ARASEC: Adaptive Resource Allocation and Model Training for Serverless EdgeCloud Computing

Developing and deploying resource-aware artificial intelligence (AI) models presents a compelling optimization challenge in edge computing and serverless domains. Current research focuses mainly on scalable training and inference that uses serverless frameworks to optimize operational costs. However, these approaches often overlook the challenges of heterogeneous-aware training and overall cost optimization, including computing, memory, and communication. Our work introduces a framework for a heterogeneous edge environment, focusing on the model and key performance metrics like accuracy, floating-point operations per second, number of parameters, and latency. This framework enables distributed training within a serverless architecture. Further, it explores the construction of machine learning models from existing serverless functions using a lookup table while estimating AI model training in edge, cloud, or hybrid settings. We test the framework on object detection tasks in AI model development and deployment by using serverless operations.