Journal of Operating Systems Development & Trends Original Research
AI-PASS: An AI-Based Predictive, Adaptive and Energy Aware Task Scheduling Framework for Heterogeneous Parallel Computing
Abstract
Heterogeneous parallel computing systems are made up of a combination of multicore CPUs, GPUs, and specialized devices to boost computational power and utilize resources. But changes in workload intensity and processor capability, energy consumption and system conditions are dynamic, making efficient task scheduling difficult. Traditional scheduling approaches are based primarily on task properties or the state of the resources employed and can be sub-optimal when workloads are evolving. This paper introduces a predictive and feedback-based scheduling framework, named AI-PASS (AI-based Predictive, Adaptive and Sustainable Scheduler), which integrates workload prediction with adaptive taskresource selection. A novel Adaptive Task Suitability Score (ATSS) is used to predict execution time, processor utilization, energy usage, thermal condition, communication overhead and compatibility of tasks and resources. The weights associated are dynamically changed based on the condition of the system. AI-PASS can be described as being closed loop with monitoring, prediction, characterization of task, selection of resources, execution, measurement, and correction based feedback. The goal of the framework is to minimize makespan, energy usage and resource imbalance, and increase utilization. Further, simulation experiments are conducted to compare the performance of AI-PASS with Round Robin, Min-Min, and HEFT on heterogeneous CPU-GPU workloads which are measured in terms of makespan, energy consumption, throughput, CPU utilization, load imbalance and scheduling overhead.
Keywords
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