Artificial Bee Colony Optimization for Delay and Cost Aware Task Scheduling in Serverless Computing Environment

Serverless edge computing is increasingly adopted in smart cities and industrial automation applications, which leverages cloud computing for scalable resources and uses Function as a Service (FaaS) for efficient load balancing, pay-as-you-go execution, and third-party management of provisioning and auto-scaling. However, task scheduling in serverless environments is challenging due to service latency, provider costs, and cold start issues caused by dynamic workloads and resource availability. While existing literature has focused on task scheduling, it often overlooks the joint minimization of service delay and provider costs, as well as long-term workloads, and energy use. In this work, we propose an optimization framework using mixed integer linear programming (MILP) to jointly minimize task execution delay and provider costs, namely DECASE. Given the NP-hard nature of the optimization for large networks, we developed a metaheuristic Artificial Bee Colony (ABC) algorithm to provide near-optimal task scheduling and resource allocation within polynomial time. The developed DECASE system significantly reduces delays and serverless resource provider costs by up to 20% and 25% compared to existing methods.