Article: Adaptive online task scheduling algorithm for resource regulation on heterogeneous platforms Journal: International Journal of Cloud Computing (IJCC) 2025 Vol.14 No.2 pp.145 - 162 Abstract: As computing technology advances, resource regulation on heterogeneous platforms has emerged as a key research area for future computing environments. In cloud task scheduling, studies focus on intelligent agent models and performance indicators that balance user experience and cost-effectiveness. Research into deep reinforcement learning and deep deterministic policy gradient (DDPG) algorithms has been conducted, incorporating heterogeneous resource regulation to address the varied needs of different data centres. Key task characteristics include length, average instruction length, and average CPU utilisation, with significant standard deviations. During training, a Poisson distribution parameter with a lambda value of 1 was used, leading to convergence in the loss curve. Although the DDPG algorithm had a slightly higher virtual machine usage cost and an instruction response time of 306.5, it provided notable economic benefits, demonstrating improved management and utilisation of computing resources. Inderscience Publishers - linking academia, business and industry through research

Title: Adaptive online task scheduling algorithm for resource regulation on heterogeneous platforms

Authors: Yongqing Liu; Fan Yang; Fuqiang Tian; Jun Mou; Bo Hu; Peiyang Wu

Addresses: State Grid Information & Telecommunication Group Co., Ltd., Beijing, 102209, China ' Aostar Information Technologies Co., Ltd., Chengdu, 610041, China ' Aostar Information Technologies Co., Ltd., Chengdu, 610041, China ' Aostar Information Technologies Co., Ltd., Chengdu, 610041, China ' Aostar Information Technologies Co., Ltd., Chengdu, 610041, China ' Aostar Information Technologies Co., Ltd., Chengdu, 610041, China

Abstract: As computing technology advances, resource regulation on heterogeneous platforms has emerged as a key research area for future computing environments. In cloud task scheduling, studies focus on intelligent agent models and performance indicators that balance user experience and cost-effectiveness. Research into deep reinforcement learning and deep deterministic policy gradient (DDPG) algorithms has been conducted, incorporating heterogeneous resource regulation to address the varied needs of different data centres. Key task characteristics include length, average instruction length, and average CPU utilisation, with significant standard deviations. During training, a Poisson distribution parameter with a lambda value of 1 was used, leading to convergence in the loss curve. Although the DDPG algorithm had a slightly higher virtual machine usage cost and an instruction response time of 306.5, it provided notable economic benefits, demonstrating improved management and utilisation of computing resources.

Keywords: heterogeneous resource regulation; cloud task scheduling; deep reinforcement learning; data centre heterogeneity; computational resource management.

DOI: 10.1504/IJCC.2025.147439

International Journal of Cloud Computing, 2025 Vol.14 No.2, pp.145 - 162

Received: 28 Oct 2024
Accepted: 05 Mar 2025
Published online: 15 Jul 2025 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article

Title: Adaptive online task scheduling algorithm for resource regulation on heterogeneous platforms

Keep up-to-date