Edge Computing Strategies For Secure Data Processing In Multi-Cloud Environments

Abstract

To operate applications with low latency requirements, edge computing infrastructures are frequently used. Users can use nodes that are close to their physical locations to dispatch arithmetic and information to the Cloud faster. Because users move around a lot and there aren't many resources at the Edge, managing these infrastructures presents fresh, challenging problems. Partitioning. This research introduces the Allocation, Placement, and Scaling system for efficient, automated, and scalable management of large-scale Edge topologies. Edge computing serves as a serverless platform for the Edge. Applications composed of small, stateless functions that adhere to latency restrictions can be uploaded by service providers. It takes care of them by executing the apps as containers at runtime, moving the containers across the Edge topology to accommodate the geographical spread of the workload, and allocating resources efficiently. In order to enhance the security, the Trusted key-based Secure Communication (TK-SCom) Model proposes as it combined cryptography along with a data classification-based learning approach and it performs the encryption and decryption process for a larger volume of data. The data classification is performed between the sender and receiver as the classification contains classifier i.e. modified boost classifier to check whether the data is normal or abnormal based on DDoS attack. Based on the data classification, the data authentication is performed to ensure security in the proposed TK-Scom Model. Then the proposed TK-Scom Model security is ensured based on operational cost and computational time.