Time-cost Tradeoff For Optimizing Contractor Npv By Cost Payment And Resource Constraints Using Nsga-ii Algorithm (case Studybandar Abbas Gas Condensate Refinery Project)

Zareei, M.; Hassan-pour, H.a.; Mosadegh-khah, M.

doi:10.22436/jmcs.012.01.02

Cited by 2 publications

(1 citation statement)

References 23 publications

(45 reference statements)

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“…The authors propose a mono-objective optimization approach to minimize the monetary cost of workflow scheduling while meeting deadline and a second multi-objective optimization approach aiming to optimize the makespan and monetary cost of workflow scheduling. In [21], the authors proposed a solution based on the NSGA-II algorithm which aims to find a tradeoff between makespan and processing cost. In [16], the authors propose a multi-objective scheduling algorithm based on NSGA-II which deals with three conflicting objectives such as makepan, processing cost and reliability.…”

Section: Literature Reviewmentioning

confidence: 99%

Multi-objective Scientific Workflows Scheduling in An Energy-aware Cloud Environment

Dahmani

Aziza

Ben-Romdhane

et al. 2023

Preprint

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The cloud has become an essential element for businesses globally, providing constant data availability while improving data center energy efficiency and reducing carbon footprints. However, data centers' energy consumption remains high, and energy reduction has become a crucial objective. In this paper, we aim to address this concern by proposing a multi-objective scientific workflow scheduling scheme on heterogeneous computing systems using a hybrid genetic algorithm approach. Our algorithm incorporates Hill Climbing to generate an initial population of chromosomes, which we further optimize using a genetic algorithm to assign each task to the most suitable virtual machine. The proposed fitness function is carefully designed to evaluate each chromosome's fitness to solve the scheduling problem. The algorithm's parameters and operators significantly affect the solution's performance, and our results show that the proposed algorithm outperforms other scheduling methods in terms of quality, reducing energy consumption, processing time, and cost. We believe that our proposed approach can play a crucial role in reducing the energy consumption and carbon footprint of cloud data centers and provide a green solution for scientific workflow scheduling.

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Section: Literature Reviewmentioning

confidence: 99%