Cloud computing has become a critical foundation for modern businesses, enabling them to innovate, scale, and operate more efficiently. This page provides an overview of the fundamental concepts and technologies that underpin cloud computing.

Cloud computing is an IT environment where applications run and resources are shared across networks—using computer servers, virtualization, or containers—with on-demand provisioning of resources. We will explore key topics such as virtualization, cloud architecture, cloud deployment models, and cloud service models (IaaS, PaaS, FaaS, SaaS). 

Cloud computing services play a critical role in providing the computational processing needed for AI software and services. Understanding this technology is essential as generative AI is rapidly transforming enterprise operations that use vast amounts of data and computing power.

Companies across industries are leveraging cloud computing not only for routine operations but also for emerging applications—such as artificial intelligence, Internet of Things (IoT), and autonomous systems—that require the flexibility and global reach of cloud infrastructure.

Krasamo, a software development company with expertise in cloud computing, helps organizations leverage these concepts to build and deploy robust cloud solutions. Please refer to our ‘Cloud Consulting Services’ page for information on our cloud consulting services.

Virtualization

Virtualization is a foundational technology that powers cloud computing by enabling the creation of virtual versions of physical IT resources. It uses a specialized software layer to abstract physical hardware, allowing multiple, isolated virtual environments to run on a single physical machine. This process is what forms a virtual infrastructure, which is typically composed of the following key components:

• Physical Hosts: These are the physical servers that provide the underlying hardware resources, including CPU, memory, storage, and networking capabilities.
• The Hypervisor: Often called a virtual machine monitor (VMM), the hypervisor is a critical software layer that runs on the physical host. Its job is to manage the interaction between the physical hardware and the virtual machines. It abstracts the host’s resources and allocates them to each virtual environment.
• Virtual Machines (VMs): These are the self-contained, software-based emulations of a computer system. Each VM runs its own independent operating system and applications, using the virtual resources allocated to it by the hypervisor.

By creating a layer of abstraction between the hardware and the VMs, the hypervisor allows a company to run multiple different applications (e.g., a web server, a database server, and an email server) concurrently on a single physical host, with each workload isolated from the others.

Types of Virtualization

The principle of virtualization can be applied to various IT resources, each serving a different purpose:

• Server Virtualization: This is the most common type, where a physical server is partitioned into multiple virtual machines (VMs), each with its own operating system and applications.
• Storage Virtualization: Combines multiple physical storage devices into what appears to be a single virtual storage pool, simplifying management and data migration.
• Network Virtualization: Abstracts network resources, like switches and routers, into software, enabling greater flexibility and control over network traffic and security.
• Desktop Virtualization: Allows users to access their desktop environment from any device, as the desktop itself runs on a remote server instead of the local machine.

Benefits of Virtualization

Virtualization offers numerous strategic benefits for businesses:

• Cost Savings: By consolidating multiple workloads onto fewer physical servers, virtualization significantly reduces hardware, power, and cooling expenses.
• Increased Efficiency: Virtualization improves resource utilization, allowing businesses to get more value and performance out of their existing infrastructure.
• Scalability and Flexibility: Virtual machines can be created, cloned, moved, or resized on demand, allowing IT to scale resources up or down quickly to meet changing business needs.
• Improved Disaster Recovery: Virtualization simplifies the backup and recovery process. Entire VMs can be replicated and restored quickly, enabling faster recovery from outages.
• Enhanced Agility: With virtualization, new applications and services can be deployed in minutes rather than weeks, dramatically increasing business agility.

Cloud Architecture

Cloud Architecture refers to the high-level blueprint used to design and structure applications and systems within a cloud environment. The choice of architecture is critical as it dictates how an application will handle scalability, resilience, and performance, and how efficiently it will use cloud resources.

Cloud Architectural Styles

There are several common architectural styles used to build cloud applications:

• Microservices: This style structures an application as a collection of small, independent services that are developed, deployed, and scaled individually. They communicate with each other over well-defined APIs. As a firm with expertise in this area, Krasamo advocates for this style to achieve modularity and agility.
• Serverless: This event-driven architecture allows developers to build and run applications without managing the underlying server infrastructure. Code is executed in stateless compute containers that are triggered by events, and the cloud provider handles all provisioning and scaling.
• Event-Driven: A broader architectural pattern where components communicate asynchronously through the production and consumption of events. This style is highly effective for building responsive, real-time systems and is often used in conjunction with microservices and serverless architectures.
• Monolithic: A traditional architectural style where all components of an application are tightly coupled into a single, large codebase. While less agile than modern styles, it can be simpler to develop and deploy for smaller, less complex applications.

Containerization

Containerization is a lightweight form of virtualization that packages an application’s code along with all its dependencies—such as libraries and configuration files—into a single, self-contained unit called a container. Unlike virtual machines, containers share the host system’s operating system kernel, making them highly efficient and portable.

• Docker: An open-source platform that popularized containerization. Docker provides the tools to build, run, and share containers easily.
• Kubernetes: An open-source container orchestration platform for automating the deployment, scaling, and management of containerized applications. It groups containers into logical units, managing their lifecycle across a cluster of machines to ensure resilience and scalability.

Benefits of Containerization:

• Portability: Containers run consistently across any environment, from a developer’s laptop to on-premises data centers and any public cloud.
• Efficiency: Because they share the host OS kernel and don’t require a full guest OS, containers are much smaller and start faster than VMs.
• Scalability: Containers can be scaled horizontally (by adding more instances) with ease, especially when managed by an orchestrator like Kubernetes.

Cloud Infrastructure

Cloud Infrastructure refers to the underlying hardware and software components that are the foundation of a cloud computing environment. These are the resources made available by cloud providers on which all services and applications are built.

Key Components of Cloud Infrastructure

• Compute: Provides the processing power. This includes virtual machines (VMs), bare-metal servers, and the serverless computing engines that run code in response to events.
• Storage: Encompasses a wide variety of data storage options, such as scalable object storage for unstructured data, high-performance block storage for databases, and shared file storage.
• Networking: Includes the software-defined networks and services that connect everything. This involves virtual networks, load balancers to distribute traffic, DNS for name resolution, and gateways.
• Management and Orchestration: The tools and services used to provision, configure, and automate cloud resources. A key example is a container orchestration platform like Kubernetes, which automates the lifecycle of containerized applications.

Cloud Computing Deployment Models

Cloud computing deployment models define where cloud infrastructure and resources are located and how they are accessed. The three main deployment models are public, private, and hybrid.

Public Cloud

Public clouds are owned and operated by third-party providers (e.g., AWS, Azure, GCP), who make computing resources (servers, storage, etc.) available to the general public over the internet.

Characteristics:

• Multi-tenant: Resources are shared among multiple customers.
• On-demand: Resources can be accessed and scaled as needed.
• Pay-as-you-go: Customers typically pay only for the resources they consume.
• Wide range of services: Public clouds offer a broad selection of services, including compute, storage, databases, and more.

Advantages:

• Scalability and elasticity: Resources can be easily scaled to handle fluctuating workloads.
• Cost-effectiveness: No upfront investment in hardware; pay-as-you-go pricing.
• High availability and reliability: Cloud providers offer robust infrastructure with redundancy and failover.
• Reduced management overhead: The provider handles infrastructure maintenance.

Disadvantages:

• Security concerns: Sharing resources with other tenants may raise security concerns for some organizations.
• Less control: Organizations have less control over the underlying infrastructure compared to private clouds.
• Potential vendor lock-in: Migrating between public cloud providers can be complex.

Examples:

• Amazon Web Services (AWS)
• Microsoft Azure
• Google Cloud Platform (GCP)

Private Cloud

Private clouds are cloud computing environments dedicated to a single organization. They can be hosted on-premises (within the organization’s data center) or off-premises (at a third-party provider’s data center). For example, it can be deployed in a Google Cloud Platform (GCP) data center.

Characteristics:

• Single-tenant: Resources are dedicated to a single organization.
• Greater control: Organizations have more control over the infrastructure and security.
• Customization: Private clouds can be customized to meet specific needs.

Advantages:

• Enhanced security and compliance: Dedicated resources and greater control over security measures.
• Customization: Can be tailored to specific requirements and legacy systems.
• Predictable costs: Private clouds can offer more predictable costs for stable workloads.

Disadvantages:

• Higher costs: Requires significant upfront investment in hardware and software (if on-premises).
• Responsibility for management: The organization is responsible for managing and maintaining the infrastructure (if on-premises).
• Limited scalability: Scalability may be limited compared to public clouds.

Examples:

• On-premises private cloud built with virtualization technologies (e.g., VMware, OpenStack).
• Hosted private cloud offered by a cloud provider.

Hybrid Cloud

Hybrid clouds combine public and private cloud environments, allowing data and applications to be shared between them. 

Characteristics:

• Combination of public and private clouds.
• Data and application portability between clouds.
• Orchestration and management across multiple environments.

Advantages:

• Flexibility: Organizations can choose the best environment for each workload.
• Scalability: Public cloud can be used to handle spikes in demand.
• Cost optimization: Organizations can optimize costs using the most cost-effective environment for each task.
• Legacy system integration: Hybrid clouds allow organizations to integrate existing on-premises systems with cloud resources.

Disadvantages:

• Complexity: Managing a hybrid cloud environment can be complex.
• Security challenges: Ensuring consistent security across multiple environments can be challenging.
• Integration issues: Integrating different cloud environments can be difficult. 

Multi-Cloud

A multi-cloud strategy involves using cloud services from multiple public cloud providers.

Characteristics:

• Use of services from two or more public cloud vendors (e.g., AWS, Azure, GCP).
• Workloads are distributed across different cloud providers.
• Focus on leveraging the specific strengths of each cloud provider.

Advantages:

• Avoid vendor lock-in: Reduces dependence on a single cloud provider.
• Best-of-breed services: Allows organizations to choose the most suitable services from each provider.
• Increased resilience: Distributing workloads across multiple clouds can improve fault tolerance.
• Compliance and data sovereignty: Can help meet specific regulatory or data location requirements.

Disadvantages:

• Increased complexity: Managing multiple cloud environments can be more complex.
• Integration challenges: Integrating services and data across different cloud providers can be difficult.
• Higher management overhead: Requires expertise in managing multiple cloud platforms.

Why Multi-Cloud is Becoming Widely Used

• Avoiding Vendor Lock-in: Organizations want to avoid being locked into a single provider’s ecosystem, which can limit flexibility and increase costs.
• Leveraging Specialized Services: Cloud providers excel in different areas (e.g., AWS for computing, Google Cloud for AI/ML). Multi-cloud allows businesses to use the best services for their needs.
• Improving Resilience: Distributing workloads across multiple clouds can enhance business continuity and disaster recovery. If one cloud provider experiences an outage, applications can continue to run on others.
• Meeting Compliance Requirements: Multi-cloud can help organizations meet data sovereignty and compliance requirements by storing data in specific geographic locations offered by different providers.

Cloud Computing Services

Cloud computing services are categorized into different models, often called the “Cloud Computing Stack.” Each model provides a different level of abstraction, with the cloud provider managing more of the underlying infrastructure as you move up the stack.

Infrastructure-as-a-Service (IaaS)

IaaS provides access to fundamental computing resources—virtual servers, storage, and networking—over the internet. The cloud provider manages the infrastructure, while the customer is responsible for the operating system, applications, and data.

Characteristics:

• Customers rent IT infrastructure—servers, VMs, storage, networks, operating systems—from a cloud provider. 
• Customers have the most control over their infrastructure.
• Customers are responsible for managing the OS, middleware, applications, and data.

Key Components and Technologies:

• Compute: Access to processing resources like Virtual Machines (VMs) and dedicated bare-metal servers.
• Storage: Access to various data storage services, including high-performance block storage and scalable object storage.
• Networking: Access to networking components like Virtual Private Clouds (VPCs), load balancers, and DNS.

Examples:

• AWS EC2 (virtual servers)
• Azure Virtual Machines
• Google Compute Engine
• AWS S3 (object storage)
• Azure Blob Storage
• Google Cloud Storage

Use Cases:

• Hosting websites and web applications
• Running development and testing environments
• Storing and processing large amounts of data
• Disaster recovery

Platform-as-a-Service (PaaS)

PaaS provides a cloud environment for developing, deploying, and managing software applications. The cloud provider manages the infrastructure and platform while the customer focuses on developing the applications. 

Characteristics:

• Provides a platform for software development, including tools, libraries, and services. 
• Reduces the need for customers to manage infrastructure.
• Supports the entire software development lifecycle. 

Key Components and Technologies:

• Development frameworks (e.g., Java Spring Framework). 
• Runtime environments (e.g., Apache Tomcat, Payara Platform). 
• Databases and data services.
• IoT platforms. 

Examples:

• AWS Elastic Beanstalk
• Azure App Service
• Google Cloud Run

Use Cases:

• Developing and deploying web applications
• Building mobile applications
• Analyzing data
• Developing IoT solutions

Data Platforms (Detailed)

Data Lake:

• A storage repository for raw data in its native format. 
• Provides flexibility and data availability. 

Data Warehousing:

• A structured repository for filtered and processed data designed to support decision-making. 

Cloud Data Platforms:

• Services that manage data resources across infrastructures (e.g., Snowflake). 

Function-as-a-Service (FaaS)

FaaS is a serverless computing model that allows developers to run code (functions) without managing servers. The cloud provider automatically provisions and scales resources based on event triggers. 

Characteristics:

• Serverless: No server management is required. 
• Event-driven: Code executes in response to events. 
• Pay-per-use: Pay only for the compute time consumed.

Examples:

• AWS Lambda 
• Azure Functions 
• Google Cloud Functions 

Use Cases:

• Processing data
• Building APIs
• Responding to events

Backend-as-a-Service (BaaS)

BaaS provides developers with a cloud platform that handles the backend infrastructure and services for mobile and web applications. 

Characteristics:

• Provides pre-built backend services (e.g., authentication, databases, storage, push notifications). 
• Simplifies mobile and web app development.

Examples:

• Firebase 

Use Cases:

• Developing mobile applications
• Building single-page web applications

Software-as-a-Service (SaaS)

SaaS delivers software applications over the internet, on-demand, and typically by subscription. The cloud provider manages the software and infrastructure, and users access the applications through a web browser or API. 

Characteristics:

• Software is accessed over the internet. 
• No installation or downloads are required. 
• Provider manages the software and infrastructure.

Examples:

• Salesforce 
• Microsoft Office 365
• Google Workspace

Use Cases:

• Email
• CRM
• Productivity applications

Cloud-Native Applications

Cloud-Native Applications are applications specifically designed and built to leverage the full capabilities of cloud computing. This approach combines modern architectural styles, technologies, and development practices to create systems that are scalable, resilient, and agile.

Core Principles of Cloud-Native Development

Building cloud-native applications is an approach that integrates several of the concepts discussed previously:

• Architected as Microservices: Applications are broken down into independent services, following the microservices style to allow for independent scaling and updates.
• Packaged in Containers: Each microservice is packaged as a lightweight container, ensuring it runs reliably and consistently across all environments. This relies on technologies like Docker and orchestration platforms like Kubernetes.
• Enabled by DevOps and CI/CD: Cloud-native development is powered by a DevOps culture, where development and operations teams collaborate. This is put into practice through Continuous Integration/Continuous Delivery (CI/CD) pipelines, which automate the process of building, testing, and deploying code changes frequently and reliably.
  • Continuous Integration (CI): Developers merge code changes into a central repository, triggering automated builds and tests.
  • Continuous Delivery (CD): Code changes that pass the CI stage are automatically prepared and released to production.

Benefits of Cloud-Native Applications

Adopting a cloud-native approach offers the ultimate benefits of the cloud:

• Maximum Agility: Faster development and automated deployment cycles enable businesses to respond quickly to market changes.
• Superior Scalability: Applications can automatically and granularly scale based on demand.
• Enhanced Resilience: Architectures are designed to be fault-tolerant, so if one component fails, the overall application remains available.

Krasamo’s Cloud Consulting Services

In addition to providing this overview of cloud computing concepts, Krasamo offers a range of cloud consulting services to help businesses plan, implement, and optimize their cloud solutions. Our expertise includes:

• Multi-Cloud Strategy: We advise clients on cloud readiness, develop migration plans, and design cloud operating models to establish best practices and avoid vendor lock-in.
• Cloud Architecture and Infrastructure: We design scalable and resilient cloud architectures, select optimal infrastructure components, and implement CI/CD pipelines.
• Cloud Migration: We guide organizations through migrating workloads to public, private, hybrid, or multi-cloud environments.
• Cloud-Native Development: We build cloud-native applications using microservices, containers, and serverless technologies and provide DevOps consulting.
• Cloud Service Models: We help businesses choose and implement the most appropriate cloud service models to meet their specific needs.
• Cloud Optimization: We assist clients in optimizing cloud costs, performance, and resource utilization.

By leveraging Krasamo’s cloud consulting expertise, businesses can:

• Accelerate their cloud adoption journey.
• Achieve their business goals effectively.
• Manage the complexity of cloud environments.
• Drive innovation and growth.

Please contact our cloud team for more information or to discuss your specific needs.