Explain the key architectural components of SDN with a neat diagram

1. Application
The application component consists of programs that communicate with the controller using APIs.
This component transmits data about desired network behavior and required resources to the controller,
creating an overview of the network status in the process. The application layer also collects data from
the controller layer to make the required decisions for fulfilling application goals.
Examples of applications include analytics, networking management, and business processes for data
center operations. For instance, an analytics application can be configured to bolster network security by
recognizing suspicious activity.
2. Controller
The controller component receives requirements and instructions from the application component and
uses logic to process and relay them to the networking layer. This core element of the SDN architecture
enables centralized supervision and management, enforcement of network policies, and automation across
both virtual and physical network environments.
The controller is also responsible for collecting data about network health and status from the
hardware layer and communicating this information to the application component. This allows the
application component to create an abstract network overview that includes statistics and events.
3. Datapath
The datapath component allows users to supervise and exert control over the forwarding and
processing of information by the hardware layer. This layer consists of a control-data-plane interface
(CDPI) agent and a traffic- forwarding module and may also contain modules for network traffic
processing.
A single network device can contain one or more SDN datapaths. Likewise, a single SDN datapath
may be defined across multiple devices. This component can also help with processes such as
management of shared hardware, logical to physical mapping, datapath slicing or virtualization, and
compatibility with non-SDN networking.
4. Control to data-plane interface
The CDPI is used as an interface between the controller component and the datapath component. Its
functions include allowing forwarding operations to be programmed, reporting network statistics, and
notifying users of events of interest. Leading SDN solutions feature CDPI components that are open,
interoperable, and vendor- neutral.
5. Northbound interface (NBI)
The NBI relays data between the controller component, the application component, and the policy
layer. This component typically provides an abstract view of the network and enables the direct
expression of network requirements and behavior, regardless of latitude (abstraction) and longitude
(functionality).
6. Southbound interface (SBI)
The SBI relays data between the controller component and individual hardware units connected to the
 network, such as routers, access points, switches, and hardware firewalls. This component further classifies
network concepts into more granular technical details meant for the lower layer of the architecture.
Simply put, SBIs enable network components to exchange data with lower-level components such as
physical and virtual switches, routers, and nodes. For instance, routers rely on the SBI to view the network
topology, decide network flow, and execute requests received from the NBI.

a) What is IoT data analytics? Different Types of IoT Data Analytics

2

b) Explain the Need of IoT Analytics? Discuss the categories

(a). IoT data analytics refers to the process of analyzing the massive amounts of data generated by Internet
of Things (IoT) devices. These devices are equipped with sensors that collect various types of data such as
temperature, humidity, location, movement, and much more. IoT data analytics involves extracting valuable
insights, patterns, and trends from this data to improve decision-making, optimize processes, enhance
efficiency, and create new business opportunities.

Here are some different types of IoT data analytics:

1. Descriptive Analytics: Descriptive analytics involves summarizing historical data to understand what
has happened in the past. It provides insights into trends, patterns, and anomalies in IoT data.
2. Diagnostic Analytics: Diagnostic analytics focuses on identifying the reasons behind certain events
or patterns observed in IoT data. It helps in understanding the root causes of issues or anomalies.
3. Predictive Analytics: Predictive analytics utilizes historical data to forecast future events or trends.
Machine learning and statistical techniques are commonly used to build predictive models that can
anticipate outcomes based on IoT data.
4. Prescriptive Analytics: Prescriptive analytics goes beyond predicting future events by suggesting
actions to optimize outcomes. It provides recommendations on what actions should be taken to
achieve desired results based on insights from IoT data.
5. Real-time Analytics: Real-time analytics involves analyzing data as it is generated by IoT devices,
enabling immediate responses to events or anomalies. It is particularly useful in applications where
timely actions are critical, such as industrial automation or healthcare monitoring.
6. Edge Analytics: Edge analytics involves processing and analyzing data locally on IoT devices or at
the network edge, rather than sending all data to centralized servers or cloud platforms. This
approach reduces latency, conserves bandwidth, and enables quicker decision-making.
7. Streaming Analytics: Streaming analytics is used to analyze continuous streams of data from IoT
devices in real-time. It involves processing data as it is generated and identifying patterns or
anomalies on-the-fly.
8. Spatial Analytics: Spatial analytics focuses on analyzing geospatial data collected by IoT devices,
such as GPS coordinates or location-based information. It helps in understanding spatial patterns,
optimizing routes, and geographically targeted decision-making.

(b). The need for IoT analytics arises from the vast amount of data generated by Internet of Things (IoT)
devices and the potential value that can be derived from analyzing this data. Here are some reasons why IoT
analytics is crucial:

1. Data Volume and Variety: IoT devices generate enormous volumes of data from various sources
such as sensors, machines, vehicles, wearables, and more. This data comes in diverse formats and
types including structured, semi-structured, and unstructured data. Analyzing this data manually is
not feasible, and IoT analytics tools are needed to extract meaningful insights.
2. Real-time Decision Making: Many IoT applications require real-time decision-making capabilities.
For instance, in industrial IoT, equipment failure prediction and preventive maintenance need to be
performed in real-time to avoid costly downtime. IoT analytics enables the processing and analysis
of data streams in real-time, facilitating quick decision-making.
 3. Operational Efficiency: IoT analytics can optimize processes and improve operational efficiency
across various industries. For example, in manufacturing, IoT analytics can help in optimizing
production schedules, reducing waste, and improving resource utilization. In logistics, it can
optimize route planning and fleet management to minimize fuel consumption and delivery times.
4. Predictive Maintenance: Predictive maintenance is one of the key applications of IoT analytics. By
analyzing historical sensor data, machine learning algorithms can predict when equipment is likely to
fail, enabling proactive maintenance to prevent breakdowns and reduce maintenance costs.
5. Enhanced Customer Experience: IoT analytics can help organizations gain insights into customer
behavior and preferences by analyzing data from IoT-enabled products and services. This
information can be used to personalize offerings, improve customer service, and create targeted
marketing campaigns.
6. Safety and Security: IoT devices are often deployed in environments where safety and security are
paramount, such as smart cities, healthcare, and critical infrastructure. IoT analytics can analyze data
from sensors and other sources to detect anomalies, identify security threats, and ensure the safety of
people and assets.

Categories of IoT Analytics:

1. Descriptive Analytics: Descriptive analytics involves summarizing historical IoT data to understand
past trends, patterns, and anomalies. It provides insights into what has happened in the past and
serves as the foundation for other types of analytics.
2. Diagnostic Analytics: Diagnostic analytics focuses on identifying the reasons behind certain events
or patterns observed in IoT data. It helps in understanding the root causes of issues or anomalies.
3. Predictive Analytics: Predictive analytics utilizes historical IoT data to forecast future events or
trends. Machine learning and statistical techniques are commonly used to build predictive models
that can anticipate outcomes based on IoT data.
4. Prescriptive Analytics: Prescriptive analytics goes beyond predicting future events by suggesting
actions to optimize outcomes. It provides recommendations on what actions should be taken to
achieve desired results based on insights from IoT data.

(a) Explain Structured versus Unstructured Data

3 (b) Data in Motion versus Data at Rest

(a). Structured and unstructured data are two fundamental categories that describe the organization and
format of data:

1. Structured Data:
o Definition: Structured data is organized and formatted in a predefined manner, typically
within a database or spreadsheet. It follows a rigid schema or data model, where each data
element is labeled and stored in a tabular format with rows and columns.
o Characteristics:
▪ Consistency: Structured data maintains consistency in terms of format and data types
across all records.
▪ Query-friendly: It is easily searchable and accessible using standard database query
languages like SQL (Structured Query Language).
▪ Examples: Examples of structured data include numerical data, dates, text fields, and
categorical variables stored in databases, spreadsheets, or data warehouses.
2. Unstructured Data:
o Definition: Unstructured data lacks a predefined data model and does not conform to a
specific format. It can be any form of data that does not fit neatly into structured databases,
such as text documents, images, videos, audio files, social media posts, emails, and sensor
data.
o Characteristics:
 ▪ Lack of structure: Unstructured data does not have a pre-defined schema or
organization, making it more challenging to process and analyze.
▪ Varied formats: It can exist in various formats and types, including natural language
text, multimedia files, and binary data.
▪ Examples: Examples of unstructured data include text documents, social media feeds,
emails, images, videos, sensor data streams, and log files.

Comparison:

• Organization: Structured data is organized in a predefined structure with a fixed schema, while
unstructured data lacks a predefined structure and can vary in format.
• Accessibility: Structured data is easily accessible and queryable using standard database query
languages, whereas accessing unstructured data may require specialized tools and techniques for
processing and analysis.
• Analysis: Analyzing structured data is typically more straightforward since it follows a consistent
format, whereas analyzing unstructured data may require advanced techniques such as natural
language processing (NLP), image recognition, or machine learning algorithms to extract meaningful
insights.
• Examples: Structured data includes data stored in databases, spreadsheets, or data warehouses, while
unstructured data includes text documents, images, videos, social media feeds, and sensor data.

In summary, structured data is organized and conforms to a predefined schema, facilitating easy storage,
retrieval, and analysis. On the other hand, unstructured data is more diverse in format and requires
specialized tools and techniques for processing and analysis. Both types of data are valuable and often
complementary, and organizations may need to leverage both structured and unstructured data to gain a
comprehensive understanding of their operations and customers.

(b). "Data in motion" and "data at rest" are two concepts that describe the state or movement of data within a
system or environment:

1. Data in Motion:
o Definition: Data in motion refers to data that is actively being transmitted, processed, or
transferred between systems, devices, or components within a network or system. It is in
constant flux and moving from one location to another.
o Characteristics:
▪ Continuous flow: Data in motion is continuously moving and being processed in real-
time.
▪ Examples: Examples of data in motion include streaming data, network traffic, real-
time sensor data, messages sent over communication channels (such as emails, instant
messages, and voice calls), and data transferred between applications or systems.
2. Data at Rest:
o Definition: Data at rest refers to data that is stored or dormant within a storage medium or
repository. It is not actively being transmitted or processed and remains stationary until
accessed or retrieved.
o Characteristics:
▪ Static state: Data at rest remains unchanged until it is accessed or modified.
▪ Examples: Examples of data at rest include data stored in databases, data warehouses,
file systems, archives, backup tapes, and offline storage devices.

Comparison:

• State: Data in motion is actively moving and being processed, while data at rest is stationary and not
actively being processed or transmitted.
• Activity: Data in motion involves real-time processing and transmission, whereas data at rest
involves storage and retrieval operations.
 • Examples: Examples of data in motion include streaming data, network traffic, and real-time sensor
data, while examples of data at rest include stored data in databases, file systems, and archives.

Usage:

• Data in motion: It is typically associated with real-time processing, streaming analytics, and
communication systems where timely processing and response are required.
• Data at rest: It is often used for storage, archival, and data management purposes, allowing
organizations to store and access historical or inactive data as needed.

In summary, data in motion refers to actively moving data being transmitted or processed in real-time, while
data at rest refers to stationary data stored or dormant within a storage medium. Both concepts are essential
in understanding how data flows and is managed within systems and networks, and organizations may need
to implement different strategies and technologies to handle data in motion and data at rest effectively.

4 Explain IoT Data Analytics Challenges

IoT data analytics presents various challenges due to the unique characteristics of IoT data and the scale of
data generated by IoT devices. Some of the key challenges include:

1. Volume: IoT devices generate vast amounts of data continuously. Managing and processing this
massive volume of data can be overwhelming for traditional analytics systems. Scalable
infrastructure and technologies are required to handle the volume of IoT data efficiently.
2. Velocity: IoT data is generated at high velocity, often in real-time or near-real-time. Processing and
analyzing streaming data in real-time require specialized tools and techniques capable of handling
high data ingestion rates and providing timely insights.
3. Variety: IoT data comes in various formats and types, including structured, semi-structured, and
unstructured data. It can include sensor data, images, videos, audio, text, and more. Integrating and
analyzing diverse data types poses challenges for data integration, normalization, and analysis.
4. Veracity: IoT data may be noisy, incomplete, or inaccurate due to sensor errors, environmental
factors, or transmission issues. Ensuring data quality and reliability is essential for obtaining accurate
insights from IoT data.
5. Value: Extracting actionable insights from IoT data can be challenging, especially considering the
sheer volume and variety of data generated. Organizations need to identify relevant patterns, trends,
and anomalies in IoT data to derive meaningful insights and value from their data analytics efforts.
6. Security and Privacy: IoT devices collect sensitive data about individuals, organizations, and
environments. Ensuring the security and privacy of IoT data throughout its lifecycle, including data
collection, transmission, storage, and analysis, is critical to prevent unauthorized access, data
breaches, and privacy violations.
7. Scalability: As the number of IoT devices and data sources grows, scalability becomes a significant
challenge. IoT analytics systems need to scale horizontally to accommodate the increasing volume of
data and the growing number of connected devices.
8. Interoperability: IoT ecosystems often comprise heterogeneous devices, protocols, and platforms
from various vendors. Ensuring interoperability and compatibility between different IoT devices and
systems is crucial for seamless data integration and analytics.
9. Complexity: IoT data analytics involves complex data processing tasks, including data
preprocessing, transformation, aggregation, and analysis. Designing and implementing robust
analytics pipelines to handle these tasks efficiently can be complex and time-consuming.

Addressing these challenges requires a holistic approach that combines advanced analytics techniques,
scalable infrastructure, robust data management practices, and effective security measures. Organizations
need to invest in technologies, skills, and processes to overcome these challenges and unlock the full
potential of IoT data analytics for driving business value and innovation.
 What is Cloud computing? Explain cloud computing architecture with neat
5 diagram?

Cloud Computing:
The term “Cloud” came from a network design that was used by network engineers to represent the location
of various network devices and there inter-connection. The shape of this network design was like a cloud.
Cloud Computing is defined as storing and accessing of data and computing services over the internet. It
doesn’t store any data on your personal computer. It is the on-demand availability of computer services like
servers, data storage, networking, databases, etc.

The main purpose of cloud computing is to give access to data centers to many users. Users can also access
data from a remote server.
Examples of Cloud Computing Services: AWS, Azure, Google

Cloud computing is the on-demand delivery of IT resources over the Internet with pay-as-you-go pricing.
Instead of buying, owning, and maintaining physical data centers and servers, you can access technology
services, such as computing power, storage, and databases, as-needed basis from a cloud provider like
Amazon Web Services (AWS).
Cloud Computing Architecture:

➢ Cloud computing which is one of the demanding technologies of the current time and which is
giving a new shape to every organization by providing on demand virtualized services/resources.
Starting from small to medium and medium to large, every organization use cloud computing
services for storing information and accessing it from anywhere and anytime only with the help of
internet.
➢ Transparency, scalability, security and intelligent monitoring are some of the most important
constraints which every cloud infrastructure should experience.

Cloud Computing Architecture:

The cloud architecture is divided into 2 parts i.e.
➢ Frontend
➢ Backend
Architecture of cloud computing is the combination of both SOA (Service Oriented Architecture) and EDA
(Event Driven Architecture). Client infrastructure, application, service, runtime cloud, storage,
infrastructure, management and security all these are the components of cloud computing architecture. The
following figure shows the architecture of cloud computing:
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.

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.

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.

Application -Application in backend refers to a software or platform to which client accesses. Means it
provides the service in backend as per the client requirement.

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.

Runtime Cloud -Runtime cloud in backend provides the execution and Runtime platform / environment to
the Virtual machine.

Storage -Storage in backend provides flexible and scalable storage service and management of stored data.

Infrastructure -Cloud Infrastructure in backend refers to the hardware and software components of cloud
like it includes servers, storage, network devices, virtualization software etc.

Management -Management in backend refers to management of backend components like application,

 service, runtime cloud, storage, infrastructure, and other security mechanisms etc.

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.

Internet -Internet connection acts as the medium or a bridge between frontend and backend and establishes
the interaction and communication between frontend and backend.

List out layers of cloud computing and discuss in detail?

6

LAYERS OF CLOUD COMPUTING:

The term service in cloud computing is the concept of being able to use reusable, fine-grained components
across a vendor’s network. Cloud computing services are divided into three classes, according to the
abstraction level of the capability provided and the service model of providers, namely:
1) Infrastructure as a Service (IaaS), 2) Platform as a Service(PaaS), and 3) Software as a Service(SaaS)
There are many other service models all of which can take the form like XaaS, i.e., Anything as a Service.
This can be Network as a Service, Business as a Service, Identity as a Service, Database as a Service or
Strategy as a Service.
These are sometimes called the cloud computing stack because they are built on top of one another.
Knowing what they are and how they are different, makes it easier to accomplish goals. These abstraction
layers can also be viewed as a layered architecture where services of a higher layer can be composed of
services of the underlying layer.
The layered organization of the cloud stack from physical infrastructure to applications as shown in bellow:

Infrastructure as a Service:
Offering virtualized resources such as computation, storage, and communication on demand is known as
Infrastructure as a Service (IaaS) .
A cloud infrastructure enables on-demand provisioning of servers running several choices of operating
systems and a customized software stack. Infrastructure services are considered to be the bottom layer of
cloud computing systems

The main advantage of using IaaS is that it helps users to avoid the cost and complexity of purchasing and
managing the physical servers. It is also known as Hardware as a Service (HaaS). IaaS customers pay on
a per-user basis, typically by the hour, week, or month. Some providers also charge customers based on the
amount of virtual machine space they use.

IaaS provides fully scalable computing resources:

⚫ RAM, CPU, storage infrastructure
⚫ Virtual machines

IaaS Providers:
➢ Service level agreements
o Between the provider and client, guaranteeing a certain level of performance from the
system.
➢ Computer hardware
o Resource components to be rented out.
o Service providers often have this set up as a grid for easier scalability.
➢ Network
o Hardware for firewalls, routers, load balancing, etc.
➢ Internet connectivity
o Allow clients to access the hardware from outside.
➢ Platform virtualization environment
o Allow clients to run the virtual machines they want.
➢ Utility computing billing
o Typically set up to bill customers based on how many system resources they use.
➢ E.g., Amazon Web Services (AWS), IBM, Microsoft

Platform as a Service:
In addition to infrastructure-oriented clouds that provide raw computing and storage services, another
approach is to offer a higher level of abstraction to make a cloud easily programmable, known as Platform
as a Service (PaaS). PaaS cloud computing platform is created for the programmer to develop, test, run, and
manage the applications.

PaaS is a category of cloud computing that provides a platform and environment to allow developers to
build applications and services over the internet. PaaS services are hosted in the cloud and accessed by users
simply via their web browser.
A PaaS provider hosts the hardware and software on its own infrastructure. As a result, PaaS frees users
from having to install in-house hardware and software to develop or run a new application. Thus, the
development and deployment of the application take place independent of the hardware.

PaaS provides the runtime environment for applications, development & deployment tools, etc.
PaaS is also known as cloudware as shown in bellow:

PaaS: application development platforms:

➢ The development tool itself is hosted in the cloud and accessed and deployed through the Internet.
➢ PaaS supplies all the resources required to build applications and services completely from the
Internet, without having to download or install software.

Paas Services:
➢ Application development cycle
⚫ Application design,
⚫ development,
⚫ testing,
⚫ deployment, and hosting

PaaS Examples:
➢ Force.com
⚫ Supporting Salesforce, an SaaS
➢ AppEngine
⚫ From Google
Google AppEngine, an example of Platform as a Service, offers a scalable environment for developing and
hosting Web applications, which should be written in specific programming languages such as Python or
Java, and use the services’ own proprietary structured object data store.

Software as a Service
Software-as-a-Service (SaaS) is a way of delivering services and applications over the Internet. Instead of
installing and maintaining software, simply access it via the Internet. SaaS model allows to use software
applications as a service to end users.
Applications reside on the top of the cloud stack. Services provided by this layer can be accessed by end
users through Web portals. Therefore, consumers are increasingly shifting from locally installed computer
programs to on-line software services that offer the same functionally. Traditional desktop applications such
as word processing and spreadsheet can now be accessed as a service in the Web.
The model in which an application is hosted as a service to customers who access it via the Internet as shown
in the following figure:
SaaS provides a complete software solution that purchase on a pay-as-you-go basis from a cloud service
provider. Most SaaS applications can be run directly from a web browser without any downloads or
installations required. The SaaS applications are sometimes called Web-based software, on-demand
software, or hosted software.
Saas Software characteristics:
➢ Ideal SaaS software
o Performing a simple task
o Without much need to interact with other systems
o E.g., Google Docs, Evernote Web Clipper
➢ Some high-powered SaaS applications
o Customer resource management (CRM)
o Video conferencing, IT service management
o Accounting, Web analytics, Web content management
➢ Being web-native
o Allow multiple customers to use an application wherever they have web access

Explain deployment models of cloud computing in detail?

7
Cloud deployment models:
Cloud computing is Internet-based computing in which a shared pool of resources is available over broad
network access, these resources can be provisioned or released with minimum management efforts and
service-provider interaction.

Types of Cloud
1. Public cloud
2. Private cloud
3. Hybrid cloud
4. Community cloud

Deployment models define the type of access to the cloud, i.e., how the cloud is located? Cloud can have
any of the four types of access: Public, Private, Hybrid and Community as shown in bellow:

PUBLIC CLOUD:

The Public Cloud allows systems and services to be easily accessible to the general public. Public cloud
may be less secure because of its openness, e.g., e-mail.
o Public Cloud provides a shared platform that is accessible to the general public through an
Internet connection.
o Public cloud operated on the pay-as-per-use model and administrated by the third party, i.e.,
Cloud service provider.
o In the Public cloud, the same storage is being used by multiple users at the same time.
o Public cloud is owned, managed, and operated by businesses, universities, government
organizations, or a combination of them.
o Amazon Elastic Compute Cloud (EC2), Microsoft Azure, IBM's Blue Cloud, Sun Cloud, and
Google Cloud are examples of the public cloud.
The fundamental characteristics of public clouds are multi-tenancy. A public cloud is meant to serve
multiple users, not a single customer. A user requires a virtual computing environment that is separated,
and most likely isolated, from other users.
Advantages of using a Public cloud are:
1. High Scalability
2. Cost Reduction
3. Reliability and flexibility
4. Disaster Recovery

Disadvantages of using a Public cloud are:

1. Loss of control over data
2. Data security and privacy
3. Limited Visibility
4. Unpredictable cost

PRIVATE CLOUD:

The Private Cloud allows systems and services to be accessible within an organization. It offers
increased security because of its private nature.
o Private cloud provides computing services to a private internal network (within
the organization) and selected users instead of the general public.
o Private cloud provides a high level of security and privacy to data through firewalls and
internal hosting. It also ensures that operational and sensitive data are not accessible to third-party
providers.
o HP Data Centers, Microsoft, Elastra-private cloud, and Ubuntu are the example of a private cloud.
 Advantages of Private cloud:

1) More Control-Private clouds have more control over their resources and hardware than public clouds
because it is only accessed by selected users.
2) Security & privacy-Security & privacy are one of the big advantages of cloud computing. Private
cloud improved the security level as compared to the public cloud.
3) Improved performance-Private cloud offers better performance with improved speed and space
capacity.

Disadvantages of Private Cloud:

1) High cost-The cost is higher than a public cloud because set up and maintain hardware resources are
costly.
2) Restricted area of operations- Private cloud is accessible within the organization, so the area of
operations is limited.
3) Limited scalability-Private clouds are scaled only within the capacity of internal hosted resources.
4) Skilled people-Skilled people are required to manage and operate cloud services.

HYBRID CLOUD:
The Hybrid Cloud is mixture of public and private cloud. However, the critical activities are performed
using private cloud while the non-critical activities are performed using public cloud.

o Hybrid cloud is a combination of public and private clouds.

Hybrid cloud = public cloud + private cloud

o The main aim to combine these clouds (Public and Private) is to create a unified, automated, and
well- managed computing environment.
o In the Hybrid cloud, non-critical activities are performed by the public cloud and critical
activities are performed by the private cloud.
o Mainly, a hybrid cloud is used in finance, healthcare, and Universities.
o The best hybrid cloud provider companies are Amazon, Microsoft, Google, Cisco, and NetApp.
 Advantages of Hybrid Cloud:

1) Flexible and secure-It provides flexible resources because of the public cloud and secure resources
because of the private cloud.
2) Cost effective-It offers the features of both the public as well as the private cloud. A hybrid cloud
is capable of adapting to the demands that each company needs for space, memory, and system.
3) Security-Hybrid cloud is secure because critical activities are performed by the private cloud.
4) Risk Management-Hybrid cloud provides an excellent way for companies to manage the risk.

Disadvantages of Hybrid Cloud:

1) Networking issues-In the Hybrid Cloud, networking becomes complex because of the private and the
public cloud.
2) Infrastructure Compatibility-Infrastructure compatibility is the major issue in a hybrid cloud. With
dual-levels of infrastructure, a private cloud controls the company, and a public cloud does not, so there is
a possibility that they are running in separate stacks.
3) Reliability-The reliability of the services depends on cloud service providers.

COMMUNITY CLOUD:
The Community Cloud allows systems and services to be accessible by group of organizations.
Community cloud is a cloud infrastructure that allows systems and services to be accessible by a group of
several organizations to share the information. It is owned, managed, and operated by one or more
organizations in the community, a third party, or a combination of them.
 Advantages of Community Cloud:

Cost effective
Community cloud is cost effective because the whole cloud is shared between several organizations
or a community.
Flexible and Scalable
The community cloud is flexible and scalable because it is compatible with every user. It allows the
users to modify the documents as per their needs and requirement.
Security
Community cloud is more secure than the public cloud but less secure than the private cloud.
Sharing infrastructure
Community cloud allows us to share cloud resources, infrastructure, and other capabilities among
various organizations.

Disadvantages of Community Cloud

There are the following disadvantages of Community Cloud -

o Community cloud is not a good choice for every organization.
o Slow adoption to data
o The fixed amount of data storage and bandwidth is shared among all community members.
o Community Cloud is costly than the public cloud.
o Sharing responsibilities among organizations is difficult.

What is a Cloud computing and write advantages and disadvantages of cloud

8 computing?

Cloud Computing is defined as storing and accessing of data and computing services over the internet. It
doesn’t store any data on your personal computer. It is the on-demand availability of computer services like
servers, data storage, networking, databases, etc.
The main purpose of cloud computing is to give access to data centers to many users. Users can also access
data from a remote server.
Examples of Cloud Computing Services: AWS, Azure, Google

Advantages of Cloud Computing:

Scalability: One of the best advantages of cloud computing is scalability. Maintaining a business,
organization, or another element is trying in ideal circumstances. Cloud Computing provides the
opportunity to scale at your own speed. Cloud computing is a business resource you pay for just as and
when you want it.

Back-up and restore data-Once the data is stored in the cloud, it is easier to get back-up and restore
that data using the cloud.

Improved collaboration-Cloud applications improve collaboration by allowing groups of people to

quickly and easily share information in the cloud via shared storage.

Excellent accessibility-Cloud allows us to quickly and easily access store information anywhere, anytime
in the whole world, using an internet connection. An internet cloud infrastructure increases organization
productivity and efficiency by ensuring that our data is always accessible.

Low maintenance cost-Cloud computing reduces both hardware and software maintenance costs for
organizations.

Unlimited storage capacity-Cloud offers us a huge amount of storing capacity for storing our important
data such as documents, images, audio, video, etc. in one place.

Data security-Data security is one of the biggest advantages of cloud computing. Cloud offers many
advanced features related to security and ensures that data is securely stored and handled.

Accessible to modern technology: Cloud computing is far more than an internet-based storage service for
data. Organizations worldwide currently use cutting-edge technologies they need to get done with their
responsibilities and run their business over the web utilizing the cloud. Some technology available on a
cloud platform includes Artificial Intelligence and Machine Learning, Data Analytics, Data Visualization,
Containerization, etc.

Cheaper: The cloud computing model is based on the ‘pay-as-you-go’ principle and offers a possibly less
expensive way for organizations to remain coordinated and online.
Mobility: One of the main advantages of cloud computing is mobility. Employees have the option to
compute heavy tasks from anywhere.

Prediction ability: Data analytics deserves more consideration. Cloud computing has accomplished more
powerful predictive analytics than other technologies.

Disadvantages of Cloud Computing:

A list of the disadvantage of cloud computing is given below -

Internet Connectivity-In cloud computing, every data (image, audio, video, etc.) is stored on the cloud,
and we access these data through the cloud by using the internet connection. If you do not have good internet
connectivity, you cannot access these data. However, we have no any other way to access data from
the cloud.

Vendor lock-in-Vendor lock-in is the biggest disadvantage of cloud computing. Organizations may face
problems when transferring their services from one vendor to another. As different vendors provide different
platforms, that can cause difficulty moving from one cloud to another.

Limited Control- Cloud infrastructure is completely owned, managed, and monitored by the service
provider, so the cloud users have less control over the function and execution of services within a cloud
infrastructure.

Security- Although cloud service providers implement the best security standards to store important
information. But, before adopting cloud technology, you should be aware that you will be sending all your
organization's sensitive information to a third party, i.e., a cloud computing service provider. While
sending the data on the cloud, there may be a chance that your organization's information is hacked by
Hackers.

Technical Issues:
Cloud technologies have technical issues. Even, the best cloud service provider companies may face this
type of trouble despite maintaining high standards of maintenance.

Explain the following services

9 (i) IaaS
(ii) Paas
(iii) SaaS

Explain the following Models

10 (i)Private Cloud
(ii)Public Cloud
(ii) Hybrid Cloud
(iv)Community Cloud

(a) What is fog computing and explain the benefits of fog computing?
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(b) Write the Pros and Cons of fog computing

Explain the differences between cloud computing and fog computing

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