A fine-grained task-aware scaling mechanism for dynamic cloud workloads
Date:2025-08-31 clicks:
Affiliation of Author(s):福建理工大学计算机科学与数学学院
Journal:Cluster Computing
Funded by:福建省自然科学基金
Key Words:Elastic scaling Task Scheduling Task Duration Distribution Fine-grained resource management Execution-Time-Sensitive Scheduling Cloud computing
Abstract:Elastic scaling is a critical approach in cloud computing that improves resource utilization and reduces costs. However, existing scaling methods are predominantly based on overall real-time load fluctuations, with limited consideration given to the characteristics of cloud task execution time distributions, a key attribute of resource allocation. This paper introduces a fine-grained elastic scaling mechanism (FGESM) that accounts for the distribution of task duration, task priority, and execution progress. By leveraging a task-aware two-tier resource pool, our approach dynamically schedules and allocates cloud resources to maximize utilization efficiency while maintaining service quality. We also propose an execution-time-sensitive scheduling algorithm to optimize task-to-resource matching. Theoretical analysis provides performance guarantees, offering insight into the conditions under which our model operates optimally. Experimental results across various workloads demonstrate the effectiveness of our approach in improving task completion time, resource utilization, cost efficiency, and SLA compliance. FGESM shows improvements in cloud resource management in experimental evaluations, demonstrating potential to enhance service quality, reduce costs, and improve overall system performance.
Indexed by:Journal paper
Discipline:Engineering
Volume:28
Issue:560
Page Number:1-26
Translation or Not:no
Date of Publication:2025-08-30
Included Journals:SCI
First Author:Chunmao Jiang
Co-author:Wei Wu