a software or
platform to which client accesses. Means it provides the service in backend as per the
client requirement.
3. Service: Service in backend refers to the major three types of cloud based services
like SaaS, PaaS and IaaS. Also manages which type of service the user accesses.
4. Runtime Cloud: Runtime cloud in backend provides the execution and Runtime
platform/environment to the Virtual machine.
5. Storage: Storage in backend provides flexible and scalable storage service and
management of stored data.
6. Infrastructure: Cloud Infrastructure in backend refers to the hardware and software
components of cloud like it includes servers, storage, network devices, virtualization
software etc.
7. Management: Management in backend refers to management of backend
components like application, service, runtime cloud, storage, infrastructure, and other
security mechanisms etc.
8. Security: Security in backend refers to implementation of different security
mechanisms in the backend for secure cloud resources, systems, files, and
infrastructure to end-users.
9. Internet: Internet connection acts as the medium or a bridge between frontend and
backend and establishes the interaction and communication between frontend and
backend.
10. Database: Database in backend refers to provide database for storing structured data,
such as SQL and NOSQL databases. Example of Databases services include Amazon
RDS, Microsoft Azure SQL database and Google CLoud SQL.
11. Networking: Networking in backend services that provide networking infrastructure
for application in the cloud, such as load balancing, DNS and virtual private networks.
12. Analytics: Analytics in backend service that provides analytics capabilities for data in
the cloud, such as warehousing, business intelligence and machine learning.
 ➢ Cloud computing Models(Service Models):

1. Software as a service (SaaS)

2. Platform as a service (PaaS)
3. Infrastructure as a service (IaaS)
4. Anything/Everything as a service (XaaS)
5. Function as a Service (FaaS)

➢ Software as a Service (SaaS)

• SaaS is a cloud computing model that provides fully functional applications over the
internet.
• With SaaS, you can access software applications without worrying about installation,
maintenance, or infrastructure.
• These applications are typically subscription-based, with updates and support
handled by the provider.

What is SaaS?
• SaaS delivers software applications that are hosted and maintained by a third-
party provider.
• You access these applications via a web browser, removing the need for on-
premises installations or infrastructure.
• SaaS is ideal for businesses that need ready-to-use applications without complex
setup procedures.

The following are some of the key features of SaaS:

• Fully managed by the vendor: The service provider takes care of all updates,
security patches, and maintenance.

• Subscription-based pricing: SaaS is usually offered through subscription-based

pricing models, making it cost-effective for businesses of all sizes.

• Browser-based access: You access SaaS applications via a web browser,

removing the need for local installations or hardware requirements.

Common use cases

The following are some of the common use cases of SaaS:

 • Email: Services like Gmail or Outlook are classic examples of SaaS for email
communication.

• Customer Relationship Management (CRM): SaaS platforms like Salesforce

help businesses manage customer relationships, track sales, and analyze data.

• Project management: SaaS tools like Trello and Asana provide collaborative
platforms for managing tasks and projects.

Popular SaaS examples

The following are some of the popular SaaS examples:

• Salesforce: A leading SaaS solution for CRM, helping businesses manage their
customer relationships and sales pipelines.

• Google Workspace: A suite of productivity tools (Docs, Sheets, Drive, etc.)

delivered as SaaS.

➢ Platform as a Service (PaaS)

PaaS provides a higher-level platform where developers can build, deploy, and manage
applications without having to manage the underlying infrastructure.
It abstracts much of the system-level operations, which enables faster development
cycles and a focus on the business logic of the application.
What is PaaS?
PaaS offers a development and deployment platform with built-in infrastructure
services.
It provides developers with tools and environments to create applications without
worrying about hardware management, operating systems, or network
configurations.

The following are some of the key features of PaaS:

• Abstraction of infrastructure: PaaS abstracts most of the infrastructure management,
so developers can focus on application development instead of dealing with the
complexities of servers and storage.
• Built-in scalability: PaaS platforms often include automatic scaling capabilities, so
applications can grow based on demand.
• Developer tools: PaaS services typically come with built-in tools for application
development, including APIs, databases, and frameworks, streamlining the
development process.
 The following are some of the common use cases of PaaS:
• Web app development: PaaS is commonly used to develop and deploy web
applications with minimal infrastructure management.
• API management: Developers can use PaaS to manage and deploy APIs, ensuring
seamless communication between different services and applications.
• Microservices architecture: PaaS is often used to build microservices, which are small,
independently deployable units of functionality that can be scaled individually.

Popular PaaS providers

• Heroku: A popular PaaS offering that allows developers to build and deploy web
applications without worrying about infrastructure.
• Google App Engine: Google’s PaaS platform provides an environment for building and
deploying applications in various languages, including Python, Java, and Go.
• AWS Elastic Beanstalk: Amazon’s PaaS offering, which allows developers to deploy and
manage applications without dealing with the underlying infrastructure.

➢ What is IaaS?
IaaS provides virtualized computing resources, such as storage, processing power, and
networking, over the internet.
With IaaS, you do not need to purchase or manage physical hardware. Instead, you get
virtual machines, storage, and networking resources on demand, as needed.
The following are some of the key features of IaaS:
• On-demand infrastructure: You can scale resources like storage, computing power, and
bandwidth without needing to purchase additional hardware.
• Scalability: IaaS providers offer flexible scaling options, which allow you to adjust
resources based on current needs.
• Pay-as-you-go pricing: IaaS typically uses a pay-per-use model, which helps businesses
save on operational costs by only paying for the resources they use.
Common use cases:
• Hosting websites: IaaS is commonly used to host websites, which provides both the
computing resources and network capabilities to handle large amounts of traffic.
• Disaster recovery: Many businesses use IaaS for backup and disaster recovery
solutions, which offer high availability without requiring substantial investments in
physical hardware.
• Test environments: Developers can use IaaS to create test environments and quickly
spin up virtual machines to test applications without affecting live production
environments.

Popular IaaS providers

• AWS EC2: Amazon Web Services (AWS) Elastic Compute Cloud (EC2) is one of the most
popular IaaS offerings, providing scalable compute capacity in the cloud.
• Google Compute Engine: Google’s IaaS offering, which provides virtual machines and
cloud storage.
• Microsoft Azure VMs: Microsoft Azure’s virtual machine service enables you to run
virtual servers and scale based on demand.

• IaaS: The user has the most control, managing operating systems, applications, and
security, while the provider handles physical infrastructure.
• PaaS: The provider manages the underlying infrastructure and runtime environment,
which allows developers to focus on coding and application deployment.
 • SaaS: The provider manages everything, and the user simply accesses the software
through a web browser.

➢ Cloud computing Models(Deployment Models) / Types of Clouds:

A deployment model in cloud computing refers to the specific environment and configuration
under which cloud services are deployed. It defines who owns the infrastructure, how it is
managed, and who can access it.

1. Public Cloud
The public cloud is a cloud environment owned and operated by third-party cloud service
providers like Amazon Web Services (AWS), Microsoft Azure, or Google Cloud Platform. All
hardware, software, and infrastructure are managed by the provider, and users share the same
infrastructure.
Example: Hosting a web application on AWS EC2 instances.
Benefits:
• Cost-effective
• Easy scalability
• No maintenance required by the user
• Highly reliable and redundant

2. Private Cloud
A private cloud is exclusively used by a single organization. It can be hosted either on-premise
or by a third-party provider. This model offers greater control and security, making it ideal for
industries with strict compliance requirements.
Example: A government agency hosting confidential data on its own private cloud
infrastructure.
Benefits:
• Enhanced security and privacy
• Greater control over resources
• Customization as per business needs

3. Hybrid Cloud
The hybrid cloud combines the best of both public and private clouds, allowing data and
applications to move between them as needed. It provides greater flexibility and helps in
optimizing workloads.
Example: Running a company’s customer-facing website on a public cloud while keeping
sensitive customer data in a private cloud.
Benefits:
• Cost efficiency with better security
• Seamless scalability
 • Business continuity and disaster recovery support

4. Community Cloud
A community cloud is shared among multiple organizations that have similar requirements,
such as security, compliance, or policy considerations. It can be managed by the organizations
or a third-party provider.
Example: Multiple hospitals sharing a community cloud for storing patient health records.
Benefits:
• Cost shared among multiple users
• Industry-specific infrastructure
• Collaborative platform for related organizations.
 Infrastructure as a Service (IaaS)
Definition:
IaaS is a cloud computing service model that delivers virtualized computing
infrastructure over the internet. It provides fundamental computing resources like
virtual machines (VMs), storage, networks, and operating systems on a pay-per-
use basis.

Key Components of IaaS:

1. Compute (Processing power)
o Virtual Machines (VMs) with customizable CPU, RAM.
o Examples: AWS EC2, Azure VMs, Google Compute Engine.
2. Storage
o Scalable storage systems like SSD, HDD, object storage, block
storage.
o Examples: AWS S3, Azure Blob Storage.
3. Networking
o Virtual networks, firewalls, load balancers, IP addresses.
o Examples: AWS VPC, Azure Virtual Network.
4. Operating Systems
o Choice of OS like Linux, Windows, Ubuntu for each VM.

Features of IaaS:
FFeature Description

Scalability Resources can be scaled up/down based on needs.

On-Demand Access Instant access to computing power via the internet.

Pay-as-you-go Charges based on actual usage.

Multiple users share the same physical hardware

Multi-tenancy ssecurely.

Flexibility Choose your OS, tools, configurations.

Advantages of IaaS:
• No need to invest in physical hardware.
• High scalability and flexibility.
 • Supports rapid innovation and development.
• Backup, recovery, and disaster recovery are easier.
• Allows full control of infrastructure.

Use Cases of IaaS:

• Website and application hosting
• Development and testing environments
• Big data processing and analytics
• High-performance computing (HPC)
• Storage, backup, and disaster recovery

Popular IaaS Providers & Examples:

Provider IaaS Service

Amazon Amazon EC2 (Elastic Compute Cloud)

Microsoft Azure Virtual Machines

Google Google Compute Engine

IBM IBM Cloud Infrastructure

Oracle Oracle Cloud Infrastructure

Real-Life Example:
A startup needs to launch a website. Instead of buying physical servers, it uses
Amazon EC2 (IaaS) to create virtual machines, installs Linux and Apache, and
deploys the site. They only pay for the hours used, and can scale the system as
user traffic grows.
 Green IT (Green Information Technology)

Definition:
Green IT refers to environmentally sustainable computing. It involves designing,
using, and disposing of computers and IT infrastructure in a way that reduces
environmental impact.

Goals of Green IT:

• Reduce energy consumption
• Minimize e-waste
• Lower carbon emissions
• Improve resource efficiency

Use of Greener Equipment

What is Greener Equipment?

Greener equipment includes energy-efficient and environmentally-friendly IT
hardware that consumes less power, lasts longer, and is easier to recycle.

Examples of Greener Equipment:

1. Energy Star Certified Devices – Computers, monitors, printers that
consume less power.
2. LCD/LED Monitors – Use less energy than CRTs.
3. Solid-State Drives (SSD) – Faster and more energy-efficient than
traditional HDDs.
4. Efficient Power Supplies – 80 PLUS certified power units reduce power
loss.
5. Virtualization – Running multiple systems on one server reduces physical
hardware needs.

Practices:
• Use laptops instead of desktops (they use less energy).
• Enable power-saving modes (sleep/hibernate).
 • Choose devices with recyclable materials.

Introduction to Data Center

What is a Data Center?

A data center is a facility that houses computer systems and associated
components such as storage systems, networking devices, and power backups.
It is used by businesses, governments, and cloud providers to store, manage,
and process large amounts of data.

Components of a Data Center:

1. Servers – Main computing units.
2. Storage Devices – For data storage (e.g., SAN, NAS).
3. Networking – Routers, switches, firewalls.
4. Power Systems – UPS (uninterruptible power supply), backup
generators.
5. Cooling Systems – Air conditioning and ventilation for temperature
control.
6. Security Systems – Fire protection, surveillance, and access control.

Green Data Centers:

These are energy-efficient data centers designed to reduce environmental
impact.
Features include:
• Efficient cooling systems (liquid cooling, outside air cooling).
• Use of renewable energy (solar, wind).
• Virtualization to reduce hardware.
• Monitoring tools to optimize energy usage.
 Summary:

Topic Key Points

Green IT Eco-friendly use of IT to reduce energy use and e-waste

Greener
Energy Star devices, SSDs, efficient power units
Equipment

Facility for data processing, includes servers, cooling,

Data Center
security

Uses less power, virtualizes servers, uses renewable

Green Data Center
energy