Distributed Task Schedulers (DTS) are essential for managing workloads at scale, and the choice of scheduling algorithm plays a pivotal role in ensuring performance, scalability, and reliability. This paper conducts a comparative analysis of three widely used scheduling algorithms—Round-Robin, First-Come-First-Served (FCFS), and Least-Loaded—within a Golang-based distributed task scheduling framework. The evaluation spans diverse CPU-, I/O-, and memory-intensive workloads, leveraging Prometheus-based metrics to capture critical indicators such as task completion time, fairness, and resource utilization. Experiments are deployed on Microsoft Azure Virtual Machines, where both coordinator and worker services run in Docker containers. By employing a “burst” method of task submission, this study highlights the reproducibility of results and provides a clear examination of each algorithm’s performance. The findings illuminate key trade-offs among scheduling strategies, offering valuable insights into their suitability for different workload scenarios.