Resource Harvesting For Parallel Functions in Serverless Workflows

In the rapidly evolving landscape of cloud computing, serverless architectures have gained attention for their scalability and cost-effectiveness. This thesis aims to introduce a novel approach to maximize resource utilization in serverless environments through the concept of harvesting idle resources within Directed Acyclic Graph (DAG)-based workloads. Our proposed solution targets resource harvesting at parallel stages by utilizing Machine Learning models to accurately harvest or accelerate serverless functions. Additionally, we present a scheduling algorithm specifically designed to address the unique requirements of DAG workloads in cloud environments. The framework leverages dynamic resource allocation techniques to identify and exploit idle resources within the serverless environment. By intelligently redistributing resources among functions in a parallel stage, our approach minimizes the overall execution time and resource waste, thereby enhancing the overall efficiency of DAG-based serverless computing. Our findings indicate that we can improve CPU utilization by 200% and memory utilization by 30%. Additionally, our system reduces request response latency by 43% on average. This thesis provides a detailed exploration of the proposed solution's architecture, implementation, and performance evaluation using real-world workloads.