The "Agile and Cognitive Cloud Edge Continuum Management" (AC3) project is dedicated to developing an agile framework that efficiently manages data wi...
The "Agile and Cognitive Cloud Edge Continuum Management" (AC3) project is dedicated to developing an agile framework that efficiently manages data within the cloud-edge computing continuum. By transitioning data processing from centralized data centers to the edge—closer to the source—this project aims to improve data control, reduce costs, and streamline operations. The AC3 framework is designed to significantly enhance service scalability, agility, and effectiveness. Its microservice architecture ensures adaptability to various network contexts and events, such as resource shortages, data surges, or data source movements. This innovative approach promises to deliver a more responsive and efficient cloud-edge continuum, benefiting a wide range of applications and industries. As part of the AC3 project, the AI-Based Application Profile task focuses on defining and constructing application profiles for the Cloud-Edge Continuum Computing Module (CECCM) to manage the lifecycle of applications and their related microservices. This initiative involves several key tasks: - Identifying Components: Determining the necessary elements that constitute an effective application profile. - Monitoring and Data Collection: Implementing robust monitoring tools to collect data on application performance and behavior. - Machine Learning Algorithms: Creating ML algorithms that can predict application profiles based on historical and real-time data. The ultimate goal of the AI-Based Application Profile task is to ensure efficient, scalable, and secure application deployment. By optimizing microservice placement and management, this project aims to enhance the overall performance and reliability of applications within the cloud-edge continuum. Another significant task under the AC3 project is Use Case 2, which focuses on a smart monitoring system leveraging unmanned aerial vehicles (UAVs). The primary challenge identified is ensuring reliable data processing and communication under varying UAV operational conditions. The proposed solution integrates advanced data transmission techniques and robust data processing architectures. Key aspects of this architecture include: - Sensor-Equipped UAVs: For real-time data collection. - Edge Computing Nodes: For initial data processing. - Cloud Services: For further analysis, ensuring data integrity and quick response times. The functional requirements cover real-time data processing, high data throughput, and low latency communications, while the non-functional requirements emphasize system reliability, scalability, and security. Key Performance Indicators (KPIs) involve measuring system responsiveness, data accuracy, and UAV operational efficiency.