Cloud Architecture and Deployment Models

Cloud Models and Capacity Planing

1. Cloud Deployment Models

Public Cloud

• Infrastructure hosted in a third party's cloud.

• Subscription-based model; you don't own the infrastructure.

• Utilizes shared tenancy.

• Examples: Amazon Web Services, Google Cloud, Azure.

Private Cloud

• Infrastructure is on-premises and owned by your company.

• Includes bare metal and virtual machines.

• Can be considered a private cloud if managed as a service.

• No reliance on public cloud infrastructure.

Community Cloud

• Specific consumers with unique needs share the same infrastructure.

• Managed by community members or a third party.

• Not available for general subscription.

Hybrid Cloud

• Combination of two or more deployment models (public, private, community).

• Often combines public cloud with on-premises data center.

• Requires private networking infrastructure between clouds.

Cloud within a Cloud or Virtual Private Cloud (VPC)

• Utilizes a provider's infrastructure to create an isolated data center network.

• Unshared by default and not accessible to other customers.

• Enables customers to have single tenancy within a multi-tenant infrastructure.

Multi-Tenancy

• Multiple customers share the same hardware resources simultaneously.

• Main business model for public cloud providers.

• Bare metal usage is possible but less common.

Multi-Cloud

• Combines a private cloud deployment with multiple public cloud providers.

• Strategy to reduce vendor lock-in with a single provider.

These deployment models offer various options for designing cloud solutions, each with its unique characteristics and advantages. Understanding these models is essential for effective cloud architecture design and decision-making

2. Cloud Service Models

In addition to cloud deployment models, it's important to understand cloud service models and the shared responsibility model. These concepts often overlap and are fundamental in cloud computing. Let's delve into cloud service models:

Infrastructure as a Service (IaaS)

• Description: The foundational layer of the technology stack in cloud computing.

• Components: Includes data centers, physical infrastructure (building, power, cooling, cabling, racks), network infrastructure, hardware, and virtualization.

• Examples: Virtual private cloud (VPC), launching a virtual machine.

• Characteristics: Offers high flexibility for resource customization. Customers have significant responsibilities for managing and maintaining resources post-creation.

Platform as a Service (PaaS)

• Description: Builds on top of IaaS, adding operating systems and software management by the provider.

• Components: Includes IaaS elements (data center, networking, servers, virtualization) plus the operating system and software stack.

• Examples: AWS Elastic Beanstalk, Heroku, Azure Function Apps.

• Characteristics: Reduces customer responsibilities and tasks compared to IaaS, but offers less configuration flexibility.

Software as a Service (SaaS)

• Description: The top layer of cloud service models, where the provider manages the entire hosted application.

• Components: Fully managed application, including infrastructure, software, and services.

• Examples: Google Workspace, Salesforce.

• Characteristics: Offers the least flexibility in terms of customer configuration but requires minimal operational overhead.

These service models represent different levels of abstraction and responsibility within the cloud computing ecosystem. Understanding them is crucial for selecting the right model to meet specific business needs and requirements.

3. Shared Responsibility Model in Cloud Computing

The shared responsibility model is a crucial concept in cloud computing that outlines the responsibilities of both the cloud provider and the customer. It defines the level of ownership and responsibility for various aspects of the product and its use. Let's explore how the shared responsibility model applies to different cloud service models:

Infrastructure as a Service (IaaS)

• Provider's Responsibilities:

  • Data centers and hardware.

  • Global network infrastructure.

  • Server hardware, compute, storage, database, and network infrastructure.

• Customer's Responsibilities:

  • Operational overhead for the operating system.

  • Network configuration.

  • Firewall configuration for resources.

  • Platform and application management on top of the infrastructure.

  • Server-side encryption.

  • Protection of network traffic.

  • Protection of data, including data encryption.

  • Data integrity checking.

  • Data management.

Platform as a Service (PaaS)

• Provider's Responsibilities:

  • Operating system.

  • Networking.

  • Firewalling.

  • Platform and application management.

  • Server-side encryption.

  • Data integrity.

• Customer's Responsibilities:

  • Client-side data encryption.

  • Network traffic protection.

  • Data management

Software as a Service (SaaS)

• Provider's Responsibilities:

  • 100% of networking, including network traffic protection.

• Customer's Responsibilities:

  • Optional client-side data encryption.

  • Data management.

In the SaaS model, the provider assumes most of the operational responsibilities, including network management and data integrity, while the customer's focus is primarily on data management. It's essential to understand this shared responsibility model to ensure that security and compliance requirements are met when using cloud services effectively.

4. Capacity Planning: Understanding Requirements

Capacity planning involves considering various factors that contribute to determining the resources needed for a project or system. One crucial aspect of capacity planning is understanding and documenting requirements, which can encompass technical, business, and other relevant factors.

1) Requirements

Let's explore the elements involved in defining requirements.

Business Needs Analysis

• Purpose: The starting point for capacity planning is to conduct a business needs analysis to identify what the business will require.

• Importance: This analysis forms the foundation for the entire capacity planning process.

Hardware Requirements

• Derived from: Business needs analysis.

• Definition: Hardware requirements specify the physical infrastructure needed to support the project or system. This includes servers, storage, networking equipment, and more.

Virtualization Requirements

• Derived from: Business needs analysis (if virtualization is the chosen approach).

• Definition: Virtualization requirements outline the capacity needed for virtualized resources, such as virtual machines, containers, or cloud instances.

Software Requirements

• Derived from: Business needs analysis.

• Scope: Software requirements encompass various software components, which may include off-the-shelf products, custom software, security software, auditing tools, and more.

Budget Considerations

• Influence: Business needs analysis contributes to budgeting decisions.

• Budget Types: Include development budget, operational budget, and security budget, taking into account costs for creating, maintaining, and securing the product or system.

2) Standard Template for Documentation

• Purpose: Establish a consistent format for documenting requirements.

• Components: Define what the document should contain, including sections, required information, optional details, and roles of approvers.

Creating a well-structured and comprehensive document that addresses these requirements is essential for effective capacity planning. It ensures that the necessary resources are allocated to meet the project or system's needs while staying within budget constraints.

3) Licensing Models and Factors

In capacity planning, it's crucial to consider various licensing models and factors that influence resource allocation and usage. Let's explore different licensing models and the key capacity planning factors:

Licensing Models

1. Per User Licensing

   • Description: Each user who uses the software requires a separate license, regardless of frequency.

2. Socket-Based Licensing

   • Description: Licensed per overall processor (socket), regardless of the number of cores within that socket.

3. Core-Based Licensing

   • Description: Licensed per core, meaning each core on a CPU requires a separate license.

4. Volume-Based Licensing

   • Description: Based on the number of installations rather than the number of individual users. Even unused installations count against the license.

5. Perpetual Licensing

   • Description: Pay once upfront, and the license remains valid indefinitely. Limited access to support and upgrades may apply.

6. Subscription-Based Licensing

   • Description: Pay on a periodic basis (e.g., daily, monthly, yearly) with potentially easier upgrades and better support.

4) User Density

The number of users simultaneously using the product. High concurrent users can lead to capacity challenges.

5) System Load

Monitoring Key Performance Indicators (KPIs) to assess the infrastructure's overall workload and performance.

6) Trend Analysis

Utilize metrics from user density, system load, and license usage to establish baselines of normal behavior. Monitor for anomalies that may require capacity adjustments.

7) Performance Monitoring

Ongoing monitoring of application performance, including user experience quality, CPU, memory, and disk usage, to ensure optimal resource allocation.

Effective capacity planning considers these licensing models and factors to ensure that resources are allocated efficiently and in line with business needs, user demands, and budget constraints.

High availability and scaling in cloud environments

Key Definitions for High Availability

Before delving into the concept of high availability, it's essential to clarify several technical terms and phrases. Here are the definitions of these terms:

1. Business Continuity

   • Definition: The strategy and processes aimed at ensuring a business remains functional in some capacity during or after a critical incident. It encompasses more than just compute infrastructure or data centers and can apply to various aspects of a business, including office space, management resource redundancy, and finances.

   • How to keep the business functional in some capacity during/after a critical incident.

2. Disaster Recovery (DR)

   • Definition: The process of saving and recovering data and other critical business processes during or after a critical incident. It involves having a well-defined plan or playbook with steps to follow for recovery, which directly contributes to business continuity.

   • How to save and recover data and other business processes during /after a critical incident. Contributes to the Business Continuity Plan.

3. Recovery Time Objective (RTO)

   • Definition: A documented time value that represents the maximum allowable delay between when services become interrupted and when they must be fully restored. RTO outlines the acceptable downtime for services during a disaster or incident.

4. Recovery Point Objective (RPO)

   • Definition: A time-based metric that defines the maximum acceptable amount of time since the last data recovery point. RPO specifies the maximum data loss that is deemed acceptable in terms of time.

5. High Availability (HA)

   • Definition: The characteristic of a system where it continues to function despite the complete failure of any component within the architecture. However, HA acknowledges that there may be an interruption of service, but it should not exceed the time limits defined by the Recovery Time Objective (RTO).

6. Redundancy

   • Definition: Redundancy is closely related to high availability but differs in that it ensures a system continues to function without degradation in performance even in the event of a complete component failure. Redundancy does not imply any interruption of service and is typically more challenging to achieve than simple high availability.

High Availability Patterns and Geographic Resource Separation.

High availability often involves implementing patterns such as failover and failback to ensure system resilience. Let's explore these patterns and the concept of geographic resource separation:

• Failover: In the event of an outage, like an on-prem data center failure, a failover can be implemented to serve the same workload from a public cloud. This may require changing DNS settings to point to the public cloud endpoint.

• Failback: After the on-prem or private cloud infrastructure becomes healthy again, a failback can be performed by making another DNS change to move end users or clients from the public cloud back to the on-prem infrastructure.

Geographic Resource Separation

• Availability Zone: An availability zone comprises one or more data centers, which appear as a single group of infrastructure to end users. While individual resources within an availability zone might lack redundancy, combining resources across multiple availability zones can create an overall highly available infrastructure.

• Region: A region consists of multiple physically separate availability zones. Unlike availability zones, which may be close together, regions are separated by larger distances, often tens or hundreds of miles or kilometers. This separation is designed to enhance resilience. Public cloud services are primarily hosted across regions, making it a common unit of resource scope, especially for Software as a Service (SaaS) offerings.

Scaling for High Availability

Scaling is a fundamental approach to achieving high availability in infrastructure. Let's explore different types of scaling and how they can be leveraged:

Horizontal Scaling

• Description: Horizontal scaling involves adding or removing resources to handle changes in workload demand. It is commonly used in public cloud infrastructure and is designed to enhance availability.

• Example: In a two-tier architecture with a load balancer and a fleet of virtual machines serving as application servers, as metrics (such as KPIs) indicating increased activity cross a threshold, you can scale out by adding more individual virtual machines to handle the application load. When reducing resources due to decreased demand, it is called scaling in.

Vertical Scaling

• Description: Vertical scaling is typically performed within a single server or virtual machine by adding resources like memory, CPU, disk space, or network throughput. It is often suitable for private clouds or on-premises data centers.

• Considerations: Vertical scaling may involve an interruption of service, as you may need to power off the virtual machine or shut down the hardware to make resource upgrades.

Advanced Scaling Strategies for High Availability

In addition to the previously discussed scaling mechanisms (horizontal, vertical, and geographical scaling), there are other innovative approaches to achieve high availability:

Cloud Bursting

• Description: Cloud bursting is used when an on-prem data center reaches saturation or capacity limits, either due to physical constraints or a lack of recent server provisioning. In this scenario, additional capacity is added in a public cloud infrastructure to handle the overflow of work.

Oversubscription

• Description: Oversubscription is primarily employed for cost efficiency. It involves provisioning virtual resources that collectively exceed the available physical resources. This approach assumes that saturation will not occur, making it a common pattern for public cloud providers. Oversubscription can apply to CPU, memory, and network throughput.

Hypervisor Affinity

• Description: Hypervisor affinity prioritizes placing resources physically and virtually close together to achieve high network throughput and low latency. This is suitable for scenarios where most traffic occurs between nodes of a workflow, and resilience is not a top priority. However, it poses a risk of simultaneous failure if resources are clustered too closely (e.g., within the same rack during maintenance).

Hypervisor Anti-Affinity

• Description: In contrast to hypervisor affinity, hypervisor anti-affinity ensures that resources are separated physically, at least within separate racks or even different data centers or regions. This approach is suitable for critical instances that must remain independent of each other to maximize availability and resilience, minimizing the risk of simultaneous outages.

Identifying and Eliminating Single Points of Failure

Improving the reliability and availability of an infrastructure often involves identifying and removing single points of failure. These vulnerabilities can manifest in various components of an infrastructure, including network equipment, servers, hardware, virtual machines, storage appliances, and database instances. While eliminating all single points of failure is challenging, especially in private cloud implementations, it's easier in public cloud environments where the responsibility for resilience is delegated to the provider. Here's an example using Amazon Web Services (AWS) to demonstrate how to avoid single points of failure:

1. Initial State:

   • Application server running on a single virtual machine.

   • DNS configured to point directly to the public IP address or DNS of the virtual machine.

   • Tightly coupled setup, posing a single point of failure.

2. Improvements:

   • Load Balancer: Split out the reverse proxy functionality into a load balancer, like AWS's Application Load Balancer. It allows for multiple availability zones and offers redundancy and automatic scaling.

   • Application Servers: Instead of a single virtual machine, use multiple virtual machines for the application servers. Employ auto-scaling to scale horizontally, ensuring redundancy.

   • Database: Utilize AWS's Aurora Serverless, a relational database service using MySQL or Postgres. Rather than running on discrete virtual machines, it employs Aurora Compute Units (ACUs) with CPU and memory. All ACUs are fronted by a single proxy endpoint, directing queries to the next available ACU.

By implementing this architecture, most potential single points of failure from the initial deployment are eliminated, significantly improving reliability and availability.

However, it's essential to recognize that increasing availability and reliability may come with increased costs and operational overhead. Decisions about where to prioritize resources and how to balance these factors depend on specific infrastructure requirements and organizational priorities.

Solution Design

Requirements Analysis

A business requirements analysis is a crucial step in understanding and documenting the needs and priorities of both users and the business when planning for cloud adoption. Here are the key elements of a business requirements analysis:

1. Software:

   • Users: Desire a familiar interface, easy-to-understand billing, and user-friendly administration tools.

   • Business: Requires scalability, cloud-native architecture, customizability, and the option for cloud service provider (CSP) management.

2. Hardware:

   • Users: May prioritize multiple platform support and Anywhere access.

   • Business: Emphasizes fast, reliable, and scalable connections, along with reliability and availability.

3. Integration:

   • Users: Expect software to integrate with existing data sources, support legacy on-premises software, and work with partner offerings.

   • Business: Values CSP-supported and managed integration without increased operational overhead.

4. Budget:

   • Users: Seek reporting flexibility, chargebacks, and showbacks, along with opportunities for migrating to managed services.

   • Business: Prefers subscription-based models for predictable revenue, scalable costs without increased operational overhead, and clear support cost predictions.

5. Compliance:

   • Users: Require CSP certification and compliance attestations, compatibility with common frameworks (NIST, ISO, SOC 1, SOC 2), and the ability to meet customer controls.

   • Business: Shares the same compliance requirements, aiming for alignment with user expectations.

6. SLA (Service Level Agreement):

   • Users: Look for adherence to uptime metrics and timely responses to outages or maintenance.

   • Business: Requires legally enforceable SLA documentation, compatibility with business application SLAs, and proactive monitoring for frequent outages.

7. User and Business Needs:

   • Users: Expect usability, familiarity, and security that doesn't hinder productivity.

   • Business: Prioritizes security, cost, and reliability, considering the overall impact on operations.

8. Security:

   • Users: Want security that doesn't impede work and an easy-to-use interface despite security measures.

   • Business: Seeks flexible security features, comprehensive data and network protection options, and a balance between security and usability.

9. Network:

   • Users: Require high-performance and reliable networks.

   • Business: Adds an emphasis on encryption options, the ability to set up hybrid cloud environments, reliability, and redundancy to mitigate localized outages.

By analyzing and documenting these elements comprehensively, organizations can align their cloud adoption strategies with the needs and expectations of both users and the business, ensuring a successful transition to the cloud while optimizing resources and outcomes.

Environment Types in Solution Designs

Solution design and operations involve more than just architectural considerations; they also encompass the management and promotion of applications through various environments. Here, we'll explore different types of environments that play a crucial role in the software development lifecycle:

1. Development Environment:

   • Purpose: Initial environment for coding and experimentation.

   • Size: Usually downsized to reduce costs.

   • Deployment: Developers can deploy code directly.

   • Testing: Sandbox for testing and experiments.

   • Reproducibility: Can be redeployed from scratch if needed.

2. Quality Assurance (QA) Environment:

   • Purpose: Testing environment for functional, regression, and security testing.

   • Size: Typically smaller than production.

   • Automation: Supports continuous integration and deployment.

   • Security: May include security testing and audits.

3. Staging Environment:

   • Purpose: Pre-production environment identical to production.

   • Size: Should be the same size as production.

   • Dependencies: Mimics production's upstream and downstream dependencies.

   • Testing: Used for further QA testing if separate from QA environment.

4. Production Environment:

   • Purpose: Full-sized infrastructure for end-user access.

   • Size: Must handle production-level loads.

   • Change Management: Follows a formal change management process.

   • Automation: Managed using automation tools, no manual changes.

5. Blue-Green Deployment:

   • Purpose: Deployment model for minimizing downtime.

   • Environments: Two identical environments (blue and green).

   • Deployment: New code is deployed to one environment while the other serves production.

   • Scaling: The environment not in use can be downsized to reduce costs.

6. Canary Deployments:

   • Purpose: Gradual deployment to a small subset of production.

   • Deployment: Updates deployed to a small percentage of resources.

   • Monitoring: Changes are monitored for issues or anomalies.

   • Rollback: Easy rollback if problems are detected.

   • Full Deployment: Proceeds to deploy to the rest of the environment upon verification.

7. Disaster Recovery Environment:

   • Purpose: Backup production environment for disaster recovery and testing.

   • Size: Scaled according to disaster recovery requirements.

   • Usage: Activated during emergencies, catastrophic events, or for DR testing.

These various environments play distinct roles in the software development lifecycle, from initial development and testing to staging, production, and disaster recovery. Properly managing and transitioning applications through these environments helps ensure quality, reliability, and efficiency in the software deployment process.

Security Testing and Other Types of Testing in Solution Design

In solution design and development, it's crucial to perform various types of testing to ensure that new code can be safely promoted to production. Two essential types of security testing are vulnerability testing and penetration testing, which help identify and address security issues:

1. Vulnerability Testing:

   • Purpose: Identifying known vulnerabilities in different contexts.

   • Contexts:

     • Network-level vulnerabilities (network security).

     • Operating system vulnerabilities (OS security).

     • Service-level vulnerabilities (including third-party dependencies).

     • Application-level vulnerabilities (code developed internally).

   • Scope: Tests are performed against a predefined list of vulnerabilities.

2. Penetration Testing:

   • Purpose: Discovering potential security issues, especially those unknown.

   • Usage: Often used to meet compliance objectives and during security audits.

   • Scope: Casts a wider net to identify weaknesses, including those not covered by vulnerability testing.

   • Result Integration: Vulnerabilities discovered during penetration testing can be included in subsequent vulnerability testing.

These security testing practices help organizations identify and mitigate security risks in their software applications.

Additionally, various other types of testing are essential during the development process:

Use Tests:

1. Performance Testing:

   • Purpose: Validates that the application remains functional as the load increases.

   • Focus: Ensures the application scales efficiently and meets performance requirements.

2. Regression Testing:

   • Purpose: Ensures that new changes or updates do not break existing functionality.

   • Focus: Verifies that changes don't negatively impact the application's stability or features.

3. Functional Testing:

   • Purpose: Validates that the application's functionality works according to specifications.

   • Focus: Tests both new and existing functionality to ensure they meet requirements.

4. Usability Testing:

   • Purpose: Evaluates the user experience to ensure it aligns with usability requirements.

   • Focus: Examines user interactions, user interface design, and overall user satisfaction.

These testing types play crucial roles in ensuring the reliability, performance, security, and user experience of software applications. Incorporating these tests into a continuous integration and continuous deployment (CI/CD) workflow helps maintain and improve the quality of the application throughout its development lifecycle.