Unit I

Introduction to Distributed
Computing
Prepared By.
Mrs.Rajashree L. Ghule
What is a Distributed System?

• Distributed System is a collection of autonomous computer systems

that are physically separated but are connected by a centralized
computer network that is equipped with distributed system software.
• The autonomous computers will communicate among each system by
sharing resources and files and performing the tasks assigned to
them.
• A distributed system in its most simplest definition is a group of
computers working together as to appear as a single computer to the
end-user.
Continue…..
• Distributed computing refers to the use of multiple interconnected
computers or nodes that work together to solve a computational
problem or perform a task.
• In a distributed computing environment, the processing workload,
data storage, and other computing resources are distributed across a
network of computers, allowing them to collaborate and share
resources.
• The primary goal of distributed computing is to achieve improved
performance, scalability, fault tolerance, and resource utilization by
harnessing the collective power of multiple machines.
Example of Distributed System:

• Mobile and web applications are examples of distributed computing

because several machines work together in the backend for the
application to give you the correct information.
• However, when distributed systems are scaled up, they can solve
more complex challenges.
• Any Social Media can have its Centralized Computer Network as its
Headquarters and computer systems that can be accessed by any
user and using their services will be the Autonomous Systems in the
Distributed System Architecture.
• Distributed System Software: This Software enables computers to coordinate
their activities and to share the resources such as Hardware, Software, Data,
etc.
• Database: It is used to store the processed data that are processed by each
Node/System of the Distributed systems that are connected to the Centralized
network.
• As we can see that each Autonomous System has a common Application that
can have its own data that is shared by the Centralized Database System.
• To Transfer the Data to Autonomous Systems, Centralized System should be
having a Middleware Service and should be connected to a Network.
• Middleware Services enable some services which are not present in the local
systems or centralized system default by acting as an interface between the
Centralized System and the local systems. By using components of Middleware
Services systems communicate and manage data.
• The Data which is been transferred through the database will be divided into
segments or modules and shared with Autonomous systems for processing.
• The Data will be processed and then will be transferred to the Centralized
system through the network and will be stored in the database.
• Let’s go with a database! Traditional databases are stored on the file system of one single
machine, whenever you want to fetch/insert information in it — you talk to that machine
directly.
• Distributed System Use Three Function:
1. Network
2. Distributed Software
3. Middleware Services
Architecture of Distributed Systems
• A distributed system is broadly divided into two essential concepts —
software architecture (further divided into layered architecture, object-
based architecture, data-centered architecture, and event-based
architecture) and system architecture (further divided into client-server
architecture and peer-to-peer architecture).
1. Software architecture

• Software architecture is the logical organization of software

components and their interaction with other structures.
• It is at a lower level than system architecture and focuses entirely on
components; e.g., the web front end of an ecommerce system is a
component.
• The four main architectural styles of distributed systems in software
components entail:
I) Layered architecture
• Layered architecture provides a modular approach to software. By
separating each component, it is more efficient.
• For example, the open systems interconnection (OSI) model uses a
layered architecture for better results.
• It does this by contacting layers in sequence, which allows it to reach
its goal.
• In some instances, the implementation of layered architecture is in
cross-layer coordination.
• Under cross-layer, the interactions can skip any adjacent layer until it
fulfills the request and provides better performance results.
1. Layered architecture is a type of software that separates components into units.
A request goes from the top down, and the response goes from the bottom up.
The advantage of layered architecture is that it keeps things orderly and modifies each layer
independently without affecting the rest of the system.
ii) Object-based architecture
• Object-based architecture centers around an arrangement of loosely
coupled objects with no specific architecture like layers.
• Unlike layered architecture, object-based architecture doesn’t have
to follow any steps in a sequence.
• Each component is an object, and all the objects can interact through
an interface (or connector).
• Under object-based architecture, such interactions between
components can happen through a direct method call.
• At its core, communication between objects happens through method invocations, often called
remote procedure calls (RPC).
Popular RPC systems include Java RMI and Web Services and REST API(Architectural Style) Calls.
The primary design consideration of these architectures is that they are less structured.
Here, component equals object, and connector equals RPC or RMI.
iii) Data-centered architecture

• Data-centered architecture works on a central data repository, either

active or passive.
• Like most producer-consumer scenarios, the producer (business)
produces items to the common data store, and the consumer
(individual) can request data from it.
• Sometimes, this central repository can be just a simple database.
• All communication between objects happens through a data storage system in a data-centered system.
It supports its stores’ components with a persistent storage space such as an SQL database, and the system
stores all the nodes in this data storage.
iv) Event-based architecture
• In event-based architecture, the entire communication is through
events. When an event occurs, the system gets the notification.
• This means that anyone who receives this event will also be notified
and has access to information.
• Sometimes, these events are data, and at other times they are URLs
to resources.
• As such, the receiver can process what information they receive and
act accordingly.
• One significant advantage of event-based architecture is that the components are loosely coupled.
Eventually, it means that it’s easy to add, remove, and modify them.
To better understand this, think of publisher-subscriber systems, enterprise services buses.
One advantage of event-based architecture is allowing heterogeneous components to
communicate with the bus, regardless of their communication protocols.
2. System architecture
• System-level architecture focuses on the entire system and the
placement of components of a distributed system across multiple
machines.
• The client-server architecture and peer-to-peer architecture are the
two major system-level architectures that hold significance today.
• An example would be an ecommerce system that contains a service
layer, a database, and a web front.
i) Client-server architecture
• As the name suggests, client-server architecture consists of a client
and a server.
• The server is where all the work processes are, while the client is
where the user interacts with the service and other resources
(remote server).
• The client can then request from the server, and the server will
respond accordingly.
• Typically, only one server handles the remote side; however, using
multiple servers ensures total safety.
Client-server architecture has one standard design feature: centralized
security.
• Data such as usernames and passwords are stored in a secure
database for any server user to have access to this information. This
makes it more stable and secure than peer-to-peer.
• This stability comes from client-server architecture, where the
security database can allow resource usage in a more meaningful way.
• The system is much more stable and secure, even though it isn’t as
fast as a server.
• The disadvantages of a distributed system are its single point of
failure and not being as scalable as a server.
ii) Peer-to-peer (P2P) architecture
• A peer-to-peer network, also called a (P2P) network, works on the
concept of no central control in a distributed system.
• A node can either act as a client or server at any given time once it
joins the network.
• A node that requests something is called a client, and one that
provides something is called a server.
• In general, each node is called a peer.
• If a new node wishes to provide services, it can do so in two ways.
• One way is to register with a centralized lookup server, which will
then direct the node to the service provider.
• The other way is for the node to broadcast its service request to every
other node in the network, and whichever node responds will provide
the requested service.
P2P networks of today have three separate sections:
• Structured P2P: The nodes in structured P2P follow a predefined
distributed data structure.
• Unstructured P2P: The nodes in unstructured P2P randomly select
their neighbors.
• Hybrid P2P: In a hybrid P2P, some nodes have unique functions
appointed to them in an orderly manner.
Characteristics of Distributed System:
Transparency
• Its hide a complexity of DS from user and application program.
• One of the essential characteristics of the distributed system, transparency, is the notion that the
user interacts with a whole quantity rather than a cluster of cooperating elements. A system capable
of presenting itself as a whole to the user is called transparent.
• Transparency is divided into the eight sub-characteristics illustrated in the following table:
Transparency Description

Access Hide differences in data representation and object

accessibility.

Location Hide the object’s location.

Relocation Hide the location of the moving object while still

in use.

Migration Hide that an object may move to another location

Replication Hide replication of the object

Concurrency Hide that an object may have a shared databases

access

Failure Hide any resource failures.

Persistence Hide the fact about memory location.

Heterogeneity
• Heterogeneity refers to the system's ability to operate on various hardware and software
components. Middleware in the software layer helps achieve heterogeneity.
• The goal of the middleware is to interpret the programming calls such that the distributed
processing gets completed.
Openness
• Another important characteristic of Distributed system is openness. A distributed system's
openness is the difficulty in extending or improving an existing system. In order to make an open
distributed system,
• The interface of the components should be well-defined and precise.
• The interface of the components should be standardized.
• Integration of new components with existing ones must be effortless.
Scalability
• In terms of effectiveness, scalability is one of the significant characteristics of distributed
systems.
• It refers to the ability of the system to handle growth as the number of users increases.
• Scalability is accomplished by adding more computer systems to the existing networks.
• A centralized system affects the scalability of a distributive system. If a system is centralised, more
nodes will try to communicate, which results in a bottleneck at that particular time in the system.
Fault Tolerance
• A distributed system is very likely to be prone to system failures. This is due to the fact that several computers have diverse-aged
hardware. The ability of a system to handle these failures is called fault tolerance. Fault tolerance is achieved by:
Recovery:
• Systems and processes will have a stored backup. It takes over when the system fails.
Redundancy: When a component acts predictable and in a controlled way is called redundancy. Concurrency
• Concurrency is the system's capability to access and use shared resources. It means multiple actions are performed at the
same time.
• In distributive systems, the concurrent execution of activities takes place in different components running on numerous
machines.
Efficiency
• Efficiency refers to the capability of the system to use its resources effectively to execute the given tasks. The system's
design, the workload handled by the system, and the hardware and software resources used are some critical factors
affecting the system's efficiency.
Some of the common ways to improve the efficiency of the system are:
• Optimizing the design of the system. This minimizes the amount of communication and coordination required between
the different components, reducing any extra power consumption.
• Carefully negating the workload of the system. This balance avoids any component overload and ensures that the system
can make the most efficient use of its resources.
Resource Sharing:
• It is the ability to use any Hardware, Software, or Data anywhere in the System.
Concurrency:
• It is naturally present in Distributed Systems, that deal with the same activity or functionality that can be performed by separate
users who are in remote locations.
• Every local system has its independent Operating Systems and Resources.
Advantages of Distributed System:

• Applications in Distributed Systems are Inherently Distributed Applications.

• Information in Distributed Systems is shared among geographically
distributed users.
• Resource Sharing (Autonomous systems can share resources from remote
locations).
• It has a better price performance ratio and flexibility.
• It has shorter response time and higher throughput.
• It has higher reliability and availability against component failure.
• It has extensibility so that systems can be extended in more remote
locations and also incremental growth.
Disadvantages of Distributed System:

• Relevant Software for Distributed systems does not exist currently.

• Security possess a problem due to easy access to data as the resources are
shared to multiple systems.
• Networking Saturation may cause a hurdle in data transfer i.e., if there is a
lag in the network then the user will face a problem accessing data.
• In comparison to a single user system, the database associated with
distributed systems is much more complex and challenging to manage.
• If every node in a distributed system tries to send data at once, the
network may become overloaded.
Applications Area of Distributed System:

• Finance and Commerce: Amazon, eBay, Online Banking, E-Commerce

websites.
• Information Society: Search Engines, Wikipedia, Social Networking, Cloud
Computing.
• Cloud Technologies: AWS, Salesforce, Microsoft Azure, SAP.
• Entertainment: Online Gaming, Music, youtube.
• Healthcare: Online patient records, Health Informatics.
• Education: E-learning.
• Transport and logistics: GPS, Google Maps.
• Environment Management: Sensor technologies.
Challenges of Distributed Systems:
While distributed systems offer many advantages, they also present
some challenges that must be addressed. These challenges include:
• Network latency: The communication network in a distributed system
can introduce latency, which can affect the performance of the
system.
• Distributed coordination: Distributed systems require coordination
among the nodes, which can be challenging due to the distributed
nature of the system.
• Security: Distributed systems are more vulnerable to security threats
than centralized systems due to the distributed nature of the system.
• Data consistency: Maintaining data consistency across multiple nodes
in a distributed system can be challenging.
Issues in Distributed Systems:
• Heterogeneity
• Scalability
• Openness
• Transparency
• Concurrency
• Security
• Failure Handling
 Heterogeneity
• Heterogeneity refers to the differences that arise in networks, programming languages,
hardware, operating systems and differences in software implementation.
• For example, there are different hardware devices, tablets, mobile phones, computers,
and etc.
• Some challenges may present themselves due to heterogeneity.
• When programs are written in different languages or developers utilize different
implementations (data structures, etc.) problems will arise when the computers try to
communicate with each other.
• Thus it is important to have common standards agreed upon and adopted to streamline
the process.
• Additionally, when we consider mobile code - code that can be transferred from one
computer to the next - we may encounter some problems if the executables are not
specified to accommodate both computers' instructions and specifications.
Scalability
• A program is scalable if a program does not need to be redesigned to
ensure stability and consistent performance as its workload increases.
• As such, a program (distributed system in our case) should not have a
change in performance regardless of whether it has 10 nodes or 100 nodes.
• As a distributed system is scaled, several factors need to be taken into
account: size, geography, and administration.
• The associated problem with size is overloading. Overloading refers to the
degradation of the system that refers as the system's workload increases
(increase in number of users, resourced consumed, etc.).
• Secondly, with geography, as the distance that our distributed system
encompasses increases, the reliability of our communication may break
down. Additionally, as a distributed system is scaled, we may have to
implement controls in the system; however, this may devolve into what we
can effectively call an administrative mess.
Openness
• The openness of distributed systems refers to the system's extensibility and
ability to be reimplemented.
• More specifically, the openness of a distributed system can be measured by
three characteristics:
• interoperability, portability, and extensibility as we previously mentioned.
• Interoperability refers to the system's ability to effectively interchange
information between computers by standardization,
• portability refers to the system's ability to properly function on different
operating systems,
• and extensibility allows developers to freely add new features or easily
reimplement existing ones without impairing functionality.
• Additionally, open distributed systems implement open interfaces, which
comes with many challenges - in this case having well-defined interfaces
may present themselves as challenges.
Transparency

• A problem with transparency may arise with distributed systems due

to the nature of the system's complexity.
• In this context, transparency refers to the distributed system's ability
to conceal its complexity and give off the appearance of a single
system.
• And when we discuss transparency, we must also discuss to what
extent.
Concurrency

• This discusses the shared access of resources which must be made

available to the correct processes.
• Problems may arise when multiple processes attempt to access the
same resources at the same time, thus steps need to be taken to
ensure that any manipulation in the system remains in a stable state;
however the illusion of simultaneous execution should be preserved.
• We refer to these preventative measure as concurrency control.
• Concurrency control should be implemented to ensure that processes
are executed in a synchronous manner.
Security

• Security is comprised of three key components: availability, integrity, and

confidentiality.
• In a similar fashion, authentication and authorization are paramount - an
entity must be verifiably authenticated as claimed and privileges must be
appropriate delegated based on authority.
• These concepts are related, availability regards the authenticated and
authorized users, integrity protects through encryption and other methods,
and confidentiality ensures that resources are not needlessly disclosed or
made available.
• Security is especially important in distributive systems due to their
association with sensitive and private data.
• Take payment and transactions information, for example.
Failure Handling

• Failures, like in any program, are a major problem.

• However, in distributive systems, with so many processes and users, the
consequences of failures are exacerbated.
• Additionally, many problems will arise due to the nature of distributive systems.
• Unexpected edge cases may present themselves in which the system is ill-
equipped for, but developers must account for.
• Failures can occur in the software, hardware, and in the network; additionally
the failure can be partial, causing some components to function and other to not.
• However, the most important part in failure handling is recognizing that not every
failure can be accounted for.
• Thus, implementing processes to detect, monitor, and repair systems failures is a
core feature in failure handling/ management.
 Goals Distributed Systems:
• Support of heterogeneous hardware and software in distributed
system.
• Resource are easily accessible across the networks.
• Distributed system should be scalable.
• The system follows open standards so that they use standard syntax
and semantics.
• The system should be capable to detect and recover from failure it
should be fault tolerant and robust.
Distributed System Models
Client-Server Model

• The Client-server model is a distributed application structure that

partitions task or workload between the providers of a resource or
service, called servers, and service requesters called clients.
• In the client-server architecture, when the client computer sends a
request for data to the server through the internet, the server accepts
the requested process and deliver the data packets requested back to
the client.
• Clients do not share any of their resources.
• Examples of Client-Server Model are Email, World Wide Web, etc.
How the Client-Server Model works ?
• Client: When we talk the word Client, it mean to talk of a person or
an organization using a particular service.
• Similarly in the digital world a Client is a computer (Host) i.e. capable
of receiving information or using a particular service from the service
providers (Servers).
• Servers: Similarly, when we talk the word Servers, It mean a person
or medium that serves something.
• Similarly in this digital world a Server is a remote computer which
provides information (data) or access to particular services.
• So, its basically the Client requesting something and
the Server serving it as long as its present in the database.
Advantages of Client-Server model:

• Centralized system with all data in a single place.

• Cost efficient requires less maintenance cost and Data recovery is
possible.
• The capacity of the Client and Servers can be changed separately.
Disadvantages of Client-Server model:

• Clients are prone to viruses, Trojans and worms if present in the

Server or uploaded into the Server.
• Server are prone to Denial of Service (DOS) attacks.
• Data packets may be spoofed or modified during transmission.
• Phishing or capturing login credentials or other useful information of
the user are common and MITM(Man in the Middle) attacks are
common.
2. Peer-to-Peer Systems
• A peer-to-peer network is a simple network of computers. It first
came into existence in the late 1970s. Here each computer acts as a
node for file sharing within the formed network. Here each node acts
as a server and thus there is no central server in the network. This
allows the sharing of a huge amount of data. The tasks are equally
divided amongst the nodes. Each node connected in the network
shares an equal workload. For the network to stop working, all the
nodes need to individually stop working. This is because each node
works independently.
Types of P2P networks
• Unstructured P2P networks: In this type of P2P network, each device
is able to make an equal contribution. This network is easy to build as
devices can be connected randomly in the network. But being
unstructured, it becomes difficult to find content. For example,
Napster, Gnutella, etc.
• Structured P2P networks: It is designed using software that creates a
virtual layer in order to put the nodes in a specific structure. These
are not easy to set up but can give easy access to users to the
content. For example, P-Grid, Kademlia, etc.
• Hybrid P2P networks: It combines the features of both P2P networks
and client-server architecture. An example of such a network is to
find a node using the central server.
P2P Network Architecture

• In the P2P network architecture, the computers connect with each other in a
workgroup to share files, and access to internet and printers.
• Each computer in the network has the same set of responsibilities and
capabilities.
• Each device in the network serves as both a client and server.
• The architecture is useful in residential areas, small offices, or small companies
where each computer act as an independent workstation and stores the data on
its hard drive.
• Each computer in the network has the ability to share data with other computers
in the network.
• The architecture is usually composed of workgroups of 12 or more computers.
How Does P2P Network Work?

• Let’s understand the working of the Peer-to-Peer network through an

example.
• Suppose, the user wants to download a file through the peer-to-peer
network then the download will be handled in this way:
• If the peer-to-peer software is not already installed, then the user first has
to install the peer-to-peer software on his computer.
• This creates a virtual network of peer-to-peer application users.
• The user then downloads the file, which is received in bits that come from
multiple computers in the network that have already that file.
• The data is also sent from the user’s computer to other computers in the
network that ask for the data that exist on the user’s computer.
Advantages of P2P Network

• Easy to maintain: The network is easy to maintain because each node is

independent of the other.
• Less costly: Since each node acts as a server, therefore the cost of the
central server is saved. Thus, there is no need to buy an expensive server.
• No network manager: In a P2P network since each node manages his or
her own computer, thus there is no need for a network manager.
• Adding nodes is easy: Adding, deleting, and repairing nodes in this
network is easy.
• Less network traffic: In a P2P network, there is less network traffic than in
a client/ server network.
Disadvantages of P2P Network

• Data is vulnerable: Because of no central server, data is always

vulnerable to getting lost because of no backup.
• Less secure: It becomes difficult to secure the complete network
because each node is independent.
• Slow performance: In a P2P network, each computer is accessed by
other computers in the network which slows down the performance
of the user.
• Files hard to locate: In a P2P network, the files are not centrally
stored, rather they are stored on individual computers which makes it
difficult to locate the files.
Middleware
• In distributed systems, middleware is a software component that
provides services between two or more applications and can be used
by them.
• Middleware can be thought of as an application that sits between two
separate applications and provides service to both.
• With all the miscommunication going around these days, it is vital for
enterprises to start using software solutions that streamline
communications across departments.
• One such product that fits this description, is known as Middleware,
which allows organizations to implement their processes seamlessly
by integrating all components of the enterprise.
Example:
• Message-oriented middleware is designed for the purpose of
transporting messages between two or more applications and is best
suited for distributed applications that require transaction-oriented
messaging.
• It could be used to monitor network traffic flows or to monitor the
health of a distributed system.
Key Points of Middleware:

• Distributed systems are becoming increasingly complex as they are

involved in activities spanning an ecosystem of partners, suppliers,
and customers.
• One way to achieve this integration is through the use of middleware.
In recent years, there has been a rise in the number of middleware
solutions available in the market.
• However, due to this array of choices available it has become vital for
enterprises to first identify what type of solution they require before
making a purchasing decision.
Advantages of Middleware in Distributed Systems:

• Middleware is an intermediate layer of software that sits between the

application and the network. It is used in distributed systems to provide
common services, such as authentication, authorization, compilation for
best performance on particular architectures, input/output translation, and
error handling.
• Middleware offers a number of advantages to distributed systems.
Middleware can be modularized from the application so it has better
potential for reuse with other applications running on different platforms.
• Application developers can design Middleware so it’s sufficiently high-level
that it becomes independent of specific hardware environments or
operating system platforms which simplifies porting applications developed
on one type of platform onto another without rewriting code or without
resorting to inefficient and expensive binary compatibility toolsets such as
cross-compilers.
Three-Tier Client Server Architecture:

• The most common type of multi-tier architecture in distributed systems is a

three-tier client-server architecture. In this architecture, the entire
application is organized into three computing tiers
a) Presentation tier
b) Application tier
c) Data-tier
• The major benefit of the three tiers in client-server architecture is that
these tiers are developed and maintained independently and this would
not impact the other tiers in case of any modification.
• It allows for better performance and even more scalability in architecture
can be made as with the increasing demand, more servers can be added.
The Three Tiers In Detail

Presentation Tier
• It is the user interface and topmost tier in the architecture.
• Its purpose is to take request from the client and displays information
to the client.
• It communicates with other tiers using a web browser as it gives
output on the browser.
• If we talk about Web-based tiers then these are developed using
languages like- HTML, CSS, JavaScript.
Application Tier

• It is the middle tier of the architecture also known as the logic tier as
the information/request gathered through the presentation tier is
processed in detail here.
• It also interacts with the server that stores the data.
• It processes the client’s request, formats, it and sends it back to the
client.
• It is developed using languages like- Python, Java, PHP, etc.
Data Tier

• It is the last tier of the architecture also known as the Database Tier.
• It is used to store the processed information so that it can be
retrieved later on when required.
• It consists of Database Servers like- Oracle, MySQL, DB2, etc.
• The communication between the Presentation Tier and Data-Tier is
done using middle-tier i.e. Application Tier.
Tier vs. layer
Tier Layer

Tier refer to the physical operation of components. Layer refers to the logical separation of an application.

Tiers are physical separated and running on the different machines Layers are logically separated but running on the same servers or the
are servers. machines.

Scalability of an application is very high. Scalability of an application is medium.

Common tiers in a multi-tier architecture include the presentation tier

Each layer focuses on specific responsibilities, such as presentation,
(user interface), application tier (business logic), and data tier
business logic, and data access, within a single tier.
(database).
Distributed System Types
Distributed Computing System:
• Cluster Computing:
A Computer Cluster is a local network of two or more homogeneous
computers. A computation process on such a computer network i.e.
cluster is called Cluster Computing.
• Grid Computing:
Grid Computing can be defined as a network of homogeneous or
heterogeneous computers working together over a long distance to
perform a task that would rather be difficult for a single machine.
Cluster Computing:
• Cluster Computing is a collection of connected computers that work
together as a unit to perform operations together, functioning in a
single system.
• Clusters are generally connected quickly via local area networks &
each node is running the same operating system.
• When input comes from a client to the main computer, the master CPU divides the task into
simple jobs and sends it to the slave note to do it when the jobs are done by the slave nodes,
they send it back to the master node, and then it shows the result to the main computer.
Advantages of Cluster Computing

• High Performance
• Easy to manage
• Scalable
• Expandability
• Availability
• Flexibility
• Cost-effectiveness
• Distributed applications
Disadvantages of Cluster Computing

• High cost.
• The problem is finding the fault.
• More space is needed.
• The increased infrastructure is needed.
• In distributed systems, it is challenging to provide adequate security
because both the nodes and the connections must be protected.
Applications of Cluster Computing

• In many web applications functionalities such as Security, Search

Engines, Database servers, web servers, proxy, and email.
• It is flexible to allocate work as small data tasks for processing.
• Assist and help to solve complex computational problems.
• Cluster computing can be used in weather modeling.
• Earthquake, Nuclear, Simulation, and tornado forecast.
Grid Computing:
• In grid computing, the subgroup consists of distributed systems,
which are often set up as a network of computer systems, each
system can belong to a different administrative domain and can differ
greatly in terms of hardware, software, and implementation network
technology.
• The different department has different computer with different OS to make the control
node present which helps different computer with different OS to communicate with
each other and transfer messages to work.
Advantages of Grid Computing

• Can solve bigger and more complex problems in a shorter time frame.
Easier collaboration with other organizations and better use of
existing equipment.
• Existing hardware is used to the fullest.
• Collaboration with organizations made easier
Disadvantages of Grid Computing

• Grid software and standards continue to evolve.

• Getting started learning curve.
• Non-interactive job submission.
• You may need a fast connection between computer resources.
• Licensing on many servers can be prohibitive for some applications.
Applications of Grid Computing
• Organizations that develop grid standards and practices for the guild
line.
• Works as a middleware solution for connecting different businesses.
• It is a solution-based solution that can meet computing, data, and
network needs.
Cluster Computing Grid Computing

Nodes must be homogeneous i.e. they should have same type of hardware and Nodes may have different Operating systems and hardwares. Machines can be
operating system. homogeneous or heterogeneous.

Computers in a grid contribute their unused processing resources to the grid

Computers in a cluster are dedicated to the same work and perform no other task.
computing network.

Computers are located close to each other. Computers may be located at a huge distance from one another.

Computers are connected by a high speed local area network bus. Computers are connected using a low speed bus or the internet.

Computers are connected in a centralized network topology. Computers are connected in a distributed or de-centralized network topology.

Scheduling is controlled by a central server. It may have servers, but mostly each node behaves independently.

Whole system has a centralized resource manager. Every node manages it’s resources independently.

Whole system functions as a single system. Every node is autonomous, and anyone can opt out anytime.

Cluster computing is used in areas such as WebLogic Application Servers, Databases, Grid computing is used in areas such as predictive
etc. modeling, Automation, simulations, etc.

It has Centralized Resource management. It has Distributed Resource Management.

2. Distributed Information System

• Distributed transaction processing: It works across different servers

using multiple communication models. The four characteristics that
transactions have:
• Atomic: the transaction taking place must be indivisible to the others.
• Consistent: The transaction should be consistent after the transaction has
been done.
• Isolated: A transaction must not interfere with another transaction.
• Durable: Once an engaged transaction, the changes are permanent.
Transactions are often constructed as several sub-transactions, jointly forming
a nested transaction.
3. Distributed Pervasive System

• Pervasive Computing is also abbreviated as ubiquitous (Changed and

removed) computing and it is the new step towards integrating
everyday objects with microprocessors so that this information can
communicate.
• A computer system available anywhere in the company or as a
generally available consumer system that looks like that same
everywhere with the same functionality but that operates from
computing power, storage, and locations across the globe.
• Home system: Nowadays many devices used in the home are digital
so we can control them from anywhere and effectively.
• Electronic Health System: Nowadays smart medical wearable devices
are also present through which we can monitor our health regularly.
• Sensor Network (IoT devices): Internet devices only send data to the
client to act according to the data send to the device.
Introduction to Artificial Intelligence and Data
Science in distributed computing:
This combination opens up amazing possibilities across a range of domains, from advancing industry
efficiency to taking on challenging scientific problems. Two quickly developing fields, artificial intelligence
(AI) and data science, use sophisticated computer methods to mine data for insightful information.

Distributing computational tasks

The following are some essential ideas and methods for allocating computing tasks:

1. Workload Division
2. Communication and Synchronization
3. Scheduling and Load Balancing:
4. Orchestration and Fault Tolerance:
Workload Division:
• Workload distribution, also known as load balancing, is a key concept
in distributed software systems.
• It refers to the process of distributing the workload across multiple
resources, such as servers or processors, in order to improve the
overall performance and efficiency of the system
• There are several different strategies that can be used for workload
distribution in background processing systems.
• Some common strategies include:
• Round-robin: This approach involves distributing the workload evenly
across all available resources, with each resource taking turns handling a
set number of tasks. This can be a simple and effective approach for
systems with a relatively small number of resources.
• Least connections: This strategy involves distributing the workload to the
resource with the fewest current connections. This can be useful for
systems where some resources are more powerful or efficient than others,
as it can help to ensure that the most capable resources are utilized as
efficiently as possible.
• Weighted distribution: This approach involves assigning different weights
to different resources, based on factors such as their processing power or
capacity. The workload is then distributed based on these weights, with
resources with higher weights receiving a larger share of the workload.
• Dynamic distribution: In this approach, the workload distribution is
continuously adjusted based on real-time data about the workload and the
resources available. This can allow the system to adapt to changing
workloads and resource availability in real-time.
Communication and Synchronization:
• Communication in distributed systems In a distributed system, each
entity may want to share information with other distributed entities.
• For example, a temperature sensor may want to share its information
with a climate control system.
• The processes run on different machines and the applications
implemented by these processes might include communication
between them.
Types of communication in distributed systems Communication among
distributed processes can be categorized into:
• 1. Unstructured communication - It involves using memory buffers to
pass information between the processes. So you might have a shared
memory region on a machine. Process can read and write to that
shared memory region and then some other prcoess can use that
information. uses shared memory or shared data structures; Update a
distributed shared memory to let others know of your local state.
Others can then read the distributed shared memory to get your local
state. No explicit messages are used.
• 2. Structured communication - also called as ’message passing’, this
uses explicit messages (or interprocess communication mechanisms)
over network. There is no shared memory in this type of
communication. The processes can be on same machines or different
machines
Synchronization in Distributed Systems
• In the distributed system, the hardware and software components communicate and
coordinate their actions by message passing.
• Each node in distributed systems can share its resources with other nodes.
• So, there is a need for proper allocation of resources to preserve the state of resources
and help coordinate between the several processes.
• To resolve such conflicts, synchronization is used.
• Synchronization in distributed systems is achieved via clocks.
• The physical clocks are used to adjust the time of nodes.
• Each node in the system can share its local time with other nodes in the system.
• The time is set based on UTC (Universal Time Coordination). UTC is used as a reference
time clock for the nodes in the system.
Clock synchronization can be achieved by 2 ways: External and Internal Clock
Synchronization.
• External clock synchronization is the one in which an external reference clock is present.
It is used as a reference and the nodes in the system can set and adjust their time
accordingly.
• Internal clock synchronization is the one in which each node shares its time with other
nodes and all the nodes set and adjust their times accordingly.
• There are 2 types of clock synchronization algorithms: Centralized and
Distributed.
• Centralized is the one in which a time server is used as a reference. The
single time-server propagates it’s time to the nodes, and all the nodes
adjust the time accordingly. It is dependent on a single time-server, so if
that node fails, the whole system will lose synchronization. Examples of
centralized are-Berkeley the Algorithm, Passive Time Server, Active Time
Server etc.
• Distributed is the one in which there is no centralized time-server present.
Instead, the nodes adjust their time by using their local time and then,
taking the average of the differences in time with other nodes. Distributed
algorithms overcome the issue of centralized algorithms like scalability and
single point failure. Examples of Distributed algorithms are – Global
Averaging Algorithm, Localized Averaging Algorithm, NTP (Network time
protocol), etc.
Scheduling and Load Balancing
Scheduling in Distributed Systems:
• The techniques that are used for scheduling the processes in distributed
systems are as follows:
• Task Assignment Approach: In the Task Assignment Approach, the user-
submitted process is composed of multiple related tasks which are
scheduled to appropriate nodes in a system to improve the performance of
a system as a whole.
• Load Balancing Approach: In the Load Balancing Approach, as the name
implies, the workload is balanced among the nodes of the system.
• Load Sharing Approach: In the Load Sharing Approach, it is assured that no
node would be idle while processes are waiting for their processing.
Characteristics of a Good Scheduling Algorithm:
The following are the required characteristics of a Good Scheduling Algorithm:
• The scheduling algorithms that require prior knowledge about the properties and
resource requirements of a process submitted by a user put a burden on the user. Hence,
a good scheduling algorithm does not require prior specification regarding the user-
submitted process.
• A good scheduling algorithm must exhibit the dynamic scheduling of processes as the
initial allocation of the process to a system might need to be changed with time to
balance the load of the system.
• The algorithm must be flexible enough to process migration decisions when there is a
change in the system load.
• The algorithm must possess stability so that processors can be utilized optimally. It is
possible only when thrashing overhead gets minimized and there should no wastage of
time in process migration.
• An algorithm with quick decision making is preferable such as heuristic methods that
take less time due to less computational work give near-optimal results in comparison to
an exhaustive search that provides an optimal solution but takes more time.
• A good scheduling algorithm gives balanced system performance by maintaining
minimum global state information as global state information (CPU load) is directly
proportional to overhead. So, with the increase in global state information overhead also
increases.
Load Balancing in Distributed Systems:

• The Load Balancing approach refers to the division of load among the
processing elements of a distributed system. The excess load of one
processing element is distributed to other processing elements that
have less load according to the defined limits.
• In other words, the load is maintained at each processing element in
such a manner that neither it gets overloaded nor idle during the
execution of a program to maximize the system throughput which is
the ultimate goal of distributed systems.
• This approach makes all processing elements equally busy thus
speeding up the entire task leads to the completion of the task by all
processors approximately at the same time.
Orchestration in distributed system
• In a distributed system, orchestration refers to the coordination and
management of various components, services, and tasks to achieve a
specific goal or workflow.
• It involves the arrangement and synchronization of activities across
multiple nodes or entities in the system.
• Orchestration is crucial for maintaining order, ensuring consistency,
and achieving efficient collaboration among the distributed
components.
Fault Tolerance in Distributed System
• Fault Tolerance is defined as the ability of the system to function
properly even in the presence of any failure. Distributed systems
consist of multiple components due to which there is a high risk of
faults occurring. Due to the presence of faults, the overall
performance may degrade.
Types of Faults

• Transient Faults: Transient Faults are the type of faults that occur once and
then disappear. These types of faults do not harm the system to a great
extent but are very difficult to find or locate. Processor fault is an example
of transient fault.
• Intermittent Faults: Intermittent Faults are the type of faults that comes
again and again. Such as once the fault occurs it vanishes upon itself and
then reappears again. An example of intermittent fault is when the working
computer hangs up.
• Permanent Faults: Permanent Faults are the type of faults that remains in
the system until the component is replaced by another. These types of
faults can cause very severe damage to the system but are easy to identify.
A burnt-out chip is an example of a permanent Fault.
Need for Fault Tolerance in Distributed Systems

Fault Tolerance is required in order to provide below four features.

• Availability: Availability is defined as the property where the system
is readily available for its use at any time.
• Reliability: Reliability is defined as the property where the system can
work continuously without any failure.
• Safety: Safety is defined as the property where the system can remain
safe from unauthorized access even if any failure occurs.
• Maintainability: Maintainability is defined as the property states that
how easily and fastly the failed node or system can be repaired.
Data Storage and Access:

● Hadoop Distributed File System (HDFS).

● AWS and Google Cloud Storage
● NoSQL databases:
Hadoop Distributed File System (HDFS).

• The Hadoop Distributed File System (HDFS) is the primary data

storage system used by Hadoop applications.
• HDFS employs a NameNode and DataNode architecture to
implement a distributed file system that provides high-performance
access to data across highly scalable Hadoop clusters.
• Hadoop itself is an open source distributed processing framework
that manages data processing and storage for big data applications.
• HDFS is a key part of the many Hadoop ecosystem technologies.
• It provides a reliable means for managing pools of big data and
supporting related big data analytics applications.
Data Processing and Analysis:

● Batch processing: Using frameworks like Hadoop MapReduce or Spark,

analyze big datasets offline.
● Stream processing: Using Apache Kafka or Apache Flink, real-time data
stream analysis is possible.
● In-memory computing: Although it uses more resources, this method of
processing and storing data in RAM allows for faster analysis.
● Distributed analytics systems: Scalable and effective platforms for analyzing
big datasets are offered by programs like Spark and Google BigQuery.
• HDFS architecture, NameNodes and DataNodes
• NameNode(MasterNode):
• Manages all the slave nodes and assign work to them.
• It executes filesystem namespace operations like opening, closing, renaming
files and directories.
• It should be deployed on reliable hardware which has the high config. not on
commodity hardware.
• DataNode(SlaveNode):
• Actual worker nodes, who do the actual work like reading, writing, processing
etc.
• They also perform creation, deletion, and replication upon instruction from
the master.
• They can be deployed on commodity hardware.
 Features of HDFS
• There are several features that make HDFS particularly useful, including:
• Data replication. This is used to ensure that the data is always available and
prevents data loss. For example, when a node crashes or there is a hardware
failure, replicated data can be pulled from elsewhere within a cluster, so processing
continues while data is recovered.
• Fault tolerance and reliability. HDFS' ability to replicate file blocks and store them
across nodes in a large cluster ensures fault tolerance and reliability.
• High availability. As mentioned earlier, because of replication across notes, data is
available even if the NameNode or a DataNode fails.
• Scalability. Because HDFS stores data on various nodes in the cluster, as
requirements increase, a cluster can scale to hundreds of nodes.
• High throughput. Because HDFS stores data in a distributed manner, the data can
be processed in parallel on a cluster of nodes. This, plus data locality (see next
bullet), cut the processing time and enable high throughput.
• Data locality. With HDFS, computation happens on the DataNodes where the data
resides, rather than having the data move to where the computational unit is. By
minimizing the distance between the data and the computing process, this
approach decreases network congestion and boosts a system's overall throughput.
AWS and Google Cloud Storage
• Google Cloud Platform:
It is a suite of cloud computing services developed by Google and launched
publicly in 2008. Google Cloud Platform provides IaaS, PaaS, and serverless
computing environments. A comparatively new Google Cloud Platform has
all the tools and services required by developers and professionals.
• Amazon Web Services(AWS):
It provides on-demand cloud computing platforms and APIs. AWS platform
was initially launched in 2002. However, AWS was officially re-launched in
2006 with three initial service offerings of Amazon S3 cloud storage, SQS,
and EC2. AWS comprises more than 212 services including computing,
storage, networking, database, analytics, application services, deployment,
management, developer tools, etc. In terms of services, AWS has an edge
over its competitors, as the amount of services offered by AWS is way more
than any of its contemporaries.
 Features Google Cloud Amazon Web Services
Offered By Google Amazon

Google Compute Engine API (IaaS), App Engine (PaaS), Kubernetes Engine Amazon Elastic Compute Cloud (IaaS), Elastic Beanstalk(PaaS), Elastic Compute Cloud
Computing Service
(Container), Cloud Functions (Serverless Functions) Container Service (Container), AWS Lambda (Serverless Functions)

Google Cloud SQL (RDBMS), Google Cloud Datastore, Google Cloud Bigtable (NoSQL Amazon Relational Database Service (RDBMS), Amazon DynamoDB (NoSQL Key–
Database Services
Key–Value), Google Cloud Datastore (NoSQL: Indexed) Value), Amazon SimpleDB (NoSQL Indexed)

Google Cloud Storage (Object Storage), Google Compute Engine Persistent Disks
Amazon Simple Storage Service (Object Storage), Amazon Elastic Block Store (Block
Storage Services (Block Storage), ZFS/Avere (File Storage), Google Cloud Storage Nearline (Cold
Storage), Amazon Elastic File System (File Storage), Amazon Glacier (Cold Storage)
Storage)

Stackdriver Monitoring (Monitoring), Google Cloud Deployment Manager

Management Services Amazon CloudWatch (Monitoring), AWS CloudFormation (Deployment)
(Deployment)

Virtual Private Cloud, Google Cloud Load Balancing, Google Cloud Interconnect, Amazon Virtual Private Cloud, Elastic Load Balancer, Direct Connect, Amazon Route
Network Services
Google Cloud DNS 53

Customization of instances Google Cloud Platform provides a wide range of customization for any Instance AWS provides limited customization.

Pricing Google charges per minute basis Amazon charges per hour basis

Google free tiers have no time limit. GCP provides $300 worth credit that can be used Amazon free tiers have a maximum validity of 12 months and later charges as per
Cost
across all services. Hence, GCP is comparatively cheaper. usage. Hence, AWS is costlier.

AWS had reported lesser downtime compared to GCP which makes it a clear winner
Downtime GCP had reported more downtime compared to AWS
in this case

Big data support Big data analysis tool is AI First Big data analysis tool is AWS Lambda.

Cloud Machine Learning Engine, Dialogflow Enterprise Edition, Cloud Natural Tools offered by AWS for AI/ML are SageMaker, Comprehend, Lex, Polly,
AI/ML Support Language, Cloud Speech API, Cloud Translation API, Cloud Video Intelligence, Cloud Rekognition, Machine Learning, Translate, Transcribe, DeepLens, Deep Learning
Job Discovery AMIs, Apache MXNet, TensorFlow

Availability GCP is available in 29 geographic regions and 88 zones worldwide AWS is available at 26 geographic regions and 84 zones worldwide

Companies using Spotify, HSBC, Home Depot, Snapchat, Philips, Coca Cola, Domino’s and many more Netflix, Twitch, LinkedIn, Facebook, ESPN, Citrix, Expedia and many more
NoSQL databases:
• NoSQL database technology stores information in JSON documents instead of columns and rows
used by relational databases. To be clear, NoSQL stands for “not only SQL” rather than “no SQL” at
all. This means a NoSQL JSON database can store and retrieve data using literally “no SQL.” Or you
can combine the flexibility of JSON with the power of SQL for the best of both worlds.
Consequently, NoSQL databases are built to be flexible, scalable, and capable of rapidly
responding to the data management demands of modern businesses. The following defines the
four most-popular types of NoSQL database:
• Document databases are primarily built for storing information as documents, including, but not
limited to, JSON documents. These systems can also be used for storing XML documents, for a
NoSQL database example.
• Key-value stores group associated data in collections with records that are identified with unique
keys for easy retrieval. Key-value stores have just enough structure to mirror the value of
relational databases (as opposed to non-relational databases) while still preserving the benefits
of the NoSQL database structure.
• Wide-column databases use the tabular format of relational databases yet allow a wide variance
in how data is named and formatted in each row, even in the same table. Like key-value stores,
wide-column databases have some basic NoSQL structure while also preserving a lot of flexibility
• Graph databases use graph structures to define the relationships between stored data points.
Graph databases are useful for identifying patterns in unstructured and semi-structured
information.
Why use NoSQL?

• Customer experience has quickly become the most important

competitive differentiator and ushered the business world into an era
of monumental change.
• As part of this revolution, enterprises are interacting digitally – not
only with their customers, but also with their employees, partners,
vendors, and even their products – at an unprecedented scale.
• This interaction is powered by the internet and other 21st century
technologies – and at the heart of the revolution of NoSQL are a
company’s big data, cloud, mobile, social media, and IoT applications.
• Support large numbers of concurrent users (tens of thousands,
perhaps millions)
• Deliver highly responsive experiences to a globally distributed base of
users
• Be always available – no downtime
• Handle semi- and unstructured data
• Rapidly adapt to changing requirements with frequent updates and
new features
Data Processing and Analysis:

● Batch processing: Using frameworks like Hadoop MapReduce or Spark,

analyze big datasets offline.
● Stream processing: Using Apache Kafka or Apache Flink, real-time data
stream analysis is possible.
● In-memory computing: Although it uses more resources, this method of
processing and storing data in RAM allows for faster analysis.
● Distributed analytics systems: Scalable and effective platforms for analyzing
big datasets are offered by programs like Spark and Google BigQuery.
Data Management and Quality:

● Data integration: Creating a single, cohesive perspective by combining

data from several sources.
● Data cleaning: Fixing mistakes and discrepancies in the data.
● Data governance: Creating guidelines and protocols to guarantee
privacy, security, and correctness in data management.
● Data compression: lowering the amount of data stored without
sacrificing information.
Tools and Technologies:

● Apache Hadoop is an open-source system for data storage and distributed

processing.
● Apache Spark: Batch and stream processing unified analytics engine.
● Apache Kafka: Real-time data processing via distributed streaming.
● Google BigQuery: A cloud-based data warehouse designed for extensive
analysis of data.
● Amazon Redshift: An analytics and data warehousing cloud-based data
warehouse.
 Understanding Parallel Processing:
● Numerous processors or cores: Many modern computers have numerous processing cores that can handle multiple tasks
at once.
● Distributed systems: Even more parallelization is possible when the processing capacity of several computers is
combined over a network.
● Algorithms and code optimization: Parallelization is a natural fit for some algorithms but not for others. For the best use
of processor cores, proper code optimization is essential.

Benefits of Parallel Processing:

● Quicker execution: Workloads are split up and handled separately, which drastically cuts down on completion times.
● Scalability: Performance is further enhanced by adding more processing power (cores or machines).
● Real-time capabilities: For applications that move quickly, parallelization allows for real-time analysis and response.
● Resource optimization: Tasks are divided among several cores or computers to make efficient use of the resources that
are available.
Strategies for Leveraging Parallel
Processing:
Strategies for Leveraging Parallel Processing:

● Determining which tasks may be parallelized: Some jobs cannot be parallelized because of
dependencies or constraints for sequential execution. Examine your process to find qualified
applicants.

● Selecting the appropriate libraries and tools: Parallel programming and task distribution features are
provided by frameworks such as CUDA, MPI, and OpenMP. Your unique needs and the design of your
system will determine which tool is best for you.

● Tuning performance and optimization: You can overcome potential bottlenecks and greatly increase
parallelization efficiency by fine-tuning your code and algorithms.
Application of Integrating AI & DS in DS:
Predictive Maintenance: (Process Plant)
The most cutting-edge method for managing maintenance in process plants is called
predictive maintenance, or PdM.

Predictive maintenance differs from other types of maintenance in many ways. Let’s
start by looking at various different types of maintenance, such as:

● Reactive maintenance, or run-to-failure

● Preventive maintenance
● Prescriptive maintenance
● Predictive maintenance (PdM), or condition monitoring

SAMGUARD Tool
 Fraud Detection
Another fascinating area where the combination of data science and artificial
intelligence might unleash enormous potential in distributed systems is fraud
detection. Now let's explore some particular use cases and applications:

1. Scalable Anomaly Detection:

2. Distributed Graph-based Fraud Detection:

3. Adaptive Fraud Scoring and Risk Assessment:

4. Collaborative Threat Intelligence Sharing:

5. Edge-based Fraud Detection:

Scalable Anomaly Detection:

• Anomaly detection, fraud detection, and outlier detection are the

terms commonly heard in the A.I. world. While having different terms
and suggesting different images to mind, they all reduce to the same
mathematical problem, which is in simple terms, the process of
detecting an entry among many entries, which does not seem to
belong there.
For example
• credit/debit card fraud detection, as a use case of anomaly detection, is the process of
checking whether the incoming transaction request fits well with the user’s previous
profile and behavior or not.
• Take this as an example: Joe is a hard-working man who works at a factory near NY. Every
day he buys a cup of coffee from a local cafe, goes to work, buys lunch, and on his way
home, he sometimes shops for groceries.
• He pays bills with his card and occasionally spends money on leisure, restaurants,
cinema, etc.
• One day, a transaction request is sent to Joe’s bank account to pay for a $30 payment at
a pizza hut near Austin, TX. Not knowing whether this was Joe on a vacation or his card
has gone missing, does this look like an anomalous transaction? Yes.
• What if someone starts paying $10 bills with Joe’s account on a “Card-holder-not-
present” basis, e.g. online payment? The banking institute would want to stop these
transactions and verify them with Joe, by SMS or Email.
Intelligent Transportation Systems (ITS)

Another excellent illustration of how AI and data science excel in distributed systems is found
in intelligent transportation systems (ITS). By combining several technologies, they seek to
increase the sustainability, safety, and efficiency of transportation networks:

● 1. Real-time Traffic Management and Congestion Control

● 2. Connected and Autonomous Vehicles (CAVs)
● 3. Public Transportation Optimization
● 4. Predictive Maintenance for Transportation Infrastructure
● 5. Multimodal Transportation Planning and Integration
Supply Chain optimization

● Supply chain optimization is an extremely fascinating field where data science and
artificial intelligence are applied to dispersed systems! Let's investigate a few
particular application domains.

1. Demand Forecasting and Inventory Management

2. Logistics and Transportation Optimization
3. Risk Management and Proactive Planning
4. Smart Contracts and Collaborative Optimization:
5. Predictive Maintenance for Machinery and Assets
Energy Management

Another exciting area where AI and data science combine in distributed systems to produce
game-changing solutions is energy management. Here are a few crucial areas in which they
excel:

1. Demand Forecasting and Grid Optimization

2. Renewable Energy Integration and Forecasting:

3. Smart Grids and Distributed Resource Management:

4. Energy Efficiency and Smart Building Controls:

Healthcare and Medical diagnostics

In this extremely important subject, AI and data science are transforming methods for
diagnosing diseases and providing medical care. Let's examine a few crucial areas
where they are having a big influence:

1. Medical Imaging and Diagnosis:

2. Clinical Decision Support and Risk Prediction:
3. Personalized Medicine and Precision Healthcare:
4. Remote Patient Monitoring and Telemedicine:
5. Drug Safety and Pharmacovigilance:
Customer Behavior Analysis and Natural
Language Processing (NLP):

NLP can be used to analyze consumer behavior in the following ways:

1. Social Media Listening and Sentiment Analysis:

2. Customer Feedback Analysis:
3. Chatbot Conversations and Personalized Recommendations:
4. Predictive Analytics and Customer Churn:
5. Voice of the Customer (VOC) Analysis
Case Study: Enhancing Scalability and
Performance in E-Commerce through
Distributed Computing
1. Abstract:
2. Introduction:
3. Problem Statement:
4. Solution:
5. Implementation:
6. Results
7. Conclusion