A Continuous Optimization Approach for Deadline-Constrained Cloud Workflow Scheduling

TY - JOUR

T1 - A Continuous Optimization Approach for Deadline-Constrained Cloud Workflow Scheduling

AU - Yang, Liwen

AU - Ye, Lingjuan

AU - Xia, Yuanqing

N1 - Publisher Copyright: © 2014 IEEE. All rights reserved.

PY - 2026

Y1 - 2026

N2 - In cloud computing, deadline-constrained workflow scheduling, a typical NP-hard problem, plays a vital role in meeting users’ quality-of-service (QoS) and efficiently managing cloud resources. Its core difficulty lies in handling the deadline constraint, where existing methods suffer from two major issues: fixed deadline distribution strategies, which lack adaptability to diverse workflows, and inefficient metaheuristic search, which requires discrete encodings and repair rules in the combinatorial space. To overcome these limitations, we propose a continuous optimization approach to efficiently address the deadline constraint. The basic idea is to find a suitable sub-deadline for each task by iterative optimization for each workflow. First, treating each task’s sub-deadline as a decision variable, a continuous optimization problem is established to solve deadline-constrained workflow scheduling. Then, a dynamic multiswarm particle swarm optimization with social learning (SL-DMSPSO) algorithm is proposed for this continuous optimization problem, incorporating with the following two novel designs: 1) the entire population is divided into multiple swarms and is redivided at each iteration, ensuring that swarms are dynamic and randomly assigned, which facilitates information exchange among swarms and enhances exploration diversity and 2) social learning is introduced when updating the particles in each swarm, combined with individual learning to balance diversity and convergence. Experiments are conducted on real-world workflows to compare SL-DMSPSO with state-of-the-art algorithms, and the results reveal the superiority of SL-DMSPSO in effectively scheduling deadline-constrained workflows in clouds.

AB - In cloud computing, deadline-constrained workflow scheduling, a typical NP-hard problem, plays a vital role in meeting users’ quality-of-service (QoS) and efficiently managing cloud resources. Its core difficulty lies in handling the deadline constraint, where existing methods suffer from two major issues: fixed deadline distribution strategies, which lack adaptability to diverse workflows, and inefficient metaheuristic search, which requires discrete encodings and repair rules in the combinatorial space. To overcome these limitations, we propose a continuous optimization approach to efficiently address the deadline constraint. The basic idea is to find a suitable sub-deadline for each task by iterative optimization for each workflow. First, treating each task’s sub-deadline as a decision variable, a continuous optimization problem is established to solve deadline-constrained workflow scheduling. Then, a dynamic multiswarm particle swarm optimization with social learning (SL-DMSPSO) algorithm is proposed for this continuous optimization problem, incorporating with the following two novel designs: 1) the entire population is divided into multiple swarms and is redivided at each iteration, ensuring that swarms are dynamic and randomly assigned, which facilitates information exchange among swarms and enhances exploration diversity and 2) social learning is introduced when updating the particles in each swarm, combined with individual learning to balance diversity and convergence. Experiments are conducted on real-world workflows to compare SL-DMSPSO with state-of-the-art algorithms, and the results reveal the superiority of SL-DMSPSO in effectively scheduling deadline-constrained workflows in clouds.

KW - Continuous optimization

KW - deadline constraint

KW - dynamic multiswarm

KW - particle swarm optimization (PSO)

KW - workflow scheduling

UR - https://www.scopus.com/pages/publications/105028334259

U2 - 10.1109/JIOT.2026.3657425

DO - 10.1109/JIOT.2026.3657425

M3 - Article

AN - SCOPUS:105028334259

SN - 2327-4662

VL - 13

SP - 15690

EP - 15703

JO - IEEE Internet of Things Journal

JF - IEEE Internet of Things Journal

IS - 8

ER -