  Efficient task scheduling in heterogeneous computing environments is
imperative for optimizing resource utilization and minimizing task completion
times. In this study, we conducted a comprehensive benchmarking analysis to
evaluate the performance of four scheduling algorithms First Come, First-Served
(FCFS), FCFS with No Queuing (FCFS-NQ), Minimum Expected Completion Time
(MECT), and Minimum Expected Execution Time (MEET) across varying workload
scenarios. We defined three workload scenarios: low, medium, and high, each
representing different levels of computational demands. Through rigorous
experimentation and analysis, we assessed the effectiveness of each algorithm
in terms of total completion percentage, energy consumption, wasted energy, and
energy per completion. Our findings highlight the strengths and limitations of
each algorithm, with MECT and MEET emerging as robust contenders, dynamically
prioritizing tasks based on comprehensive estimates of completion and execution
times. Furthermore, MECT and MEET exhibit superior energy efficiency compared
to FCFS and FCFS-NQ, underscoring their suitability for resource-constrained
environments. This study provides valuable insights into the efficacy of task
scheduling algorithms in heterogeneous computing environments, enabling
informed decision-making to enhance resource allocation, minimize task
completion times, and improve energy efficiency
