What is cloud computing?
Cloud computing refers to the delivery of computing services, including storage, processing power, networking, databases, analytics, software, and intelligence, over the internet (the cloud) to offer faster innovation, flexible resources, and economies of scale. Instead of owning and maintaining physical data centers and servers, individuals and organizations can access and use computing resources on a pay-as-you-go basis from cloud service providers
Simply put, the cloud is the Internet—more specifically, it's all of the things you can access remotely over the Internet. When something is in the cloud, it means it's stored on Internet servers instead of your computer's hard drive
Key characteristics of cloud computing include:
On-Demand Self-Service: Users can provision and manage computing resources as needed without requiring human intervention from the service provider.
Broad Network Access: Cloud services are accessible over the internet from a variety of devices such as laptops, smartphones, and tablets.
Resource Pooling: Cloud providers pool computing resources to serve multiple customers, with different physical and virtual resources dynamically assigned and reassigned according to demand.
Rapid Elasticity: Resources can be scaled up or down quickly to accommodate changes in demand, allowing for flexibility and cost efficiency.
Measured Service: Cloud computing resources are metered, and users pay for only the resources they consume. This pay-as-you-go model enables cost optimization
Popular cloud service providers include Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform (GCP), and others, each offering a wide range of services to meet different computing needs. In this blog, we will learn about AWS Cloud and its functionality.
Amazon Web Services (AWS) is a comprehensive and widely used cloud computing platform provided by Amazon.com. AWS offers a vast array of cloud services, including computing power, storage, databases, machine learning, analytics, networking, security, and more. Launched in 2006, AWS has become a key player in the cloud computing industry and is utilized by millions of customers globally, ranging from startups and small businesses to large enterprises and government organizations.
AWS Pricing Models
Amazon Web Services (AWS) offers a variety of pricing models to accommodate different use cases and customer preferences. It's important to carefully choose the pricing model that aligns with your usage patterns, budget considerations, and performance requirements. AWS provides a Pricing Calculator on their website to estimate costs based on specific usage scenarios. Additionally, customers should regularly review and optimize their resource usage to ensure cost efficiency.
The primary pricing models used by AWS are as follows:
On-Demand Instances:
Description: Pay for computing capacity by the hour or by the second with no upfront commitment. This model provides flexibility and is suitable for unpredictable workloads or short-term projects.
Use Case: Well-suited for development and testing, applications with variable workloads, and short-term projects.
Reserved Instances (RIs):
Description: Requires a one- or three-year commitment in exchange for a significant discount compared to On-Demand pricing. RIs provide cost savings for steady-state workloads with predictable usage.
Use Case: Best for applications with steady, predictable usage over time.
Spot Instances:
Description: Allows users to bid for unused EC2 capacity at potentially lower costs than On-Demand instances. However, these instances can be terminated if the capacity is needed by On-Demand users.
Use Case: Suitable for fault-tolerant applications, batch processing, and workloads with flexible start and end times.
Dedicated Hosts:
Description: Provides physical servers with EC2 instance capacity for use with your existing software licenses. Dedicated Hosts offer visibility into the underlying infrastructure and control over the placement of instances.
Use Case: Ideal for regulatory requirements, licensing restrictions, and applications that need visibility into the underlying hardware.
Savings Plans:
Description: Similar to Reserved Instances but offers more flexibility in terms of instance types and families. Savings Plans provide significant cost savings in exchange for a commitment to a consistent amount of usage (measured in $/hr) for a 1- or 3-year term.
Use Case: Suitable for a wide range of instance types in exchange for a commitment to a consistent usage amount.
Elastic Load Balancer (ELB) Pricing:
Description: ELB pricing is based on the number of load balancer hours and data processed by the load balancer.
Use Case: For distributing incoming application traffic across multiple targets, ensuring high availability and fault tolerance.
Amazon RDS Pricing:
Description: RDS pricing is based on factors such as DB instance type, storage, and data transfer. RDS offers On-Demand and Reserved Instance pricing options.
Use Case: Ideal for managing and scaling relational databases in the cloud.
Amazon S3 Pricing:
Description: S3 pricing is based on factors such as storage usage, number of requests, and data transfer. S3 offers Standard, Intelligent-Tiering, Glacier, and other storage classes with different pricing structures.
Use Case: For scalable and durable object storage in the cloud.
Difference between on-premises, on-cloud, and hybrid cloud computing models
On-Premises Computing:
On-premises computing, also known as on-prem or on-premise, refers to the traditional model where an organization owns and operates its computing infrastructure, including servers, storage, networking, and other resources. All hardware is physically located within the organization's facilities (data centers or server rooms).
When to Use On-Premises Computing:
Data Sensitivity: For organizations with strict data privacy and security requirements, on-premises solutions provide direct control over the infrastructure.
Regulatory Compliance: In industries with specific regulatory requirements, maintaining on-premises infrastructure may be necessary to comply with standards.
Predictable Workloads: For stable and predictable workloads where the need for scalability is minimal.
On-Cloud Computing:
On-cloud computing, or cloud computing, refers to the model where computing resources are provided over the internet by third-party cloud service providers. Users can access and use resources on a pay-as-you-go basis without the need to invest in and maintain physical hardware.
When to Use On-Cloud Computing:
Scalability: For workloads with variable demand or unpredictable growth, cloud computing allows for easy scalability.
Cost Efficiency: Avoiding large upfront capital expenditures and paying only for actual usage is a priority.
Global Reach: Organizations with a global presence can leverage cloud services to deploy resources in multiple regions easily.
Hybrid Cloud Computing:
Hybrid cloud computing involves a combination of on-premises infrastructure and cloud services, creating an integrated and flexible environment. It allows data and applications to be shared between on-premises and cloud environments.
When to Use Hybrid Cloud Computing:
Data Governance: For organizations that need to keep certain data on-premises due to regulatory or compliance requirements.
Bursting Workloads: In situations where there are periodic spikes in demand, a hybrid approach allows organizations to scale to the cloud when needed.
Legacy Systems Integration: When transitioning from on-premises systems to the cloud, a hybrid approach allows for a gradual migration without disrupting existing operations.
IAAS vs PAAS vs SAAS
Understanding the difference between IAAS, PAAS & SAAS in layman's terms
History of AWS and key milestones and developments in its evolution
Amazon Web Services (AWS) has a rich history marked by key milestones and developments that have shaped its evolution into a dominant force in the cloud computing industry. Here's an overview of significant events in the history of AWS:
Launch of AWS (2006):
Date: March 14, 2006
Milestone: AWS was officially launched, offering its first service, Amazon Simple Queue Service (SQS). This marked the beginning of AWS as a provider of web services.
Introduction of Amazon Elastic Compute Cloud (EC2) (2006):
Date: August 25, 2006
Milestone: AWS introduced EC2, allowing users to rent virtual servers on which they could run their applications. This marked a significant shift in the way computing resources were provisioned.
AWS Expands Globally (2008):
- Milestone: AWS expanded its global infrastructure by adding a European region in Ireland. This marked the first step in building a global network of data centers.
Launch of Amazon Virtual Private Cloud (VPC) (2009):
Date: August 26, 2009
Milestone: Amazon VPC was introduced, allowing users to provision a logically isolated section of AWS where they could launch resources in a virtual network.
AWS Elastic Beanstalk (2011):
Date: January 19, 2011
Milestone: AWS Elastic Beanstalk was launched, simplifying the process of deploying and managing applications in the AWS cloud.
Introduction of AWS Marketplace (2012):
Date: April 19, 2012
Milestone: AWS Marketplace was introduced, providing a platform for buying and selling software that runs on AWS.
Launch of Amazon Redshift (2012):
Date: November 28, 2012
Milestone: AWS introduced Amazon Redshift, a fully managed data warehouse service designed for high-performance analysis using standard SQL queries.
AWS GovCloud (US) (2011) and other Regions (Ongoing):
- Milestone: AWS continued to expand its global footprint by adding new regions, including the launch of AWS GovCloud (US) in 2011 to meet the specific regulatory requirements of U.S. government agencies.
Introduction of AWS Lambda (2014):
Date: November 13, 2014
Milestone: AWS Lambda was launched, introducing serverless computing where users could run code without provisioning or managing servers.
Launch of Amazon Aurora (2015):
Date: July 28, 2015
Milestone: Amazon Aurora, a fully managed relational database service, was introduced, providing high performance, availability, and durability.
AWS Snowball (2015) and Snowmobile (2016):
- Milestone: AWS introduced Snowball, a petabyte-scale data transport solution, and later introduced Snowmobile, an exabyte-scale data transfer service, to help customers physically transfer large amounts of data to AWS.
AWS re: Invent Conference (Annual Event):
- Milestone: AWS started hosting the annual AWS re: Invent conference, becoming a major event for cloud professionals, featuring keynotes, sessions, and announcements about new services and features.
Introduction of AWS Glue and Amazon SageMaker (2017):
Date: November 29, 2017
Milestone: AWS introduced AWS Glue, a fully managed extract, transform, and load (ETL) service, and Amazon SageMaker, a fully managed machine learning service.
Launch of AWS Outposts (2019):
Date: December 3, 2019
Milestone: AWS Outposts were introduced, extending AWS infrastructure, services, and APIs to customer data centers, enabling a hybrid cloud approach.
Introduction of AWS Wavelength (2019):
- Milestone: AWS Wavelength was introduced, bringing AWS services to the edge of the 5G network to reduce latency for applications.
AWS Nitro System (Ongoing):
- Milestone: AWS introduced the Nitro System, an underlying platform for its EC2 instances that offloads many traditional virtualization functions to dedicated hardware and software.
AWS Graviton2 Processors (2020):
- Milestone: AWS launched EC2 instances powered by the Graviton2 processors, designed to deliver better price performance for a variety of workloads.
AWS continues to innovate and expand its services, regions, and capabilities, playing a pivotal role in the growth and evolution of cloud computing. The company's commitment to customer-centric innovation has solidified its position as a leader in the cloud services industry.