Financial Modeling for ARED's Innovative Infrastructure Service

The core objective of this project is to develop a comprehensive financial model for ARED, a pioneering company in the distributed infrastructure as a service sector. Learners will delve into the intricacies of ARED's business operations, market dynamics, and revenue streams to craft a financial model that not only reflects the company's current financial health but also its future growth trajectory. Additionally, this project involves researching and integrating software licensing as a novel revenue stream within the financial model. By the end of the project, learners are expected to present a robust financial model that incorporates traditional and innovative revenue mechanisms, aligning with ARED's strategic goals and enhancing its financial sustainability.

EdgeTech Deployment: Building Scalable Distributed Cloud Infrastructure

The primary objective of this project is to develop and deploy a scalable and efficient distributed cloud infrastructure that utilizes edge technology. By the end of the project, learners are expected to have built a robust system capable of delivering enhanced computing power, storage, and database services directly at the network's edge. This infrastructure should effectively reduce latency, increase cost efficiency, and ensure high availability and fault tolerance through innovative redundancy and recovery solutions.Problem Solving:Learners will tackle the challenge of integrating multiple advanced technologies to create a unified system that supports dynamic scaling and management of distributed resources. The project will address key issues such as:Minimizing latency in data access and processing by implementing edge computing solutions.Ensuring data consistency and reliability across distributed networks.Automating the deployment and management of resources using Infrastructure as Code (IaC) and custom scripting.Developing a proprietary orchestration engine that efficiently allocates workloads across both virtual and physical infrastructures.Expected Outcome:By the conclusion of this project, learners will have designed and implemented a state-of-the-art distributed cloud infrastructure that not only meets the operational demands of modern digital services but also sets new standards for data handling and application delivery within the industry. This will prepare them to contribute effectively to similar cutting-edge projects in real-world scenarios, enhancing their professional skills in cloud computing, network management, and system architecture.

Intelligent Edge: Developing a Self-Healing AI System for Distributed Infrastructure

The main objective of this project is to develop and implement a self-healing AI model for ARED's distributed edge gateway network, which is powered by GPUs and runs on the Yocto operating system. This network supports a range of applications essential for managing both the health of the hardware and various networking functionalities, including Zabbix for health monitoring, CoovaChilli and FreeRADIUS for network management, Hostapd for access point management, and additional tools for log collection and analysis.Problem Learners Will Be Solving:Learners will tackle the challenge of ensuring the robustness, reliability, and scalability of ARED's edge infrastructure by creating an AI-driven system capable of identifying and automatically rectifying a wide array of operational issues. This encompasses detecting and addressing hardware malfunctions, software crashes, network connectivity issues, and performance bottlenecks, among other potential failures, without human intervention.Expected Outcome by the End of the Project:By the end of this project, learners are expected to achieve the following outcomes:Develop a Self-Healing AI Model: Create a sophisticated AI model that can analyze data from various sources within the edge infrastructure, detect anomalies or signs of impending failures, and initiate corrective actions autonomously.Integrate with Existing Systems: Seamlessly integrate this AI model with ARED's current edge monitoring and management tools, ensuring a unified approach to infrastructure health and performance management.Implement Automation for Self-Healing: Establish a comprehensive set of automated response mechanisms that the AI model can trigger to address detected issues, ranging from simple service restarts to complex configuration adjustments.Adaptive Learning and Improvement: Incorporate mechanisms for continuous learning and adaptation within the AI model, enabling it to refine its predictive accuracy and effectiveness in issue resolution over time based on outcomes and feedback.Operationalize the Self-Healing System: Successfully deploy the self-healing system across ARED's distributed edge gateway network, demonstrating its ability to minimize downtime, reduce manual troubleshooting efforts, and enhance the overall reliability and performance of the infrastructure.This project aims to significantly advance ARED's operational capabilities, enabling the company to scale its infrastructure deployment more effectively and ensure high levels of service availability and reliability for its business customers.