Serverless Edge Computing Framework for Efficient Offloading Method with Time Frame based Priority Resource Management

With the exponential growth of data produced by ubiquitous and Internet of Things (IoT) devices, innovative methods are required to implement new IoT applications. Even though the cloud can store and process an almost infinite amount of data, low-latency applications cannot tolerate the delays that occur when sending data to and receiving it from the cloud. Bringing computation and data closer to consumers and devices, like edge computing does, seems like a promising option in this case. However, comprehensive application deployment on edge servers is limited by their limited resources. To address these issues, an edge serverless architecture is proposed in this research that makes efficient use of edge infrastructure's resources while requiring little configuration and operational overhead with reduced latency. The deployment of the system must reflect the fact that the demands of the applications must be handled at runtime, in the face of uncertainty and in a decentralized way. For the edge deployment of service-based IoT applications, a decentralized resource management technique is proposed. In this research, a Serverless Edge Computing Framework for Efficient Offloading method with Time Frame based Priority Resource Management model with Reduced Latency (SEC-TFbPR-MRL) is proposed. The goal is to make it possible for low-latency applications to run smoothly yet having a negligible effect on the edge computing environment.