HyEcoSec: Hybrid Cloud Economic and Secure Workflow Scheduling System

Main Article Content

Sarra Hammouti
Belabbas Yagoubi
Sid Ahmed Makhlouf

Abstract

Scheduling scientific workflows in cloud environments is challenging due to the need to balance multiple objectives while satisfying various constraints. In this paper, we propose a Hybrid cloud Economic and Secure workflow scheduling system (HyEcoSec) designed to minimize makespan and cost while ensuring security and meeting budget and deadline constraints. HyEcoSec comprises two modules: a Security Compliance Module to manage user-defined security requirements, and a Scheduling Module integrating a static scheduler based on the Multi-Portions Slime Mould Algorithm (MPSMA) and a dynamic re-scheduler to handle runtime failures. MPSMA, an enhanced version of the Slime Mould Algorithm (SMA), balances makespan and cost using a modified Pareto approach under budget and deadline constraints. Performance evaluation using the WorkowSim demonstrates the effectiveness of HyEcoSec. Compared to baseline methods, MPSMA achieves a competitive execution time, reduces makespan by 26% and cost by 43%, and improves resource utilization by 22%. It ensures compliance with budget and deadline constraints in almost all cases, outperforming the compared algorithms. The integration of security services shows minimal cost and time impact, confirming HyEcoSec's suitability for secure environments. Furthermore, the dynamic re-scheduler enhances execution efficiency, reducing makespan by 61% and cost by 49% in failure scenarios.

Article Details

How to Cite
[1]
S. Hammouti, B. . Yagoubi, and S. A. Makhlouf, “HyEcoSec: Hybrid Cloud Economic and Secure Workflow Scheduling System”, ECTI-CIT Transactions, vol. 19, no. 3, pp. 485–500, Aug. 2025.
Section
Research Article
Author Biography

Sid Ahmed Makhlouf, Oran 1 Ahmed Ben Bella University, Algeria

Ph.D. candidate in the University of Oran1 Ahmed Ben Bella (Algeria). His main research interests include Distributed System, Cluster, Grid & Cloud Computing, Load Balancing, Task & Workflow Scheduling, and Machine Learning

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