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Density-Adaptive Locust Swarm Optimization for Cloud Workflow Scheduling

Year: 2026

Type: software

Abstract: This project presents a density-adaptive locust swarm optimization algorithm integrated with a self-supervised ordinary least squares warm-start mechanism, designed to optimize scientific workflow scheduling on public cloud infrastructures and achieve Pareto-optimal solutions by balancing performance, makespan, and operational costs.

Source: Code Ocean

DOI: 10.24433/co.0075749.v3

Author: Mohammed Alaa Ala'anzy

Institutions: Shandong University, University of Southern Denmark

Language: English

Topic: Cloud Computing and Resource Management

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: green

OpenAlex ID: W7221094094

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