Cloud Computing: Storage, Architecture, and Migration

Posted on Apr 6, 2025 in Technology

Data Storage Comparison

Cloud Data Management

Cloud data management is the process of storing, accessing, securing, and analyzing data in cloud environments. It ensures availability, security, and efficiency in handling data across distributed systems.

Key Components of Cloud Data Management

• Data Storage: Data is stored in cloud-based storage solutions such as Object Storage (e.g., Amazon S3), Block Storage, and File Storage. Storage is scalable, redundant, and spread across multiple data centers for high availability.
• Data Integration: Cloud data management integrates different data sources (databases, applications, IoT devices) into a unified platform. ETL (Extract, Transform, Load) and data pipelines help move data from on-premises systems to the cloud.
• Data Security and Compliance: Uses encryption, identity access management (IAM), multi-factor authentication (MFA), and security monitoring tools. Ensures compliance with regulations like GDPR, HIPAA, and CCPA for data privacy.
• Data Backup and Disaster Recovery: Regular backups ensure data is safe from accidental deletion or cyber threats. Disaster recovery (DR) strategies involve redundant storage and failover systems to restore operations quickly.
• Data Governance and Access Control: Defines who can access, modify, and delete data using Role-Based Access Control (RBAC). Ensures data lineage, audit trails, and logging for compliance and security.

HDFS (Hadoop Distributed File System)

HDFS is a distributed file system designed for big data storage and processing. It follows a master-slave architecture and is optimized for handling large files across a distributed environment.

Working of HDFS

1. File Write Operation
2. File Read Operation
3. Block Replication
4. Heartbeat and Block Reports

Cloud Data Storage Challenges

1. Security and Privacy
2. Data Loss and Recovery
3. Compliance and Legal Issues
4. Latency and Performance Issues
5. Vendor Lock-in
6. Data Management and Organization
7. Hidden Costs
8. Access Control and Identity Management
9. Data Sovereignty Concerns

Seven-Step Model for Cloud Migration

Migrating to the cloud is a structured process to ensure a smooth transition with minimal risks. The seven-step model for cloud migration helps organizations move their workloads efficiently.

1. Assess: Analyze the current IT infrastructure, applications, and business needs. Identify workloads that can be migrated and evaluate cloud benefits. Assess compliance, security, and cost implications.
2. Plan: Develop a migration strategy based on the assessment. Choose the right cloud model (Public, Private, Hybrid) and service model (IaaS, PaaS, SaaS). Define migration timelines, budget, and key stakeholders.
3. Design: Create an architecture blueprint for the cloud environment. Ensure security, scalability, and network configurations are in place. Develop data migration and integration plans.
4. Pilot: Test migration with a small workload (proof of concept). Validate cloud performance, security, and compatibility with existing systems. Identify potential risks and optimize before full migration.
5. Migrate: Execute the migration in phases, prioritizing critical workloads. Use appropriate migration techniques like rehosting, refactoring, or re-platforming. Monitor the process to ensure minimal downtime and data integrity.
6. Validate: Perform thorough testing to verify application performance and security. Ensure that data integrity and compliance requirements are met. Optimize resources to improve cost efficiency and performance.
7. Operate & Optimize: Continuously monitor and manage cloud resources. Implement automation for scaling, security, and maintenance. Optimize cloud usage for cost efficiency.

Cloud Deployment Models

Cloud deployment models define how cloud resources are deployed and managed based on ownership, accessibility, and purpose. There are four main cloud deployment models:

• Public Cloud: Owned and operated by third-party providers like AWS, Microsoft Azure, or Google Cloud. Resources are shared among multiple users (multi-tenant). Cost-effective, scalable, but less control over data security.
• Private Cloud: Dedicated cloud infrastructure for a single organization. Can be on-premise or hosted by a third-party provider. Offers greater security, control, and compliance but is costlier.
• Hybrid Cloud: A combination of public and private clouds for flexibility. Critical data is stored in a private cloud, while less-sensitive workloads run on the public cloud. Provides scalability and security but requires complex integration.
• Community Cloud: Shared cloud infrastructure among organizations with common concerns (e.g., government, healthcare). Offers enhanced security and compliance but has limited scalability.

Cloud Computing Definition and Importance

Cloud computing is the delivery of computing services—including servers, storage, databases, networking, software, and analytics—over the internet (“the cloud”) instead of relying on local servers or personal computers. It enables users to access and manage data, applications, and IT resources on-demand, with scalability and flexibility.

Importance of Cloud Computing

• Cost Efficiency: Reduces capital expenses by eliminating the need for on-premise hardware and maintenance.
• Scalability: Allows businesses to scale resources up or down as needed.
• Flexibility & Accessibility: Enables access to data and applications from anywhere with an internet connection.
• Security: Provides built-in security measures, including encryption and compliance with industry standards.
• Disaster Recovery: Ensures business continuity with automated backups and recovery options.
• Collaboration: Enhances teamwork by enabling real-time file sharing and communication.

Cloud Service Models

Cloud computing offers three primary service models, each catering to different needs in terms of control, flexibility, and management.

1. Infrastructure as a Service (IaaS)

Definition: IaaS provides virtualized computing resources such as servers, storage, and networking over the internet.

Key Features: Offers scalability and flexibility. Users manage operating systems, applications, and data. The cloud provider manages the hardware and networking.

Examples: Amazon Web Services (AWS) EC2, Google Compute Engine (GCE), Microsoft Azure Virtual Machines

Use Cases: Hosting websites and applications, big data processing and analytics, disaster recovery and backup.

2. Platform as a Service (PaaS)

Definition: PaaS provides a complete development and deployment environment, including tools and infrastructure, to build, test, and deploy applications.

Key Features: Developers focus on coding, while the cloud provider manages the infrastructure. Built-in tools for testing, security, and database management. Supports multiple programming languages and frameworks.

Examples: Google App Engine, Microsoft Azure App Services, Heroku

Use Cases: Developing web and mobile applications, automating software deployment, continuous integration and delivery (CI/CD).

3. Software as a Service (SaaS)

Definition: SaaS delivers fully managed software applications over the internet, eliminating the need for installation or maintenance.

Key Features: No need for infrastructure management or software installation. Accessible from any device with an internet connection. Regular updates and security patches provided by the vendor.

Examples: Google Workspace (Docs, Sheets, Gmail, etc.), Microsoft 365 (Word, Excel, Outlook, etc.), Dropbox, Salesforce, Zoom

Use Cases: Cloud-based collaboration tools, customer relationship management (CRM), email, communication, and file sharing.

Google File System (GFS)

Google File System (GFS) is a scalable, distributed file system developed by Google for handling large-scale data processing workloads. Here are the key features of the GFS architecture:

Key Features of GFS

1. Scalability: Designed to handle petabytes of data distributed across thousands of machines. Can efficiently manage millions of files and large-scale workloads.
2. Fault Tolerance & High Availability Replication: Each file is divided into chunks (64MB each) and replicated (default is 3 copies) across multiple machines to prevent data loss. Automatic recovery: If a machine or chunk fails, the system automatically restores the lost chunks from other replicas. Self-healing mechanism: The system continuously monitors and rebalances data distribution.
3. Master-Slave Architecture: Single Master Node: Manages metadata, chunk locations, and file system namespace. Chunkservers (Workers): Store actual file chunks and serve client requests. Clients: Communicate with the master for metadata but access chunkservers directly for data reads/writes, reducing the master’s load.

Cloud System Architecture

Cloud system architecture refers to the design and structure of cloud computing environments. It defines how different cloud components interact to deliver scalable, on-demand computing resources over the internet.

Key Components of Cloud System Architecture

• Frontend (Client-Side): Includes web browsers, mobile apps, and client devices. Users interact with cloud services through user interfaces (UIs) or APIs.
• Backend (Cloud Infrastructure):
  • Application Layer: Hosts and runs cloud applications.
  • Service Layer: Manages cloud services like SaaS, PaaS, and IaaS.
  • Resource Management Layer: Allocates computing resources dynamically.
  • Virtualization Layer: Abstracts physical hardware to create virtual machines (VMs) and containers.
  • Physical Layer: Consists of data centers, servers, and storage devices.

Cloud Delivery Models

• Infrastructure as a Service (IaaS): Provides virtualized computing resources.
• Platform as a Service (PaaS): Offers development platforms and tools.
• Software as a Service (SaaS): Delivers software applications over the internet.

Deployment Models

• Public Cloud: Services provided by third-party vendors (e.g., AWS, Google Cloud).
• Private Cloud: Dedicated infrastructure for a single organization.
• Hybrid Cloud: Combination of public and private cloud for flexibility.
• Multi-Cloud: Use of multiple cloud providers for redundancy and performance.

Pros and Cons of Cloud Computing

Cloud computing has revolutionized IT infrastructure, offering several advantages and some drawbacks. Here’s a breakdown:

Pros (Advantages)

• Cost Efficiency: Reduces upfront capital expenses for hardware and infrastructure. Follows a pay-as-you-go model, charging only for what you use.
• Scalability and Flexibility: Easily scales up or down based on demand. Supports businesses of all sizes, from startups to enterprises.
• Accessibility and Mobility: Access cloud services anytime, anywhere using an internet connection. Enables remote work and global collaboration.
• Disaster Recovery and Data Backup: Cloud providers offer automated backups and disaster recovery solutions. Ensures minimal downtime and data loss in case of failures.
• Automatic Updates and Maintenance: Cloud providers handle software updates, security patches, and maintenance. Reduces IT management overhead.

Cons (Disadvantages)

• Security and Privacy Risks: Storing sensitive data in the cloud poses risks of hacking, data breaches, and unauthorized access. Compliance with data protection regulations is essential.
• Internet Dependency: Requires a stable internet connection to access cloud services. Downtime or poor connectivity can disrupt operations.
• Limited Control and Customization: Users rely on cloud providers for infrastructure control. Some businesses may require more customization than what public cloud services offer.
• Hidden Costs: While cloud computing reduces initial investment, unexpected costs from data transfer, storage, and scaling can accumulate. Need for cost optimization strategies.
• Vendor Lock-In: Moving data between cloud providers can be complex and costly. Dependency on a single provider may limit flexibility.