Optimizing Task Scheduling in Heterogeneous Computing Environments: A Comparative Analysis of CPU, GPU, and ASIC Platforms Using E2C Simulator

TL;DR

This paper compares four task scheduling algorithms across CPU, GPU, and ASIC platforms in heterogeneous environments, revealing that MECT and MEET algorithms outperform others in energy efficiency and robustness.

Contribution

It provides a comprehensive benchmarking analysis of scheduling algorithms using the E2C simulator, highlighting the effectiveness of MECT and MEET in diverse workload scenarios.

Findings

MECT and MEET outperform FCFS variants in energy efficiency.

MECT and MEET are robust across different workload levels.

Scheduling algorithms significantly impact resource utilization and energy consumption.

Abstract

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