The rapid growth of Internet of Things (IoT) applications has increased the amount of computational workload generated at the network edge. Cloud computing provides high computational capacity, but transferring every task to a remote cloud can increase network delay, communication overhead and energy consumption. Edge computing reduces the physical distance between data sources and computational resources, but the limited and heterogeneous nature of edge resources creates a task scheduling challenge. Energy-aware scheduling is therefore important for improving the efficiency of Edge–Cloud environments. This paper presents LEATS-Sim-4-EC: Lightweight Energy-Aware Task Scheduling Simulator for Edge–Cloud Computing. The proposed simulator is implemented completely using client-side standard web technologies and operates directly in a web browser without any additional dependencies nor requires a dedicated server or database. A wizard-based interface allows the user to configure computing nodes, generate workloads, select a scheduling algorithm, execute the simulation and analyze the results. Three scheduling methods are considered: First Come First Serve (FCFS), Shortest Job First (SJF) and the proposed Lightweight Energy-Aware Task Scheduling (LEATS) approach. The simulator considers task size, processing capacity, node workload, network delay and estimated energy consumption. Simulation experiments are performed for different workload sizes using repeated runs. The results indicate that the LEATS strategy can reduce simulated energy consumption compared with the baseline methods while maintaining reasonable response time and node utilisation. The primary contribution of this work is the development of accessible, zero-installation educational simulator and prototyping tool rather than a novel scheduling optimization breakthrough.

Edge Computing, Cloud Computing, Task Scheduling, Energy-Aware Computing, IoT, Simulation, JavaScript, Green Computing, Resource Management.