diff --git a/.github/workflows/release-octavia-ovn.yml b/.github/workflows/release-octavia-ovn.yml
index d88b22f6..40cc902a 100644
--- a/.github/workflows/release-octavia-ovn.yml
+++ b/.github/workflows/release-octavia-ovn.yml
@@ -15,10 +15,12 @@ on:
pluginTag:
description: 'Set plugin version'
required: true
- default: '5.0.0'
+ default: '7.0.0'
type: choice
options:
- '5.0.0'
+ - '6.0.0'
+ - '7.0.0'
# Defines two custom environment variables for the workflow. These are used for the Container registry domain, and a name for the Docker image that this workflow builds.
env:
diff --git a/docs/accelerated-computing-infrastructure.md b/docs/accelerated-computing-infrastructure.md
new file mode 100644
index 00000000..b9ed45d9
--- /dev/null
+++ b/docs/accelerated-computing-infrastructure.md
@@ -0,0 +1,282 @@
+# How does Rackspace implement Accelerated Computing?
+
+{ align=left : style="max-width:175px" }
+
+Rackspace integrates high-performance networking, computing, and security solutions to meet the evolving needs of modern cloud environments. By leveraging advanced switches, scalable servers, and next-generation security, we enable accelerated computing with high availability, low-latency connectivity, and optimal performance across global infrastructures. These technologies work seamlessly together to address the unique challenges of today's cloud environments.
+
+## High-Speed Network Switches
+
+The high-performance, fixed-port switches are designed to meet the demands of modern data center environments, enterprise networks, and high-traffic workloads. These switches offer advanced features that make them ideal for cloud environments, multi-tenant data centers, and complex hybrid cloud architectures. They support high-speed, low-latency connectivity, programmability, and scalability, ensuring efficient traffic handling, seamless integration into Software-Defined Networking (SDN) environments, and robust security measures. Key features include:
+
+ 1. **High-Speed Connectivity**: Offers flexible port configurations (1G to 100G Ethernet) for high-density access and uplink,
+ supporting rapid data transmission across diverse networks.
+
+ 2. **Low Latency & High Performance**: With up to 3.6 Tbps throughput, these switches provide low-latency operation, ideal for
+ high-frequency trading, real-time analytics, media streaming, and large-scale storage.
+
+ 3. **Layer 2 & Layer 3 Features**: Includes essential network functions like VLANs, VXLAN, OSPF, BGP, and multicast for
+ efficient routing, network segmentation, and virtualized environments in dynamic data centers.
+
+ 4. **Programmability & Automation**: Native support for automation tools and APIs integrates seamlessly with SDN frameworks like
+ Cisco ACI, enabling simplified network management, real-time telemetry, and automated operations for large-scale networks.
+
+ 5. **Security & Policy Management**: Equipped with robust security features such as MACsec encryption, role-based access control
+ (RBAC), DAI, and IP ACLs, these switches ensure secure, policy-driven management in multi-tenant and regulated environments.
+
+ 6. **Scalability & High Availability**: Built for scalability, these switches feature modular designs with redundant power
+ supplies, hot-swappable components, and flexible cooling options, ensuring high availability and reliability in large
+ deployments.
+
+ 7. **Quality of Service (QoS)**: Advanced QoS capabilities, including priority flow control and dynamic buffer allocation,
+ ensure optimal performance for latency-sensitive applications such as voice and video communication.
+
+ 8. **Energy Efficiency**: Designed with energy-efficient components and Cisco EnergyWise support, these switches help reduce
+ operational costs while minimizing environmental impact, critical for large data centers and sustainability initiatives.
+
+ 9. **Intelligent Traffic Distribution**: Intelligent traffic management and load balancing optimize network resource
+ utilization, ensuring high performance and availability in cloud, storage, and compute-intensive environments.
+
+ 10. **IPv6 Support**: Full IPv6 compatibility ensures that these switches can handle the growing number of IP addresses
+ necessary for modern, next-generation networks.
+
+**Key Use Cases**:
+ 1. **High-Density Data Center Access**: For data centers needing flexible port speeds (1G to 100G) to connect virtualized
+ servers, storage, and applications.
+
+ 2. **Spine-Leaf Architectures**: Ideal for scalable data centers that can expand by adding leaf switches as workloads grow.
+
+ 3. **SDN and Cisco ACI Deployments**: Suited for enterprises or data centers seeking simplified, policy-driven network
+ management and automation.
+
+ 4. **Edge Computing and IoT with PoE**: Best for IoT or industrial applications needing both power and network connectivity at
+ the edge via Power over Ethernet.
+
+ 5. **Secure Segmentation and Multitenancy**: Perfect for cloud and hosting providers requiring network segmentation for
+ multi-department or multi-tenant environments.
+
+ 6. **Virtualized and Hybrid Cloud**: For enterprises using hybrid cloud models or heavy virtualization, ensuring scalability and
+ performance for on-premises and cloud workloads.
+
+ 7. **Latency-Sensitive Applications**: Designed for industries like finance, healthcare, and research requiring low-latency,
+ high-throughput for real-time data processing and simulations.
+
+ 8. **Automated and Centralized Management**: Ideal for enterprises with complex network infrastructures aiming to reduce manual
+ tasks through automation and centralized management.
+
+ 9. **Enhanced Security**: Suitable for industries with stringent security requirements (e.g., finance, healthcare) to protect
+ sensitive data and ensure compliance.
+
+ 10. **Load Balancing and High Availability**: Perfect for e-commerce, web hosting, and content delivery networks that need
+ efficient traffic management and high availability for critical applications.
+
+## High-Performance Computing Servers
+
+These high-performance computing (HPC) servers are designed to handle the most demanding workloads, including AI/ML, big data analytics, high-performance computing, and data-intensive applications. Built for scalability, flexibility, and robust processing power, they are ideal for environments requiring large-scale parallel processing, GPU acceleration, substantial memory, and extensive storage capacity. Key features include:
+
+ 1. **High-Core Processors**:
+ * AMD EPYC processors with up to 128 cores or Intel Xeon processors with up to 44 cores, enabling
+ parallel processing for AI model training, simulations, and large-scale data analytics.
+ * **Dual-Socket Support**: Intel Xeon E5-2600 v3/v4 processors support up to 44 cores, making them ideal for multi-threaded
+ workloads.
+
+ 2. **Massive Memory Capacity**:
+ * Systems can support up to 12TB of DDR5 memory (in some models) or up to 3TB of DDR4 memory (24 DIMM slots), optimized for
+ memory-intensive workloads like scientific computing, AI/ML, and big data analytics.
+ * **High Memory Capacity**: Specifically, the HPE ProLiant DL380 Gen9 offers up to 3TB of DDR4 RAM, ideal for virtualized
+ environments and memory-heavy applications.
+
+ 3. **Scalability and Performance**:
+ * The servers offer high scalability for both compute and memory, ensuring long-term flexibility for expanding workloads in
+ AI, HPC, and data analytics.
+ * **Enhanced Compute Power**: Intel Xeon and AMD EPYC processors provide highly efficient computing power for modern
+ enterprise and research applications.
+
+ 4. **Flexible, High-Speed Storage**:
+ * The servers support up to 100 drives, including NVMe, SAS, and SATA SSDs, allowing for scalable storage options and
+ high-capacity configurations.
+ * **HPE Smart Array Controllers**: Advanced storage management, data protection, and RAID functionality are included in some
+ configurations to improve reliability and fault tolerance.
+ * **Storage Flexibility**: Configurations can combine high-speed NVMe SSDs for performance and SATA HDDs for capacity,
+ allowing for optimal storage tiers.
+
+ 5. **GPU and Accelerator Support**:
+ * These servers can support up to 6 GPUs (including NVIDIA A100 and H100), accelerating deep learning, AI/ML model training,
+ and high-performance computing simulations.
+ * GPU support for AI/ML and scientific workloads ensures high parallel processing power, particularly useful for deep
+ learning and real-time analytics.
+ * **PCIe Gen 4.0 & Gen 5.0 Expansion**: Multiple PCIe slots for GPUs, FPGAs, and accelerators, ensuring high bandwidth and
+ minimal latency.
+
+ 6. **Optimized Networking**:
+ * Multiple 10GbE and 25GbE networking options provide scalable and high-performance connectivity for distributed computing,
+ data-heavy tasks, and real-time data processing.
+ * **Networking Expansion**: Embedded 4 x 1GbE ports and support for FlexibleLOM allow for scalable networking and
+ high-bandwidth connections, important for large-scale data applications.
+
+ 7. **High Availability and Redundancy**:
+ * **Redundant Power Supplies**: Hot-swappable power supplies ensure uninterrupted operations, even during maintenance.
+ * **Hot-Plug Fans and Drives**: Maintain and replace hardware without downtime, increasing system availability.
+ * **RAID Support**: Multiple RAID configurations ensure data redundancy and fault tolerance, crucial for high-availability
+ environments.
+
+ 8. **Energy Efficiency**:
+ * The systems feature advanced multi-vector cooling and high-efficiency power supplies, such as 80 PLUS Platinum and
+ Titanium-rated units, minimizing energy usage.
+ * **Dynamic Power Capping**: These servers can intelligently manage power consumption, optimizing energy usage without
+ compromising performance.
+
+ 9. **Security Features**:
+ * Secure Boot and TPM (Trusted Platform Module) support ensure physical and firmware-level security to protect sensitive data
+ from boot through runtime.
+ * **Hardware Root of Trust and System Lockdown**: Provides system integrity and enhanced protection against attacks.
+ * **Lockable Drive Bays**: Additional physical security features prevent unauthorized tampering.
+ * **Remote Management**: Tools like HPE iLO 4 and iDRAC9 allow for easy monitoring, management, and firmware updates
+ remotely.
+
+ 10. **Advanced Management**:
+ * iLO 4 (HPE) or iDRAC9 (other models) allows remote server management and monitoring.
+ * Intelligent Provisioning and Active Health System: Simplifies setup and deployment, while providing proactive server
+ health monitoring.
+ * OpenManage (Dell) streamlines management for integrating with existing IT systems.
+
+ 11. **Scalability and Customization**:
+ * These servers offer flexible configurations to meet the needs of various applications from cloud computing to scientific
+ research.
+ * **Modular Design**: Tool-free design allows for easy upgrades and future-proofing.
+ * **Expansion Slots**: Multiple PCIe Gen 3.0/4.0/5.0 slots for additional NICs, HBAs, GPUs, and other expansion cards.
+
+ 12. **OS and Hypervisor Support**:
+ * These systems are compatible with a wide range of operating systems and hypervisors, including Windows Server, Red Hat,
+ SUSE, Ubuntu, VMware ESXi, and others, making them versatile for various workloads and environments.
+
+**Key Use Cases**:
+ 1. **AI/ML Model Training and Inference**: For deep learning, real-time AI inference, and AI model training.
+
+ 2. **High-Performance Computing (HPC)**: For scientific research, simulations, and computational-intensive tasks.
+
+ 3. **Big Data Analytics**: For large-scale data processing and analytics, including real-time analytics and big data workloads.
+
+ 4. **Media Streaming and Content Delivery Networks**: For applications requiring massive storage and high throughput.
+
+ 5. **Edge Computing and Low-Latency Applications**: For decentralized data processing, such as edge AI and real-time
+ decision-making.
+
+ 6. **Cloud Infrastructure and Virtualization**: For cloud providers, virtualization environments, and enterprise data centers.
+
+ 7. **Database Workloads and OLTP**: For handling high-throughput transactional workloads in enterprise environments.
+
+ 8. **Scientific Applications and Research**: For complex scientific simulations, research, and discovery that require high-core
+ processing and large memory configurations.
+
+ 9. **Backup and Disaster Recovery**: For secure and scalable backup solutions with high data integrity.
+
+ 10. **Software-Defined Storage (SDS) and Hyper-Converged Infrastructure (HCI)**: For next-gen storage solutions that require
+ both high performance and flexibility.
+
+## Application Delivery Controllers (ADC)
+
+The high-performance application delivery and security controller are designed to optimize application traffic, enhance security, and improve user experience for enterprises, service providers, and data centers. Key features include:
+
+ 1. **High Performance**: Offers up to 80 Gbps of L4 throughput and 8 Gbps of SSL/TLS encryption throughput, ideal for handling
+ high volumes of traffic and complex security requirements.
+
+ 2. **SSL/TLS Offloading**: Dedicated hardware for SSL offloading improves application response times by freeing up server
+ resources and supporting modern encryption standards like TLS 1.3.
+
+ 3. **Comprehensive Security**: Equipped with Web Application Firewall (WAF), bot protection, DDoS mitigation, IP intelligence,
+ and threat services to secure web applications from common vulnerabilities and attacks.
+
+ 4. **Traffic Management**: Supports advanced load balancing (GSLB, LTM) and failover capabilities, with customizable traffic
+ management via iRules scripting for granular control over routing and traffic behavior.
+
+ 5. **Orchestration and Automation**: iApps and iControl REST APIs simplify deployment and integrate with DevOps tools for
+ streamlined automation, reducing configuration errors.
+
+ 6. **Access Control with APM**: Integrates with Access Policy Manager (APM) for secure access, Single Sign-On (SSO),
+ Multi-Factor Authentication (MFA), and Zero Trust capabilities.
+
+ 7. **Application Acceleration**: Features like TCP optimization, caching, and compression reduce latency and bandwidth
+ consumption, enhancing application performance.
+
+ 8. **Programmability**: Customizable with iRules for tailored traffic management and iCall for automating tasks based on events.
+
+ 9. **High Availability**: Supports active-active and active-passive HA modes for continuous uptime, with failover and
+ synchronization for improved reliability.
+
+ 10. **Scalability**: Modular licensing allows feature expansion without hardware replacement, providing cost savings and
+ investment protection.
+
+ 11. **Virtualization Support**: Compatible with F5 Virtual Editions (VEs), enabling consistent policies across on-premises and
+ cloud environments for hybrid or multi-cloud deployments.
+
+ 12. **Network Integration**: Supports IPv6, IPsec, VLANs, and VPNs, with flexible network interface options (1GbE, 10GbE, 25GbE)
+ for diverse infrastructures.
+
+**Key Use Cases**:
+ 1. **Large Enterprises and Data Centers**: Perfect for organizations needing efficient traffic management, enhanced application
+ performance, and robust security.
+
+ 2. **Service Providers**: Ideal for managing high traffic volumes with advanced traffic management, scalability, and
+ comprehensive security.
+
+ 3. **E-commerce and Online Services**: Excellent for protecting e-commerce platforms with Web Application Firewall (WAF), bot
+ protection, and DDoS mitigation.
+
+ 4. **Hybrid Cloud Environments**: Best for seamless application delivery and security across on-premises and cloud
+ infrastructures in hybrid or multi-cloud setups.
+
+## Next-Generation Firewalls (NGFW)
+
+This next-generation firewall is designed for large-scale environments, including enterprise data centers and service providers. It offers robust security features that protect against advanced threats while maintaining high throughput and seamless integration into high-speed networks. Key features include:
+
+ 1. **High Performance and Throughput**: Delivers up to 72 Gbps of firewall throughput and 32 Gbps of Threat Prevention
+ throughput, ensuring efficient handling of large traffic volumes and complex security tasks.
+
+ 2. **Advanced Threat Prevention**: Integrates IPS, antivirus, anti-spyware, and sandboxing technology for real-time threat
+ detection, blocking malware, exploits, and zero-day attacks.
+
+ 3. **Application-Based Traffic Control with App-ID**: Offers granular control over applications regardless of port or protocol,
+ enhancing visibility and security across the network.
+
+ 4. **User and Content-Based Control with User-ID and Content-ID**: User-ID maps traffic to specific users for policy
+ enforcement, while Content-ID inspects data for malicious content, URL filtering, and prevents data leakage.
+
+ 5. **SSL Decryption & Inspection**: Capable of decrypting SSL/TLS traffic for deeper inspection of encrypted data, ensuring
+ protection against hidden threats within secure communications.
+
+ 6. **Integrated WildFire for Advanced Malware Analysis**: Suspicious files are sent for sandboxing to analyze behavior and
+ detect previously unknown threats.
+
+ 7. **Scalable and Modular Connectivity Options**: Supports 10GbE, 25GbE, and 40GbE interfaces for seamless integration into
+ high-speed networks, ensuring optimal performance and flexibility.
+
+ 8. **High Availability and Redundancy**: Supports high availability (HA) configurations, ensuring continuous uptime with
+ automatic failover capabilities.
+
+ 9. **Comprehensive Security Subscriptions**: Includes Threat Prevention, URL Filtering, DNS Security, GlobalProtect, and SD-WAN
+ for advanced protection and secure remote access.
+
+ 10. **Automation & Centralized Management**: Integrated with centralized firewall management platforms and supports API
+ integration for automation, enhancing efficiency in security operations and DevOps workflows.
+
+ 11. **Machine Learning for Autonomous Security**: Uses machine learning to enhance threat detection, adapt security protocols,
+ and proactively defend against emerging threats.
+
+ 12. **Zero Trust Network Security Capabilities**: Enforces least-privilege access and continuous verification of identity,
+ aligning with Zero Trust security principles.
+
+ 13. **Energy Efficiency and Form Factor**: Designed to deliver high performance while maintaining energy efficiency, reducing
+ operational costs and minimizing environmental impact.
+
+**Key Use Cases**:
+ 1. **Enterprise Data Centers**: Ideal for large data centers requiring high throughput, threat protection, and efficient traffic
+ management.
+
+ 2. **Service Providers and Large Enterprises**: Perfect for securing and managing complex, high-traffic networks with
+ scalability and performance.
+
+ 3. **Cloud and Hybrid Environments**: Suited for hybrid and multi-cloud setups, ensuring consistent security across on-premises
+ and cloud infrastructures.
+
+ 4. **High-Risk Sectors**: Beneficial for industries like finance, healthcare, and government, requiring advanced security
+ features such as threat detection and SSL inspection.
diff --git a/docs/accelerated-computing-overview.md b/docs/accelerated-computing-overview.md
new file mode 100644
index 00000000..e1e46274
--- /dev/null
+++ b/docs/accelerated-computing-overview.md
@@ -0,0 +1,183 @@
+# What is Accelerated Computing?
+
+{ align=left : style="max-width:175px" }
+
+## Overview
+
+Accelerated computing uses specialized hardware called accelerators, such as the following:
+
+* Graphics Processing Units ([GPUs](https://en.wikipedia.org/wiki/Graphics_processing_unit))
+* Neural Processing Units ([NPUs](https://support.microsoft.com/en-us/windows/all-about-neural-processing-units-npus-e77a5637-7705-4915-96c8-0c6a975f9db4))
+* Smart Network Interface Cards([Smart NICs](https://codilime.com/blog/what-are-smartnics-the-different-types-and-features/))
+* Tensor Processing Units ([TPUs](https://en.wikipedia.org/wiki/Tensor_Processing_Unit))
+* Field Programmable Gate Arrays ([FPGAs](https://en.wikipedia.org/wiki/Field-programmable_gate_array))
+
+These accelerators are used to perform computations faster than traditional CPUs alone because they are designed to handle highly parallel, complex, or data-intensive tasks more efficiently. This makes them ideal for applications like machine learning, scientific simulations, graphics rendering, and big data processing.
+
+## Benefits
+
+* **Parallel Processing**: Accelerators can perform multiple calculations simultaneously. For example, GPUs have thousands of
+ cores, allowing them to execute many tasks at once, which is ideal for matrix calculations common in machine learning.
+
+* **Optimized Architectures**: Each type of accelerator is tailored to specific tasks. GPUs are optimized for floating-point
+ operations, making them well-suited for image processing and deep learning. TPUs are specifically designed by Google for
+ neural network computations, while FPGAs can be customized to accelerate a variety of applications.
+
+* **Energy and Cost Efficiency**: Accelerators can reduce energy usage and costs by performing computations faster, which is
+ particularly important in data centers and high-performance computing environments.
+
+* **Enhanced Performance in AI and Data-Intensive Workloads**: Accelerated computing has become foundational for AI and machine
+ learning, where training models on large datasets can take days or weeks without specialized hardware.
+
+### GPUs
+
+GPUs are widely used as accelerators for a variety of tasks, especially those involving high-performance computing, artificial intelligence, and deep learning. Originally designed for rendering graphics in video games and multimedia applications, GPUs have evolved into versatile accelerators due to their highly parallel architecture. Here’s how GPUs act as effective accelerators:
+
+ 1. **Massively Parallel Architecture**: With thousands of cores, GPUs excel in parallel tasks, such as training neural networks,
+ which require numerous simultaneous operations like matrix multiplications.
+
+ 2. **High Throughput for Large Data**: GPUs deliver superior throughput for processing large datasets, making them ideal for
+ applications in image/video processing, data analytics, and simulations (e.g., climate modeling).
+
+ 3. **Efficient for Deep Learning and AI**: GPUs handle deep learning tasks, especially matrix calculations, much faster than
+ CPUs. Popular frameworks like TensorFlow and PyTorch offer GPU support for both training and inference.
+
+ 4. **Real-Time Processing**: GPUs enable low-latency processing for time-sensitive applications like autonomous driving and
+ real-time video analysis, where rapid decision-making is crucial.
+
+ 5. **Accelerating Scientific Computing**: In scientific research, GPUs speed up computations for complex simulations in fields
+ like molecular dynamics, astrophysics, and genomics.
+
+ 6. **AI Model Inference and Deployment**: Post-training, GPUs accelerate AI model inference for real-time predictions across
+ industries like healthcare, finance, and security.
+
+ 7. **Software Ecosystem**: NVIDIA GPUs benefit from a robust ecosystem (e.g., CUDA, cuDNN) that supports AI, machine learning,
+ and scientific computing, enabling developers to fully harness GPU power.
+
+In summary, GPUs function as accelerators by leveraging their parallel processing capabilities and high computational power to speed up a range of data-intensive tasks. They are versatile tools, widely used across industries for tasks that require rapid, efficient processing of large volumes of data.
+
+### NPUs
+
+NPUs are specialized accelerators designed specifically to handle AI and deep learning tasks, especially neural network computations. They’re highly optimized for the types of mathematical operations used in AI, such as matrix multiplications and convolutions, making them particularly effective for tasks like image recognition, natural language processing, and other machine learning applications. Here’s how NPUs function as powerful accelerators:
+
+ 1. **Optimized for Neural Network Operations**: NPUs excel at tensor operations and matrix multiplications, which are central to
+ neural networks, providing greater efficiency than CPUs or GPUs.
+
+ 2. **Parallelized Processing**: With multiple cores optimized for parallel tasks, NPUs handle large datasets and complex
+ computations simultaneously, making them ideal for deep learning applications.
+
+ 3. **Low Power Consumption**: NPUs are energy-efficient, making them well-suited for mobile and edge devices, such as
+ smartphones and IoT sensors, where power is limited.
+
+ 4. **Faster Inference for Real-Time Apps**: NPUs accelerate AI model inference, enabling real-time applications like facial
+ recognition, voice assistants, and autonomous driving.
+
+ 5. **Offloading from CPUs and GPUs**: By offloading neural network tasks, NPUs reduce the burden on CPUs and GPUs, improving
+ overall system performance, especially in multi-process environments.
+
+ 6. **Wide Device Integration**: NPUs are increasingly integrated into various devices, from mobile phones (e.g., Apple’s Neural
+ Engine) to data centers (e.g., Google’s TPU), enabling AI in low-power and resource-constrained environments.
+
+In summary, NPUs are specialized accelerators that enhance AI processing efficiency, enabling faster and more accessible AI applications across a wide range of devices.
+
+### Smart NICs
+
+Smart NICs act as accelerators by offloading and accelerating network-related tasks, helping to improve the performance of servers in data centers, cloud environments, and high-performance computing applications. Unlike traditional NICs that only handle basic data transfer, Smart NICs have onboard processing capabilities, often including dedicated CPUs, FPGAs, or even GPUs, which enable them to process data directly on the card. Here’s how they function as powerful accelerators:
+
+ 1. **Offloading Network Tasks**: Smart NICs offload tasks like packet processing, encryption, load balancing, and firewall
+ management, freeing the CPU to focus on application-specific computations.
+
+ 2. **Accelerating Data Processing**: With built-in processing capabilities, Smart NICs handle tasks such as data encryption,
+ compression, and analysis, crucial for data-intensive environments like data centers.
+
+ 3. **Programmable Logic (FPGA-Based NICs)**: Many Smart NICs use FPGAs, which can be reprogrammed for specific networking
+ functions, offering flexibility and adaptability to evolving network protocols.
+
+ 4. **Enhanced Performance with RDMA**: By supporting Remote Direct Memory Access (RDMA), Smart NICs enable direct
+ memory-to-memory data transfers, reducing latency and improving throughput for latency-sensitive applications.
+
+ 5. **Security Acceleration**: Smart NICs offload security functions like encryption, firewall management, and intrusion
+ detection, processing them in real-time to enhance network security without compromising performance.
+
+ 6. **Data Center and Cloud Optimization**: In virtualized environments, Smart NICs offload and accelerate virtual network
+ functions (VNFs), reducing CPU load and improving resource utilization for cloud and data center applications.
+
+ 7. **Accelerating Storage Networking**: Smart NICs accelerate storage networking tasks, such as NVMe-oF, for faster remote
+ storage access and improved performance in data-heavy applications.
+
+ 8. **Edge Computing and IoT**: Smart NICs process data locally in edge devices, performing tasks like filtering, aggregation,
+ and compression to reduce latency and optimize data transfer.
+
+In summary, Smart NICs enhance system performance by offloading network and data processing tasks, enabling higher efficiency, lower latency, and improved scalability in data-intensive environments.
+
+### TPUs
+
+TPUs are specialized accelerators developed by Google to optimize and accelerate machine learning workloads, particularly for deep learning and neural network computations. Unlike general-purpose processors, TPUs are custom-designed to efficiently handle the massive amounts of matrix operations and tensor computations commonly required by machine learning algorithms, especially deep neural networks. Here’s how TPUs function as powerful accelerators:
+
+ 1. **Matrix Multiplication Optimization**: TPUs accelerate matrix multiplications, essential for deep learning models, making
+ them much faster than CPUs or GPUs.
+
+ 2. **Parallel Processing**: With a large number of cores, TPUs enable high levels of parallelism, allowing them to process
+ complex neural networks quickly.
+
+ 3. **Energy Efficiency**: Designed to consume less power than CPUs and GPUs, TPUs reduce energy costs, making them ideal for
+ large-scale data centers.
+
+ 4. **High-Speed Memory Access**: TPUs use high-bandwidth memory (HBM) to quickly feed data to processing units, speeding up
+ training and inference.
+
+ 5. **Performance for Training and Inference**: TPUs deliver low-latency performance for both model training and real-time
+ inference, critical for AI applications.
+
+ 6. **TensorFlow Integration**: TPUs are tightly integrated with TensorFlow, offering optimized tools and functions for seamless
+ use within the framework.
+
+ 7. **Scalability with TPU Pods**: TPU Pods in Google Cloud allow scaling processing power for massive datasets and
+ enterprise-level machine learning models.
+
+ 8. **Optimized Data Types**: Using BFloat16, TPUs enable faster computation while maintaining model accuracy, reducing memory
+ and processing demands.
+
+ 9. **Edge TPUs for IoT**: Edge TPUs provide low-power machine learning capabilities for edge and IoT devices, supporting
+ real-time AI tasks like image recognition.
+
+In summary, TPUs are custom-designed accelerators that significantly enhance the speed, efficiency, and scalability of machine learning tasks, particularly in large-scale and real-time AI applications.
+
+### FPGAs
+
+FPGAs are highly customizable accelerators that offer unique advantages for specialized computing tasks, especially in data-intensive fields such as machine learning, financial trading, telecommunications, and scientific research. FPGAs are programmable hardware that can be configured to perform specific functions with high efficiency, making them very versatile. Here’s how FPGAs function as powerful accelerators:
+
+ 1. **Customizable Hardware**: Unlike fixed accelerators, FPGAs can be reprogrammed for specific tasks, allowing optimization for
+ workloads like data encryption, compression, or AI inference.
+
+ 2. **High Parallelism**: FPGAs feature thousands of independent logic blocks, enabling parallel data processing, ideal for
+ applications like real-time signal processing and genomics.
+
+ 3. **Low Latency & Predictability**: FPGAs provide extremely low latency and deterministic performance, crucial for real-time
+ applications like high-frequency trading.
+
+ 4. **Energy Efficiency**: FPGAs are energy-efficient by utilizing only the necessary hardware resources for a task, making them
+ suitable for energy-sensitive environments like edge devices.
+
+ 5. **Flexibility**: Reprogrammability allows FPGAs to adapt to evolving standards and algorithms, such as new networking
+ protocols or machine learning models.
+
+ 6. **Machine Learning Inference**: FPGAs excel at AI inference tasks, providing low-latency, efficient processing for
+ applications like object detection and speech recognition.
+
+ 7. **Custom Data Types**: FPGAs can handle specialized data formats to optimize memory and processing speed, ideal for
+ applications like AI with reduced-precision data.
+
+ 8. **Hardware-Level Security**: FPGAs can implement custom cryptographic algorithms for secure communications or sensitive data
+ handling.
+
+ 9. **Real-Time Signal Processing**: FPGAs are used in industries like telecommunications and aerospace for high-speed, real-time
+ signal processing in applications like radar and 5G.
+
+ 10. **Edge Computing & IoT**: FPGAs are deployed in edge devices to perform local computations, such as sensor data processing
+ or on-device AI inference, reducing reliance on the cloud.
+
+ 11. **Integration with Other Accelerators**: FPGAs can complement CPUs and GPUs in heterogeneous computing environments,
+ handling tasks like preprocessing or data compression while other accelerators manage complex workloads.
+
+In summary, FPGAs are versatile accelerators, offering customizable, energy-efficient hardware for high-speed, low-latency processing across various specialized applications, from signal processing to machine learning.
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diff --git a/docs/openstack-quota.md b/docs/openstack-quota.md
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@@ -0,0 +1,29 @@
+# OpenStack Quotas
+
+To read more about Openstack quotas please visit the [upstream docs](https://docs.openstack.org/nova/rocky/admin/quotas.html).
+
+#### Viewing Your Quota
+
+You can view your quotas via Skyline or using the OpenStack CLI client. The Skyline home page is the easiest way to view your quota limits and utilization in one place.
+
+
+You can also view quota information using the OpenStack CLI, however, you'll need to use a couple of commands.
+Use the quota option to list all of your quota limits.
+
+```shell
+openstack quota show
+```
+
+
+
+The limits option returns quota limits along with utilization information for most services.
+
+```shell
+openstack limits show --absolute
+```
+
+
+
+#### Increasing Your Quota
+
+If you need to request a quota increase, please contact your Account Manager or navigate to the [MyCloud](https://mycloud.rackspace.com) portal, create a ticket, and select Account as the category.
diff --git a/docs/security-introduction.md b/docs/security-introduction.md
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--- /dev/null
+++ b/docs/security-introduction.md
@@ -0,0 +1,65 @@
+# Genestack Secure Development Practices
+
+Genestack is a complete operation and deployment ecosystem for OpenStack services that heavily utilizes cloud native application like
+Kubernetes. While developing, publishing, deploying and running OpenStack services based on Genestack we aim to ensure that our engineering teams follow
+security best practices not just for OpenStack components but also for k8s and other cloud native applications used within the Genestack ecosystem.
+
+This security primer aims to outline layered security practices for Genestack, providing actionable security recommendations at every level to mitigate
+risks by securing infrastructure, platform, applications and data at each layer of the development process.
+This primer emphasizes secure development practices that complement Genestack's architecture and operational workflows.
+
+
+## Layered Security Approach
+
+Layered security ensures comprehensive protection against evolving threats by addressing risks at multiple levels. The approach applies security measures
+to both physical infrastructure and also provides security focus to the development of the application itself. The aim is to minimize a single point
+of failure compromising the entire system. This concept aligns with the cloud native environments by catagorizing security measures across the lifecycle and stack of the cloud native technologies.
+
+The development team follow a set of practices for Genestack: Rackspace OpenStack Software Development Life Cycle (Rax-O-SDLC). The SDLC practice aims to produce
+software that meets or exceeds customer expectation and reach completion within a time and cost estimate. SDLC process is divided into six distinct phases: `Scope`, `Implement`,
+`Document`, `Test`, `Deployment` and `Maintain`.
+
+For each of the above stages fall within the security guidelines from CNCF that models security into four distince phases.
+
+Security is then injected at each of these phases:
+
+1. **Develop:** Applying security principles during application development
+
+2. **Distribute:** Security practices to distribute code and artifacts
+
+3. **Deploy:** How to ensure security during application deployment
+
+4. **Runtime:** Best practices to secure infrastructure and interrelated components
+
+
+Lets look at it from OpenStack side of things. We want to see security across:
+
+1. **Infrastructure:** Both physical and virtual resources
+
+2. **Platform:** Services that support workloads
+
+3. **Applications:** Containerized workloads and instances that run the services
+
+4. **Data:** Security of data at rest and in transit
+
+
+CNCF defines its security principles as:
+
+1. Make security a design requirement
+
+2. Applying secure configuration has the best user experience
+
+3. Selecting insecure configuration is a conscious decision
+
+4. Transition from insecure to secure state is possible
+
+5. Secure defaults are inherited
+
+6. Exception lists have first class support
+
+7. Secure defaults protect against pervasive vulnerability exploits
+
+8. Security limitations of a system are explainable
+
+
+These guidelines can be adopted to have a secure foundation for Genestack based cloud.
diff --git a/docs/security-lifecycle.md b/docs/security-lifecycle.md
new file mode 100644
index 00000000..f3c03d03
--- /dev/null
+++ b/docs/security-lifecycle.md
@@ -0,0 +1,308 @@
+# Layered Security
+
+Layered security in cloud-native environments involves applying protection measures across all lifecycle stages: development, distribution, deployment, and runtime. Each stage incorporates specific controls to address security risks and maintain a robust defense. For example, during development, practices like secure coding and dependency scanning are emphasized. The distribution stage focuses on verifying artifacts, such as container images, with cryptographic signatures. Deployment involves infrastructure hardening and policy enforcement, ensuring secure configuration. Finally, runtime security includes monitoring and detecting anomalies, enforcing least privilege, and safeguarding active workloads to mitigate threats dynamically.
+
+Lets look at each stage in detail.
+
+## Develop
+
+The Develop phase in cloud-native security emphasizes integrating security into the early stages of application development. It involves secure coding practices, managing dependencies by scanning for vulnerabilities, and incorporating security testing into CI/CD pipelines. Developers adopt tools like static and dynamic analysis to identify risks in source code and applications. This proactive approach helps prevent vulnerabilities from progressing further down the lifecycle, reducing risks in later stages.
+
+
+ { width="700" height="400"}
+ Ref: CNCF Cloud Native Security Develop Phase
+
+
+
+=== "Infrastructure Layer"
+
+ * **CNCF Context**
+
+ Ensure infrastructure as code (IaC) is secure and scanned for vulnerabilities using tools like Checkov or Terraform Validator.
+
+ * **Recommendations**
+
+ Have separate development, staging and production environments.
+
+ Securely configure physical and virtual resources (e.g., OpenStack nodes, Kubernetes nodes) during the development of IaC templates.
+
+ Implement CI pipelines that test and validate infrastructure configurations against security benchmarks.
+
+
+=== "Platform Layer"
+
+ * **CNCF Context**
+
+ Harden platform components like OpenStack services or Kubernetes clusters in the configuration stage.
+
+ * **Recommendations**
+
+ Use security-focused configurations for platforms supporting workloads (e.g., hardened Helm charts, secured Nova and Neutron configurations).
+
+ Audit configuration files for misconfigurations using tools like kube-score.
+
+
+=== "Applications Layer"
+
+ * **CNCF Context**
+
+ Use secure coding practices and dependency scans to mitigate risks in application code.
+
+ * **Recommendations**
+
+ Ensure failures in CI is fixed and that tests exists for failure scenarios.
+
+ Develop application with 12 factor app concept.
+
+ Review code before merging. Follow pre-production and production branching.
+
+ Containerize workloads using scanned, validated images.
+
+ Integrate static application security testing (SAST) into CI pipelines.
+
+
+=== "Data Layer"
+
+ * **CNCF Context**
+
+ Secure sensitive data early by implementing encryption strategies.
+
+ * **Recommendations**
+
+ Ensure sensitive configuration data (e.g., Secrets, keys) is managed securely using Vault or Barbican.
+
+ Use tools like Snyk to scan code for data exposure risks.
+
+
+
+## Distribute
+
+The Distribute phase in cloud-native security focuses on ensuring that all software artifacts, such as container images and binaries, are securely handled during distribution. Key practices include signing artifacts with cryptographic signatures to verify their integrity and authenticity, scanning artifacts for vulnerabilities, and employing policies to prevent the distribution of untrusted or non-compliant components. A secure artifact registry, access controls, and monitoring of repository activity are essential to maintain trust and protect the supply chain. These measures help reduce risks of tampered or malicious artifacts being deployed in production environments.
+
+
+
+ { width="700" height="400"}
+ Ref: CNCF Cloud Native Security Distribute Phase
+
+
+
+=== "Infrastructure Layer"
+
+ * **CNCF Context**
+
+ Securely distribute infrastructure artifacts such as VM images or container runtimes.
+
+ * **Recommendations**
+
+ Build secure image building pipelines.
+
+ Use image signing (e.g., Sigstore, Notary) for OpenStack or Kubernetes.
+
+ Validate VM images against OpenStack Glance hardening guidelines.
+
+
+=== "Platform Layer"
+
+ * **CNCF Context**
+
+ Ensure platform components are securely distributed and deployed.
+
+ * **Recommendations**
+
+ Apply signed configuration files and use secure channels for distributing Helm charts.
+
+ Use integrity validation tools to verify signed manifests.
+
+ Use secure registry for container images.
+
+
+=== "Applications Layer"
+
+ * **CNCF Context**
+
+ Harden containers and validate their integrity before deployment.
+
+ * **Recommendations**
+
+ Leverage tools like Harbor to enforce vulnerability scans for container images.
+
+ Ensure SBOM (Software Bill of Materials) generation for all containerized workloads.
+
+ Cryptographically sign images.
+
+ Have container manifest scanning and hardening policies enforced.
+
+ Develop security tests for applications.
+
+
+=== "Data Layer"
+
+ * **CNCF Context**
+
+ Secure data movement and prevent exposure during distribution.
+
+ * **Recommendations**
+
+ Encrypt data at rest and enforce TLS for data in transit between distribution systems.
+
+ Periodically rotate encryption keys to ensure freshness.
+
+ Use secure container image registry with RABC policy.
+
+
+
+## Deploy
+
+The Deploy phase in cloud-native security focuses on securely setting up and configuring workloads and infrastructure in production environments. This phase emphasizes using tools like Infrastructure as Code (IaC) to define secure, consistent configurations. Security controls include enforcing policies such as mandatory access controls, network segmentation, and compliance with deployment best practices. Additionally, ensuring that only trusted artifacts, verified in the "Distribute" phase, are deployed is critical. Continuous validation of deployments and automated scanning help maintain security posture and prevent misconfigurations or vulnerabilities from affecting the runtime environment.
+
+
+ { width="700" height="400"}
+ Ref: CNCF Cloud Native Security Deploy Phase
+
+
+=== "Infrastructure Layer"
+
+ * **CNCF Context**
+
+ Use hardened deployment practices for nodes, ensuring compliance with security policies.
+
+ * **Recommendations**
+
+ Deploy OpenStack nodes and Kubernetes clusters with minimal services and secure configurations.
+
+ Automate security testing for production deployments.
+
+ Do pre-deploy infrastructure checks. (Example: host state, kerel version, patch status)
+
+ Setup secure log storage.
+
+
+=== "Platform Layer"
+
+ * **CNCF Context**
+
+ Secure APIs and runtime configurations for platforms.
+
+
+ * **Recommendations:**
+
+ Enable TLS for OpenStack APIs and enforce RBAC in Kubernetes clusters.
+
+ Use tools like OpenStack Security Groups and Kubernetes NetworkPolicies to isolate workloads.
+
+ Have strong observability and metrics for the platform.
+
+ Setup log aggregration.
+
+
+=== "Applications Layer"
+
+ * **CNCF Context:**
+
+ Apply runtime policies for containerized applications.
+
+ * **Recommendations:**
+
+ Enforce PodSecurity policies for Kubernetes workloads and hypervisor-level security for OpenStack instances.
+
+ Continuously monitor applications for compliance with runtime security policies.
+
+ Have a strong Incident Management policy.
+
+ Have a strong Alert/Event Management and Automation policy.
+
+
+=== "Data Layer"
+
+ * **CNCF Context:**
+
+ Protect sensitive data as it enters the runtime environment.
+
+ * **Recommendations:**
+
+ Encrypt data in transit using SSL/TLS and secure APIs with rate-limiting and authentication.
+
+ Perform regular audits of access logs for sensitive data.
+
+ Ensure data protection before deploy. (Example: make sure database backup exist)
+
+
+
+## Runtime
+
+The Runtime phase in cloud-native security focuses on protecting active workloads and infrastructure against threats while applications are operational. Key practices include continuous monitoring for anomalous behavior, enforcing runtime policies to restrict actions beyond predefined boundaries, and using tools like intrusion detection systems (IDS) and behavioral analytics. Securing runtime environments also involves employing least-privilege access controls, managing secrets securely, and isolating workloads to contain potential breaches. These proactive measures help maintain the integrity and confidentiality of applications in dynamic cloud-native ecosystems.
+
+
+ { width="700" height="400"}
+ Ref: CNCF Cloud Native Security Runtime Phase
+
+
+
+=== "Infrastructure Layer"
+
+ * **CNCF Context**
+
+ Monitor nodes for anomalies and ensure compliance with runtime configurations.
+
+
+ * **Recommendations**
+
+ Use tools like Prometheus and Falco to detect anomalies in OpenStack and Kubernetes nodes.
+
+ Automate incident response with tools like StackStorm.
+
+
+=== "Platform Layer"
+
+ * **CNCF Context**
+
+ Continuously secure platform services during operation.
+
+
+ * **Recommendations**
+
+ Apply monitoring tools to detect unusual API or service behaviors in OpenStack and Kubernetes.
+
+ Set up alerting for deviations in usage patterns or API calls.
+
+
+=== "Applications Layer"
+
+ * **CNCF Context**
+
+ Monitor containerized workloads for malicious or unexpected behavior.
+
+
+ * **Recommendations**
+
+ Use runtime security tools like Aqua Security or Sysdig to secure containerized applications.
+
+ Enforce network policies to restrict communication between workloads.
+
+
+=== "Data Layer"
+
+ * **CNCF Context**
+
+ Secure data throughout its lifecycle in runtime.
+
+
+ * **Recommendations**
+
+ Encrypt data streams and apply access controls to sensitive information.
+
+ Use backup solutions and test recovery mechanisms to ensure data availability.
+
+
+The Runtime phase encompasses several key components that form the foundation of a secure and highly available cloud environment. These components include:
+
+- Orchestration
+- Compute
+- Storage
+- Access Control
+
+Each of these components involves complex interdependencies and is critical to the stability and security of your cloud infrastructure. Ensuring their security not only requires adherence to best practices during the Develop, Distribute, and Deploy phases but also relies heavily on the overall cloud environment's design.
+
+## Building a Secure and Resilient Cloud Environment
+
+Our objective is to provide comprehensive guidelines for designing a secure and highly available cloud. Start by reviewing the recommendations outlined in our Cloud Design Documentation to understand best practices for structuring your cloud infrastructure. With this foundation, we can establish security principles tailored to each critical component, ensuring they are robust and resilient against potential threats.
diff --git a/docs/security-stages.md b/docs/security-stages.md
new file mode 100644
index 00000000..147c2ae0
--- /dev/null
+++ b/docs/security-stages.md
@@ -0,0 +1,215 @@
+# Securing Private Cloud Infrastructure
+
+To ensure a secure and highly available cloud, the security framework must address orchestration, compute, storage, and access control in the context of a larger cloud design. This guide builds on a multi-layered, defense-in-depth approach, incorporating best practices across physical, network, platform, and application layers, aligned with a region -> multi-DC -> availability zone (AZ) design. Each component is discussed below with actionable strategies for robust protection.
+
+## Orchestration Security
+Orchestration platforms, such as OpenStack and Kubernetes, are fundamental to managing resources in a cloud environment. Securing these platforms ensures the stability and integrity of the overall cloud infrastructure. Below, we outline security considerations for both OpenStack and Kubernetes.
+
+### Securing OpenStack
+OpenStack offers a robust framework for managing cloud resources, but its complexity requires careful security practices.
+
+- Implement software-defined networking (SDN) with micro-segmentation and zero-trust principles.
+- Leverage OpenStack Neutron for VXLAN/VLAN isolation, network function virtualization (NFV), and dynamic security group management.
+- Deploy next-generation firewalls (NGFWs) and intrusion prevention systems (IPS) to monitor and secure network traffic.
+- Use stateful packet inspection and machine learning-based anomaly detection to identify threats in real time.
+
+- Secure OpenStack Keystone with multi-factor authentication (MFA) and federated identity management (SAML, OAuth, or LDAP).
+- Enforce the principle of least privilege using RBAC and automated access reviews.
+
+- Integrate logs with a Security Information and Event Management (SIEM) system for real-time analysis.
+- Use machine learning-powered threat hunting and anomaly detection to enhance monitoring capabilities.
+
+### Securing Kubernetes
+Kubernetes is widely used for container orchestration, and securing its components is essential for maintaining a resilient cloud environment.
+
+Pod Security Standards (PSS)
+
+- Adopt Kubernetes' Pod Security Standards, which define three security profiles:
+
+ - Privileged: Allows all pod configurations; use sparingly.
+ - Baseline: Enforces minimal restrictions for general-purpose workloads.
+ - Restricted: Applies the most stringent security controls, suitable for sensitive workloads.
+
+Pod Security Admission (PSA)
+
+- Enable Pod Security Admission to enforce Pod Security Standards dynamically.
+- Configure namespaces with PSA labels to define the allowed security profile for pods in that namespace (e.g., restricted or baseline).
+
+Service Account Security
+
+- Avoid default service accounts for workload pods.
+- Use Kubernetes RBAC to restrict the permissions of service accounts.
+- Rotate service account tokens regularly and implement short-lived tokens for increased security.
+
+Network Policies
+
+- Use Network Policies to define pod-to-pod communication and restrict access.
+- Allow only necessary traffic between services
+- Block external traffic to sensitive pods unless explicitly required.
+- Implement micro-segmentation within namespaces to isolate workloads.
+
+Kubernetes API Access
+
+- Restricting access to the control plane with network security groups.
+- Enabling RBAC for granular access control.
+- Securing API communication with mutual TLS and enforcing short-lived certificates.
+- Logging all API server requests for auditing purposes.
+
+
+## Compute Security
+Compute resources, including hypervisors and virtual machines (VMs), must be hardened to prevent unauthorized access and ensure isolation.
+
+### Hypervisor and Host Security
+
+- Use hardware-assisted virtualization security features.
+- Enable Secure Boot, Trusted Platform Module (TPM), and kernel hardening (ASLR, DEP).
+- Leverage SELinux/AppArmor and hypervisor-level isolation techniques.
+- Use Intel SGX or AMD SEV for confidential computing.
+
+### Virtual Machine Security
+
+- Perform image security scanning and mandatory signing.
+- Enforce runtime integrity monitoring and ephemeral disk encryption.
+- Ensure robust data-at-rest encryption via OpenStack Barbican.
+- Secure all communications with TLS and automate key management using HSMs.
+
+
+## Storage Security
+Protecting data integrity and availability across storage systems is vital for cloud resilience.
+
+- Encrypt data-at-rest and data-in-transit.
+- Implement automated key rotation and lifecycle management.
+- Use immutable backups and enable multi-region replication to protect against ransomware and data loss.
+- Establish encrypted, immutable backup systems.
+- Conduct regular RPO testing to validate recovery mechanisms.
+- Geographically distribute backups using redundant availability zones.
+
+
+## Access Control Security
+Access control ensures only authorized users and systems can interact with the cloud environment.
+
+- Implement multi-factor physical security mechanisms.
+- Biometric authentication and mantrap entry systems.
+- Maintain comprehensive access logs with timestamped and photographic records.
+- Redundant sensors for temperature, humidity, and fire.
+- UPS with automatic failover and geographically distributed backup generators.
+- Use IAM policies to manage user and system permissions.
+- Automate identity lifecycle processes and align access policies with regulatory standards.
+
+## Network and Infrastructure Security
+
+### Network Segmentation and Isolation Network Design Principles
+
+Implement software-defined networking (SDN) with
+
+- Micro-segmentation Zero-trust network architecture Granular traffic control policies.
+- Use OpenStack Neutron advanced networking features.
+- VXLAN/VLAN isolation Network function virtualization (NFV) Dynamic security group management.
+- Deploy next-generation firewall (NGFW) solutions.
+- Implement intrusion detection/prevention systems (IDS/IPS).
+- Configure stateful packet inspection.
+- Utilize machine learning-based anomaly detection.
+
+
+## Larger Cloud Design: Integrating Region -> Multi-DC -> AZ Framework
+To enhance the security of orchestration, compute, storage, and access control components, the design must consider:
+
+- Regions: Isolate workloads geographically for regulatory compliance and disaster recovery.
+- Data Centers: Enforce physical security at each location and implement redundant power and environmental protection mechanisms.
+- Availability Zones (AZs): Segment workloads to ensure fault isolation and high availability.
+
+
+Effective OpenStack private cloud security requires a holistic, proactive approach. Continuous adaptation, rigorous implementation of multi-layered security controls, and commitment to emerging best practices are fundamental to maintaining a resilient cloud infrastructure. We can summarize the main cloud security principles in terms of the following:
+
+
+| **Pillar** | **Definition** | **Key Point(s)** |
+|---------------------|-------------------------------------------------------------------------------|----------------------------------------------------|
+| **Accountability** | Clear ownership and responsibility for securing cloud resources. | Track actions with detailed logs and use IAM tools.|
+| **Immutability** | Ensures resources are not altered post-deployment to preserve integrity. | Use immutable infrastructure and trusted pipelines.|
+| **Confidentiality** | Protects sensitive data from unauthorized access or exposure. | Encrypt data (e.g., TLS, AES) and enforce access control.|
+| **Availability** | Ensures resources are accessible when needed, even under stress. | Implement redundancy and DDoS protection. |
+| **Integrity** | Keeps systems and data unaltered except through authorized changes. | Verify with hashes and use version control. |
+| **Ephemerality** | Reduces exposure by frequently replacing or redeploying resources. | Use short-lived instances and rebase workloads regularly. |
+| **Resilience** | Builds systems that withstand and recover from failures or attacks. | Design for high availability and test disaster recovery. |
+| **Auditing and Monitoring** | Continuously observes environments for threats or violations. | Centralize logs and conduct regular security audits. |
+
+
+## Security Standards
+
+## NIST SP 800-53 (National Institute of Standards and Technology Special Publication 800-53)
+
+NIST SP 800-53 is a comprehensive catalog of security and privacy controls designed to protect federal information systems and organizations.
+It is widely adopted by public and private organizations to implement robust security frameworks.
+
+Main Focus Areas:
+
+- Access Control
+- Incidence Response
+- Risk assessment
+- Continuous monitoring
+
+## PCI DSS (Payment Card Industry Data Security Standard)
+
+PCI DSS is a security standard designed to ensure that organizations processing, storing, or transmitting credit card information maintain a secure environment.
+It is mandatory for entities handling payment card data.
+
+Main Focus Areas:
+
+- Secure Network Configurations
+- Encryption of Sensitive Data
+- Regular Monitoring and Testing
+- Strong Access Control Measures
+
+## ISO/IEC 27001 (International Organization for Standardization)
+
+ISO/IEC 27001 is a globally recognized standard for establishing, implementing, and maintaining an information security management system (ISMS).
+It helps organizations systematically manage sensitive information to keep it secure.
+
+Main Focus Areas:
+
+- Risk Management
+- Security Policies
+- Asset Management
+- Compliance and Audits
+
+## CIS Controls (Center for Internet Security)
+
+The CIS Controls are a prioritized set of actions to defend against the most common cyber threats.
+They provide actionable guidance for organizations of all sizes to enhance their security posture.
+
+Main Focus Areas:
+
+- Inventory and Control of Assets
+- Secure Configurations for Hardware and Software
+- Continuous Vulnerability Management
+- Data Protection
+
+## FedRAMP (Federal Risk and Authorization Management Program)
+
+FedRAMP is a U.S. federal program that provides a standardized approach to assessing, authorizing, and monitoring cloud service providers.
+It leverages NIST SP 800-53 as its foundation and ensures compliance for cloud services used by federal agencies.
+
+Main Focus Areas:
+
+- Security Assessments
+- Continuous Monitoring
+- Cloud Service Provider Authorization
+
+## GDPR (General Data Protection Regulation)
+
+GDPR is a European Union regulation focused on protecting personal data and ensuring privacy for EU citizens.
+It applies to all organizations processing or storing the personal data of individuals within the EU, regardless of location.
+
+Main Focus Areas:
+
+- Data Subject Rights (e.g., right to access, right to be forgotten)
+- Data Protection by Design and Default
+- Data Breach Notifications
+- Cross-Border Data Transfer Restrictions
+
+
+## Recommended References
+
+- OpenStack Security Guide
+- CIS OpenStack Benchmarks
+- SANS Cloud Security Best Practices
diff --git a/docs/security-summary.md b/docs/security-summary.md
new file mode 100644
index 00000000..7da01ec4
--- /dev/null
+++ b/docs/security-summary.md
@@ -0,0 +1,15 @@
+# Summary
+
+GeneStack's multi-region and hybrid design, leveraging OpenStack and Kubernetes, provides a robust foundation for cloud-native workloads. By integrating layered security practices, you can enhance resilience against evolving threats. Regular audits, continuous improvement, and adherence to cloud-native security principles are vital for maintaining a secure environment.
+
+Lets summarize our layered security approach in a table:
+
+| Lifecycle Phase | Infrastructure | Platform | Application | Data |
+| :---------: | :----------------------------------: | :-----------------------------------:| :-----------------------------:|:---------------------------------------:|
+| **Develop** | Secure IaC, Validate Nodes | Harden Platform Configs | Secure code, container images |Encrypt sensitive configuration and data |
+| **Distribute** | Validate Container/VM Images | Secure API and configuration delivery| Verify container integrity |Protect data during distribution |
+| **Deploy** | Handen Deployed Nodes | Enforce RBAC and security policies | Apply runtime policies |Encrypt data in transit |
+| **Runtime** | Monitor and Remediate Issues | Detect API issues and misuse | Monitor and secure workloads |Protect sensitive data streams |
+
+
+By integrating these practices our security approach ensures comprehensive protection across the entire lifecycle of Genestack based OpenStack cloud.
diff --git a/mkdocs.yml b/mkdocs.yml
index 901058cc..f4ad745f 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -137,6 +137,9 @@ nav:
- Regions in OpenStack: openstack-cloud-design-regions.md
- Availability Zones in OpenStack: openstack-cloud-design-az.md
- Host Aggregates in OpenStack: openstack-cloud-design-ha.md
+ - Accelerated Computing:
+ - Overview: accelerated-computing-overview.md
+ - Infrastructure: accelerated-computing-infrastructure.md
- Other Design Documentation:
- Genestack Infrastructure Design: openstack-cloud-design-genestack-infra.md
- Deployment Guide:
@@ -301,5 +304,11 @@ nav:
- Openstack Volumes: openstack-volumes.md
- Openstack Load Balancers: openstack-load-balancer.md
- Openstack Networks: openstack-networks.md
+ - Openstack Quotas: openstack-quota.md
+ - Security Primer:
+ - Introduction: security-introduction.md
+ - Security In Phases: security-lifecycle.md
+ - Cloud Security: security-stages.md
+ - Summary: security-summary.md
- Blog: https://blog.rackspacecloud.com/
- Status: api-status.md