Introduction to Cloud Computing

1. UNIT I INTRODUCTION
Introduction to Cloud Computing – Definition
of Cloud – Evolution of Cloud Computing –
Underlying Principles of Parallel and
Distributed Computing – Cloud
Characteristics – Elasticity in Cloud –
Ondemand Provisioning.

2. What is Cloud?
• The term Cloud refers to Network or Internet.
• Cloud can provide services over public and
private networks, i.e., WAN, LAN or VPN.
• Applications such as e-mail, web conferencing,
customer relationship management (CRM)
execute on cloud.

3. What is Cloud Computing?
• Cloud computing is a service, which offers
customers to work over the internet.
• Cloud computing means storing and accessing
the data and programs over the internet rather
than the computer’s hard disk.
• Cloud Computing refers to manipulating,
configuring, and accessing the hardware and
software resources remotely. It offers online data
storage, infrastructure, and application.

5. Benefits of Cloud Computing

6. Disadvantages of Cloud Computing

7. Characteristics

8. Characteristics
• On-demand - Resources should be always available when
you need them, and you have control over turning them
on or off to ensure there’s no lack of resource or wastage
happen.
• Scalable - You should be able to scale (increase or
decrease the resource) when necessary. The cloud
providers should have sufficient capacity to meet
customer’s needs.
• Multi-tenant - Sometimes you may be sharing the same
resource (e.g. hardware) with another tenant. But of
course, this is transparent to the customer. Cloud provider
shall responsible the security aspect, ensuring that one
tenant won’t be able to access other’s data.

9. Characteristics
• Self-service computation and storage resource -
Related processes including: billing, resource
provisioning, and deployment should be self-service
and automated, involving much less manual
processing. If a machine where our service is hosted
fails, the cloud provider should be able to failover our
service immediately.
• Reliability - Cloud provider should be able to provide
customer reliability service, committing to uptimes of
their service.
• Utility-based subscription - You will pay the cloud
provider as a utility based subscription, just like paying
your electricity bill – without any upfront investment.

11. Distributed Systems
• It is a composition of multiple independent systems but
all of them are depicted as a single entity to the users.
• The purpose of distributed systems is to share resources
and also use them effectively and efficiently.
• Distributed systems possess characteristics such as
scalability, concurrency, continuous availability,
heterogeneity, and independence in failures.
• But the main problem with this system was that all the
systems were required to be present at the same
geographical location.
• Thus to solve this problem, distributed computing led to
three more types of computing and they were
– Mainframe computing, cluster computing, and grid
computing.

12. Mainframe computing
• Mainframes which first came into existence in 1951 are
highly powerful and reliable computing machines.
• These are responsible for handling large data such as
massive input-output operations.
• Even today these are used for bulk processing tasks such
as online transactions etc.
• These systems have almost no downtime with high fault
tolerance.
• After distributed computing, these increased the
processing capabilities of the system. But these were very
expensive.
• To reduce this cost, cluster computing came as an
alternative to mainframe technology.

13. Cluster computing
• In 1980s, cluster computing came as an alternative to
mainframe computing.
• Each machine in the cluster was connected to each other
by a network with high bandwidth.
• These were way cheaper than those mainframe systems.
• These were equally capable of high computations.
• Also, new nodes could easily be added to the cluster if it
was required.
• Thus, the problem of the cost was solved to some extent
but the problem related to geographical restrictions still
pertained.
• To solve this, the concept of grid computing was
introduced.

14. Grid computing
• In 1990s, the concept of grid computing was introduced.
• It means that different systems were placed at entirely
different geographical locations and these all were
connected via the internet.
• These systems belonged to different organizations and
thus the grid consisted of heterogeneous nodes.
• Although it solved some problems but new problems
emerged as the distance between the nodes increased.
• The main problem which was encountered was the low
availability of high bandwidth connectivity and with it
other network associated issues.
• Thus. cloud computing is often referred to as “Successor
of grid computing”.

15. Virtualization
• It was introduced nearly 40 years back.
• It refers to the process of creating a virtual layer
over the hardware which allows the user to run
multiple instances simultaneously on the
hardware.
• It is a key technology used in cloud computing.
• It is the base on which major cloud computing
services such as Amazon EC2, VMware vCloud, etc
work on.
• Hardware virtualization is still one of the most
common types of virtualization.

16. Web 2.0
• It is the interface through which the cloud
computing services interact with the clients.
• It is because of Web 2.0 that we have interactive
and dynamic web pages.
• It also increases flexibility among web pages.
Popular examples of web 2.0 include Google
Maps, Facebook, Twitter, etc.
• Needless to say, social media is possible because
of this technology only.
• In gained major popularity in 2004.

17. Service orientation
• It acts as a reference model for cloud computing.
• It supports low-cost, flexible, and evolvable
applications.
• Two important concepts were introduced in this
computing model.
• These were Quality of Service (QoS) which also
includes the SLA (Service Level Agreement) and
Software as a Service (SaaS).

18. Utility computing
• It is a computing model that defines service
provisioning techniques for services such as
compute services along with other major services
such as storage, infrastructure, etc which are
provisioned on a pay-per-use basis.

19. Underlying Principles of Parallel and
Distributed Computing System

20. Parallel Computing VS Distributed computing
• Parallel computing is the
process of performing
computational tasks across
multiple processors at
once to improve computing
speed and efficiency.
• It divides tasks into sub-
tasks and executes them
simultaneously through
different processors.
• Distributed computing is
the process of connecting
multiple computers via a
local network or wide area
network
• So that they can act
together as a single ultra-
powerful computer capable
of performing
computations that no single
computer within the
network would be able to
perform on its own.

21. Parallel Computing
Advantages
• It saves time and money because many resources working
together cut down on time and costs.
• It may be difficult to resolve larger problems on Serial
Computing.
• You can do many things at once using many computing
resources.
• Parallel computing is much better than serial computing for
modeling, simulating, and comprehending complicated real-
world events.
Disadvantages
• The multi-core architectures consume a lot of power.
• Parallel solutions are more difficult to implement, debug, and
prove right due to the complexity of communication and
coordination, and they frequently perform worse than their
serial equivalents.

22. Distributed Computing
Advantages
• It is flexible, making it simple to install, use, and debug new services.
• In distributed computing, you may add multiple machines as
required.
• If the system crashes on one server, that doesn't affect other servers.
• A distributed computer system may combine the computational
capacity of several computers, making it faster than traditional
systems.
Disadvantages
• Data security and sharing are the main issues in distributed systems
due to the features of open systems
• Because of the distribution across multiple servers, troubleshooting
and diagnostics are more challenging.
• The main disadvantage of distributed computer systems is the lack of
software support.

23. Features Parallel Computing Distributed Computing
Definition It is a type of computation
in which various processes
runs simultaneously.
It is that type of
computing in which the
components are located
on various networked
systems that interact and
coordinate their actions
by passing messages to
one another.
Communicati
on
The processors
communicate with one
another via a bus.
The computer systems
connect with one another
via a network.
Functionality Several processors execute
various tasks
simultaneously in parallel
computing.
Several computers
execute tasks
simultaneously.

24. Features Parallel Computing Distributed Computing
Number of
Computers
It occurs in a single
computer system.
It involves various
computers.
Memory The system may have
distributed or shared
memory.
Each computer system in
distributed computing
has its own memory.
Usage It helps to improve the
system performance
It allows for scalability,
resource sharing, and the
efficient completion of
computation tasks.

25. Cloud Elasticity
• Cloud Elasticity is the property of a cloud to grow or
shrink capacity for CPU, memory, and storage
resources to adapt to the changing demands of an
organization.
• This is not applicable for all kind of environment, it is
helpful to address only those scenarios where the
resources requirements fluctuate up and down
suddenly for a specific time interval.
• It is most commonly used in pay-per-use, public
cloud services. Where IT managers are willing to pay
only for the duration to which they consumed the
resources.

26. Benefit of elasticity in cloud computing
• Cloud elasticity helps users prevent over-
provisioning or under-provisioning system
resources.
• Over-provisioning refers to a scenario where
you buy more capacity than you need.
• Under-provisioning refers to allocating fewer
resources than you use.
• This elasticity helps to minimize infrastructural
cost.

27. Cloud scalability
• In cloud computing, the term cloud scalability
refers to the capacity to improve or reduce IT
resources, depending on the requirement
changing demand.
• In other words, we can say that scalability is
employed to satisfy the static growth in the
workload.

28. Cloud Elasticity Cloud Scalability
It is used just to fulfil the
sudden requirement in the
workload for a short period.
It is used to fulfil the static boost in
the workload.
It is preferred to satisfy the
dynamic modifications, where
the required resources can
improve or reduce.
It is preferred to handle growth in the
workload in an organisation.
Cloud elasticity is generally
used by small enterprises
whose workload expands only
for a specific period.
Cloud scalability is utilised by big
enterprises.
It is a short term event that is
used to deal with an unplanned
or sudden growth in demand.
It is a long term event that is used to
deal with an expected growth in
demand.

29. Ondemand Provisioning
• On-demand computing (ODC) is a
delivery model in which computing
resources are made available to the
user as needed.
• The resources may be maintained
within the user's enterprise or made
available by a cloud service provider.

30. Ondemand Provisioning
• Cloud provisioning is the allocation of
resources and services from a cloud provider
to a customer.
• It is a key feature of cloud computing.
• It refers to how a client gets cloud services
and resources from a provider.

31. Ondemand Provisioning
• The growing catalog of cloud services that
customers can provision includes
infrastructure as a service, software as a
service and platform as a service, in public or
private cloud environments.

32. Cloud Provisioning Types
• There are various cloud provisioning delivery
models.
• Each model depends on the types of resources or
services an organization purchases, how and
when the cloud service provider delivers them,
and how customers pay for them.
• The three models
– Advanced
– Dynamic
– User self-provisioning

33. Advanced Cloud Provisioning
• Also known as “post-sales cloud provisioning”
customers get the resources upon contract or
service signup.
• They sign formal contracts with the cloud service
provider.
• The provider then prepares and delivers the
agreed-upon resources or services.
• The customers are charged a flat fee or billed
every month.

34. Dynamic Cloud Provisioning
• Also referred to as “on-demand cloud provisioning,”
customers are provided with resources on runtime.
• In this delivery model, cloud resources are deployed
to match customers’ fluctuating demands.
• Deployments can scale up to accommodate spikes in
usage and down when demands decrease.
• Customers are billed on a pay-per-use basis.
• When this model is used to create a hybrid cloud
environment, it is sometimes called “cloud bursting.”

35. User Cloud Provisioning
• In this delivery model, customers add a cloud device
themselves.
• Also known as “cloud self-service,” clients buy
resources from the cloud service provider through a
web interface or portal.
• The model usually involves creating a user account
and paying for resources with a credit card.
• The resources are quickly spun up and made
available for use within hours, if not minutes.
• An example of this includes an employee purchasing
cloud-based productivity applications via Microsoft
365 or G Suite.

36. Cloud Provisioning Benefits
• Scalability:
– Lets companies simply scale up and down their cloud
resources depending on their short-term usage
requirements.
• Speed:
– Organizations’ developers can quickly spin up several
workloads on-demand, so the companies no longer
require IT administrators to provide and manage
computing resources.
• Cost savings:
– While traditional on-premises technology requires large
upfront investments, many cloud service providers let
their customers pay for only what they consume.

37. Cloud Provisioning Challenges
• Complex management and monitoring:
• Resource and service dependencies:
• Policy enforcement

38. • Complex management and monitoring:
– Organizations may need several provisioning tools to customize their
cloud resources.
– Many also deploy workloads on more than one cloud platform,
making viewing everything on a central console more challenging.
• Resource and service dependencies:
– Cloud applications and workloads often tap into basic infrastructure
resources, such as computing, networking, and storage.
– But public cloud service providers offer higher-level ancillary services
like serverless functions and machine learning (ML) and big data
capabilities.
– Such services may carry dependencies that can lead to unexpected
overuse and surprise costs.
• Policy enforcement:
– User cloud provisioning helps streamline requests and manage
resources but requires strict rules to make sure unnecessary
resources are not provided.
– That is time-consuming since different users require varying levels of
access and frequency.
– Setting up rules to know who can provide which resources, for how
long, and with what budgetary controls can be difficult.