cloud computing -module 2

1. MODULE-II

2. Amazon Web Services(AWS)-IaaS
• Amazon Web Services (AWS) is a popular Infrastructure as a Service (IaaS)
provider. As an IaaS provider, AWS offers a wide range of cloud-based
infrastructure resources that can be provisioned and managed over the internet.
• AWS provides various kinds of services.
1. Virtual Machines: AWS Elastic Compute Cloud (EC2) allows you to create and
manage virtual machines (known as instances) in the cloud. You have full
control over the operating system, applications, and configuration of these
instances.
2. Storage: AWS provides various storage options such as Amazon Simple Storage
Service (S3) for object storage, Elastic Block Store (EBS) for block-level storage
volumes, and Amazon Glacier for long-term archival storage.
3. Networking: AWS offers networking services like Virtual Private Cloud (VPC),
which allows you to create isolated network environments with control over IP
ranges, subnets, and network gateways. You can also configure security groups,
network ACLs, and connect to your on-premises network using VPN or Direct
Connect.

3. 5. Load Balancing and Auto Scaling: AWS Elastic Load Balancing (ELB) distributes incoming
traffic across multiple EC2 instances to improve availability and fault tolerance. Auto
Scaling allows you to automatically adjust the number of instances based on demand.
6. Database Services: AWS provides managed database services like Amazon RDS (Relational
Database Service) for popular database engines such as MySQL, PostgreSQL, and Oracle. It
also offers Amazon DynamoDB, a NoSQL database service, and Amazon Redshift for data
warehousing.
7. Monitoring and Management: AWS provides services like Amazon Cloud Watch for
monitoring resource utilization, AWS Cloud Formation for infrastructure deployment and
management, and AWS Systems Manager for centralized management and automation of
AWS resources.
8. Security: AWS offers various security features, including Identity and Access Management
(IAM) for user access control, Virtual Private Cloud (VPC) for network isolation, and security
groups for firewall rules. It also provides options for encryption at rest and in transit.
9. Other Services: AWS has a vast ecosystem of services, including server less computing with
AWS Lambda, content delivery with Amazon CloudFront, messaging and queuing with
Amazon Simple Queue Service (SQS) and Amazon Simple Notification Service (SNS), and
many more.

4. Elastic compute cloud(EC2)
• Amazon Elastic Compute Cloud (Amazon EC2) is a core service offered by Amazon
Web Services (AWS). It provides scalable virtual machine instances in the cloud,
allowing you to quickly provision and manage computing resources to run your
applications.
• Here are some key features and concepts related to Amazon EC2:
 Instances: Amazon EC2 allows you to create and manage virtual machine instances.
You can select the instance type that best suits your needs in terms of CPU,
memory, storage, and networking capacity.
 Availability Zones: Amazon EC2 instances are hosted in various data centers known
as Availability Zones. Each Availability Zone is an isolated location designed to
provide high availability and fault tolerance.
 Regions: AWS regions consist of multiple Availability Zones located in different
geographic areas. You can select the region where you want your instances to be
located based on factors such as latency, compliance, and data residency
requirements.

5. Simple Storage Service (S3)
• Simple Storage Service (S3) is a highly scalable and secure cloud storage service provided by
Amazon Web Services (AWS). It allows individuals and organizations to store and retrieve large
amounts of data in the cloud.
• Here are some key features and concepts related to Amazon S3:
 Scalability: S3 is designed to be highly scalable, allowing you to store and retrieve any amount of
data at any time. It automatically scales to accommodate your storage needs, whether you have a
few gigabytes or several petabytes of data.
 Durability and Availability: S3 is designed for durability and availability. It stores multiple copies
of your data across multiple data centers, providing a high level of redundancy. This ensures that
your data is highly durable and accessible even in the event of hardware failures or natural
disasters.
 Bucket: In S3, data is organized into containers called "buckets." A bucket acts as a top-level
folder or directory that stores your objects (files) and provides a unique namespace within S3.
Each bucket has a globally unique name, and you can control access and permissions at the
bucket level.
 Object: An object is the fundamental entity stored in S3. It represents a file and consists of the
actual data along with its metadata. Each object is identified by a unique key, which includes the
object's name and its location within the bucket. Objects can range in size from a few bytes to
multiple terabytes.

6.  Access Control: S3 provides fine-grained access control mechanisms to secure your data. You can define
access policies and permissions at the bucket level or at the individual object level. S3 supports various
authentication methods, including AWS Identity and Access Management (IAM) roles, access control
lists (ACLs), and bucket policies.
 Data Management: S3 offers several features for managing your data, such as versioning, lifecycle
policies, and cross-region replication. Versioning allows you to keep multiple versions of an object, while
lifecycle policies enable you to automate the transition of objects to different storage classes or to
delete them after a specified period. Cross-region replication allows you to replicate objects across
different AWS regions for enhanced data durability and availability.
 Integration and Ecosystem: S3 integrates with various AWS services and has a rich ecosystem of tools
and libraries. It can be easily integrated with other AWS services like AWS Lambda, Amazon CloudFront,
Amazon Athena, and more. Additionally, there are numerous third-party tools and client libraries
available for interacting with S3.
Overall, Amazon S3 provides a reliable, scalable, and cost-effective solution for storing and managing
data in the cloud. It is widely used by organizations of all sizes for various use cases, including backup and
restore, data archiving, content distribution, big data analytics, and static website hosting.

7. Simple Queuing Services(SQS)
• AWS does offer a messaging and queuing service called Amazon Simple Queue Service (SQS). It is a
fully managed message queuing service that enables you to decouple and scale microservices,
distributed systems, and serverless applications.
• Amazon Simple Queue Service (SQS) provides the following features:
 Message Queue: SQS allows you to create message queues that act as temporary repositories for
messages. Each message in the queue can be up to 256 KB in size and contains any information that
you want to transmit between components of your application.
 Decoupling: SQS decouples the components of a distributed system, allowing them to operate
independently. Producers send messages to a queue, and consumers retrieve messages from the
queue. This decoupling enables asynchronous communication and helps to ensure the reliability and
scalability of your system.
 Scalability: SQS automatically scales to accommodate the volume of messages in the queue. It can
handle any amount of traffic without requiring capacity planning.
 Reliability: SQS is designed to be highly available and reliable. It redundantly stores messages across
multiple servers and data centers to ensure durability. It also provides at-least-once message delivery,
which means that a message will be delivered to a consumer at least once, and it is up to the
consumer to handle duplicates if they occur.

8.  Message Visibility: When a consumer receives a message from the queue, the message becomes
temporarily invisible to other consumers. This feature ensures that only one consumer processes a
message at a time. If the consumer fails to process the message within a specified time (visibility
timeout), the message becomes visible again and can be picked up by another consumer.
 FIFO Queues: SQS also supports FIFO (First-In-First-Out) queues, which preserve the order in which
messages are sent and received. FIFO queue are useful in scenarios where message order is critical,
such as in transactional systems.
 Integration: SQS integrates with other AWS services, allowing you to build scalable and decoupled
applications. For example, you can use AWS Lambda functions to process messages from an SQS
queue, or you can trigger AWS Step Functions workflows based on messages in the queue.
Amazon SQS is commonly used in various applications and architectural patterns, such as event-driven
systems, distributed systems, and micro services. It provides an easy-to-use and reliable messaging
infrastructure, allowing you to build loosely coupled and scalable applications in the AWS cloud.

9. What is VMware vCloud ?
• VMware vCloud, as a platform, provides infrastructure services that enable
organizations to build and manage their own private cloud infrastructure. It offers
the necessary components and features for Infrastructure-as-a-Service, including
virtualized compute, storage, and networking resources. With vCloud, organizations
can create, provision, and manage virtual machines (VMs), networks, and storage in
a self-service manner.
• The primary goal of vCloud is to abstract the underlying physical infrastructure and
provide a software-defined data center (SDDC) environment. It allows IT teams to
manage and allocate resources efficiently, implement multi-tenancy, and provide
self-service capabilities to end-users or departments within the organization.
• In summary, while there may not be a specific VMware vCloud-IaaS product,
VMware vCloud as a platform encompasses Infrastructure-as-a-Service capabilities
that facilitate the creation, provisioning, and management of virtualized
infrastructure resources.

10. Characteristics of VMware vCloud-IaaS
 Software-Defined Data Center (SDDC): VMware vCloud is built on the concept of the Software-Defined Data Center, where
compute, storage, networking, and security are abstracted from physical infrastructure and managed through software. This
enables the creation of flexible and scalable cloud environments.
 Multi-Tenancy: vCloud allows for the creation of isolated virtual data centers, known as "organizations," which enable secure
multi-tenancy. Each organization can have its own resource allocation, policies, and access controls, allowing multiple users or
departments to share a common infrastructure while maintaining separation and control.
 Self-Service Provisioning: vCloud offers self-service capabilities, allowing authorized users to provision virtual machines (VMs)
and other resources on-demand through a web-based portal or API. This empowers users to quickly deploy and manage their
own virtual infrastructure without requiring manual intervention from IT administrators.
 Infrastructure Abstraction: vCloud abstracts the underlying infrastructure, providing a unified and consistent management
layer for various virtualization technologies, including VMware vSphere. This enables organizations to leverage their existing
VMware investments and extend them to a cloud environment.
 Resource Pooling and Allocation: vCloud enables efficient resource pooling and allocation, allowing IT administrators to
create resource pools and allocate CPU, memory, storage, and network resources to different virtual data centers or
organizations. This helps optimize resource utilization and ensures that each organization receives the necessary resources
based on their requirements.
 Networking and Security: vCloud provides network virtualization capabilities, allowing organizations to create virtual
networks and define routing, firewalling, and load balancing rules. It supports integration with VMware NSX, a software-
defined networking solution, to provide advanced network and security services in a cloud environment.
 Hybrid Cloud Connectivity: vCloud supports hybrid cloud scenarios, allowing organizations to extend their private cloud
infrastructure to public cloud services, such as VMware Cloud on AWS or other vCloud-based public clouds. This enables
workload mobility, data replication, and seamless integration between private and public cloud environments.
 Orchestration and Automation: vCloud integrates with VMware vRealize Orchestrator and vRealize Automation, providing
robust orchestration and automation capabilities for managing complex workflows, provisioning tasks, and service catalog
management.

11. vCloud Express
vCloud Express was designed as a public cloud offering by VMware, allowing customers to provision and
manage virtual machines (VMs) in a scalable and on-demand manner. It aimed to provide a simplified
and user-friendly experience for developers and organizations seeking cloud infrastructure services.
• Virtual Infrastructure: vCloud Express offered a virtualized infrastructure environment based on
VMware's virtualization technology. Customers could create and manage virtual machines (VMs) within
the cloud environment, providing flexibility and scalability.
• Self-Service Portal: The service provided a self-service portal that allowed users to provision, manage,
and monitor their virtual resources, including virtual machines, networks, and storage. Users had
control over their virtual infrastructure and could scale resources up or down as needed.
• Scalability and Flexibility: vCloud Express offered the ability to scale resources based on demand. Users
could allocate additional virtual machines, storage, and network resources to meet their workload
requirements.
• Pay-as-You-Go Pricing: The service followed a pay-as-you-go pricing model, allowing customers to pay
for the resources they consumed. This provided cost efficiency as users only paid for the resources they
utilized.
• Compatibility with VMware Ecosystem: vCloud Express was designed to work seamlessly with
VMware's existing suite of virtualization products and tools. Customers could leverage their existing
VMware infrastructure and tools to manage and migrate workloads to the cloud.

12. Assignment
• Task to be completed by own
– What is vCloud and vCloud Express? Find out the
Limitations/disadvantages of vCloud Express
– Find out what are the trending Vmware vCloud as
IaaS platform.

13. Google App Engine(GAE)-PaaS
• Google App Engine (GAE) is a platform-as-a-service (PaaS) offering by Google Cloud
Platform (GCP) that allows developers to build, deploy, and scale applications
easily.
• It provides a serverless environment for hosting web applications and services,
abstracting away the infrastructure management, so developers can focus on
writing code.
• It's a powerful platform that simplifies application deployment and management,
making it easier for developers to focus on building their applications.

14. Characteristics of GAE
• Flexible Environment and Standard Environment: Google App Engine offers two environments for running
applications: the Flexible Environment and the Standard Environment. The Flexible Environment allows you
to run applications in containers using Docker, while the Standard Environment uses a more restricted
runtime environment.
• Auto Scaling and Load Balancing: App Engine automatically manages the resources and scales your
application based on the incoming traffic. It can handle sudden spikes in traffic by dynamically allocating
more resources, and it can scale down when the traffic decreases. Load balancing ensures that the incoming
requests are distributed across multiple instances of your application.
• Data Storage: App Engine provides various options for storing data, including Google Cloud Datastore, a
NoSQL document database, and Google Cloud SQL, a fully managed relational database service. You can also
use other Google Cloud storage services like Cloud Storage or Cloud Firestore.
• Support for Multiple Programming Languages: App Engine supports several programming languages such as
Java, Python, Node.js, Go, and PHP, allowing you to write applications in your preferred language.
• Integration with Google Cloud Services: App Engine seamlessly integrates with other Google Cloud services,
such as Google Cloud Pub/Sub for messaging, Google Cloud Tasks for task execution, and Google Cloud APIs
for accessing various Google services.
• Deployment and Versioning: Deploying an application to App Engine is straightforward using the gcloud
command-line tool or the Google Cloud Console. You can deploy different versions of your application,
allowing you to easily roll back to a previous version if needed.
• Monitoring and Logging: App Engine provides monitoring and logging capabilities through Google Cloud
Monitoring and Google Cloud Logging. You can track the performance and health of your application, set up
alerts, and analyze logs for debugging.
• Security and Identity: App Engine offers built-in security features such as automatic SSL certificates, access
control through Identity and Access Management (IAM), and integration with other Google Cloud security
services.

15. The Java Runtime Environment(JRE)
• The JRE is a software package that provides the runtime environment for executing
Java applications.
• Java Runtime Environment (JRE) is a software package provided by Oracle Corporation
that allows running Java applications.
• It provides the necessary runtime environment for executing Java programs, including
the Java Virtual Machine (JVM), core libraries, and other components required to run
Java applications.
• Some examples of PaaS providers which support java are Google App Engine, Heroku,
Red Hat Open shift, IBM Cloud foundry etc.
• These PaaS providers offer Java runtime environments (JRE) as part of their platform,
making it easier for developers to deploy and manage Java applications in the cloud.
• JRE is required to run Java applications, but it does not include development tools for
creating or compiling Java code. For development purposes, developers typically use
the Java Development Kit (JDK), which includes the JRE along with development tools.

16. MODULE- III

17. Key Components of JRE
• Java Virtual Machine (JVM): The JVM is the heart of the Java Runtime Environment. It is responsible for
executing Java bytecode, which is the compiled form of Java source code. The JVM translates bytecode into
machine-specific instructions and manages memory, thread execution, and other low-level functionalities.
• Core Libraries: The JRE includes a set of core libraries that provide essential functionalities to Java
applications. These libraries offer classes and methods for tasks such as file input/output (I/O), networking,
database connectivity, user interface (UI), and more. Developers can leverage these libraries to build robust
and feature-rich Java applications.
• Class Loader: The JRE's class loader is responsible for dynamically loading Java classes and resources at
runtime. It locates and loads the required class files from the classpath, which is a list of directories or JAR
files containing the compiled Java classes. The class loader ensures that the classes are loaded as needed
during program execution.
• Java Development Kit (JDK) Compatibility: The JRE is designed to be compatible with a specific version of
the Java Development Kit (JDK). The JDK includes the JRE along with additional development tools such as
compilers, debuggers, and profilers. The JRE allows running Java applications without requiring the
development tools provided by the JDK.
• Security Manager: The JRE includes a security manager that enforces the security policies defined for Java
applications. It controls access to system resources, such as files, network connections, and system
properties, preventing malicious code from performing unauthorized actions. The security manager
ensures that Java applications run in a secure sandbox environment.
• Java Web Start: Java Runtime Environment includes Java Web Start, a deployment technology that enables
launching Java applications directly from a web browser. With Java Web Start, users can click on a link or
button on a webpage to launch a Java application, which is automatically downloaded and run in a secure
manner.

18. Python runtime environment
• A Python runtime environment refers to the software
and resources required to execute Python programs.
• It includes the necessary components to interpret and
run Python code, such as the Python interpreter,
standard libraries, and additional dependencies.
• Python runtime environment combines the necessary
software components and configurations to execute
Python code and provides a foundation for developing
and running Python applications.

19. Key components of python runtime environment
• Python Interpreter: The Python interpreter is the core component that executes Python code. It reads the Python source
code, interprets it, and produces the desired output.
• Python Standard Library: Python comes with a comprehensive standard library that provides a wide range of modules
and packages for various tasks. These modules offer functionalities for file I/O, networking, data manipulation, web
development, and more.
• Third-Party Packages: Python has a vast ecosystem of third-party libraries and packages developed by the community.
These packages extend the functionality of Python by providing additional tools, frameworks, and utilities for specific
purposes, such as data analysis, machine learning, web scraping, etc. Examples of popular third-party package managers
include pip (the default package manager for Python) and conda (commonly used for scientific computing).
• Virtual Environments: Virtual environments allow you to create isolated Python environments with specific package
dependencies. This is useful when you're working on multiple projects that require different package versions or when
you want to avoid conflicts between packages. Popular virtual environment tools for Python include virtualenv and conda
environments.
• Integrated Development Environment (IDE) or Text Editor: While not strictly part of the Python runtime environment, an
IDE or text editor is often used to write, edit, and manage Python code. Some popular choices for Python development
include PyCharm, Visual Studio Code, Sublime Text, and Atom.
• Operating System and Hardware: Python can run on various operating systems such as Windows, macOS, and Linux. The
runtime environment also relies on the underlying hardware infrastructure, including CPU, memory, and storage, to
execute Python programs efficiently.

20. Data Store in Python Runtime Environment
• File System: Python allows you to read from and write to files on your computer's file
system using functions like open(), read(), and write(). This is useful for handling text
files, CSV files, JSON files, and other file-based data.
• Relational Databases: Python provides libraries like SQLite3 and connectors for popular
databases such as MySQL, PostgreSQL, and Oracle. You can use these libraries to interact
with relational databases, execute SQL queries, and manage data stored in tables.
• NoSQL Databases: Python also offers libraries and connectors for various NoSQL
databases like MongoDB, Redis, and Cassandra. These libraries allow you to work with
non-relational data models, such as key-value stores, document databases, and
columnar databases.
• Object-Relational Mapping (ORM) Libraries: ORMs like SQLAlchemy provide a higher-
level abstraction to interact with databases. They allow you to define Python classes that
map to database tables, and perform operations like querying, inserting, updating, and
deleting records using Python code.

21. Development Workflow in Python Runtime Environment
• Code Editors and IDEs: You can use code editors or integrated development environments (IDEs) like PyCharm, Visual
Studio Code, Sublime Text, or Atom to write, edit, and manage your Python code. These tools provide features like
syntax highlighting, code completion, debugging, and version control integration to enhance your development
experience.
• Version Control: Version control systems like Git enable you to track changes to your codebase, collaborate with others,
and manage different versions of your project. You can use Git commands or graphical interfaces like GitKraken,
SourceTree, or GitHub Desktop to interact with repositories.
• Package Management: Python uses package managers like pip (for libraries from the Python Package Index) and conda
(for managing packages and environments) to install, upgrade, and remove third-party libraries and dependencies. You
can specify package requirements in a requirements.txt file or a conda.yaml file to ensure consistent environments
across different machines.
• Virtual Environments: As mentioned earlier, virtual environments help create isolated Python environments with
specific package versions. Tools like virtualenv and conda environments enable you to create, activate, and manage
virtual environments, ensuring project dependencies are contained and separate from the system Python environment.
• Testing and Debugging: Python offers frameworks like unittest, pytest, and doctest for writing automated tests to verify
the correctness of your code. Integrated debugging tools in IDEs allow you to set breakpoints, inspect variables, and step
through your code to identify and fix issues.
• Documentation and Collaboration: Documenting your code using tools like Sphinx or MkDocs helps generate project
documentation and API references. Collaboration platforms like GitHub, GitLab, or Bitbucket facilitate code sharing, issue
tracking, and pull request reviews to collaborate with other developers.