Work

LIWSA-Task: Density-Adaptive Locust-Inspired Swarm Optimization with Self-Supervised Regression Warm-Start for Pareto-Efficient Multi-Cloud Task Scheduling

Year: 2026

Type: software

Abstract: A Pareto-efficient multi-cloud task scheduling framework featuring LIWSA (Density-Adaptive Locust-Inspired Swarm Optimization) enhanced by a self-supervised regression warm-start mechanism to optimize performance, cost, and resource utilization across heterogeneous cloud environments.

Source: Code Ocean

DOI: 10.24433/co.9054184.v1

Authors: Yousef A. Alsaaidah, Abdullah Muhammed, Mohammed Alaa Ala'anzy, Mohamed Othman, Azizol Abdullah

Institutions: Universiti Putra Malaysia, 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: W7169803402

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