Security Aware Resource Management Framework (SARMF) for Edge-Cloud Computing

In recent years, there has been an increasing demand for edge computing, which utilizes resources at the edge of the network to improve response times and reduce bandwidth requirements. However, existing methods for managing and securing edge computing resources have failed to meet the Quality of Service (QoS) and Service Level Agreement (SLA) requirements of many applications. This has led to a need for a new approach that can address these requirements while maintaining the security and reliability of the system. This survey presents an overview of a new Security Aware Resource Management Framework (SARMF), focusing on how it addresses the QoS and SLA requirements of edge cloud computing. The study begins by discussing the challenges faced by existing methods, and how the SARMF is designed to overcome these challenges. This study has provided a detailed description of the SARMF, including its architecture, key components, and how it operates. It describes how the SARMF uses machine learning algorithms to analyze data on resource usage and network traffic patterns, and how it adapts to changing circumstances to ensure that resources are allocated effectively and that the system remains secured against new and evolving security threats. This study also discusses about the benefits of the SARMF, including improved accuracy and efficiency of resource allocation, reduced risk of security incidents and disruptions, and the ability to meet the QoS and SLA requirements of different applications and organizations. Overall, this study provides an in-depth analysis of the SARMF, highlighting how it addresses the QoS and SLA requirements of edge cloud computing in a secured and efficient manner. By providing a comprehensive overview of the SARMF, this research study aims to help researchers and practitioners understand how this framework can be used to improve the management and security of computing resources in edge cloud computing environments.