Task Scheduling Menggunakan Firefly Algorithm dan Opposition-Based Learning pada Cloud Environment

Faizah, Nida'ul (2024) Task Scheduling Menggunakan Firefly Algorithm dan Opposition-Based Learning pada Cloud Environment. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Seiring dengan perkembangan teknologi cloud computing yang pesat dan kompleksitas yang semakin meningkat dalam manajemen sumber daya di lingkungan cloud, diperlukan peningkatan signifikan dalam efisiensi task scheduling. Penelitian ini bertujuan untuk meningkatkan efisiensi task scheduling di lingkungan cloud dengan mengintegrasikan Firefly Algorithm (FA) dan Opposition Based Learning (OBL). Eksperimen dan simulasi menggunakan CloudSim digunakan untuk mengevaluasi kinerja algoritma yang diusulkan dengan parameter eksperimen mencakup Total Wait Time, Average Start Time, Average Execution Time, Average Finish Time, Throughput, Makespan, imbalance degree, total Scheduling Length, Resource Utilization, dan total Energy Consumption. Uji coba menunjukkan hasil bahwa FA berhasil memberikan perbaikan signifikan pada Total Energy Consumption dan Imbalance Degree dibandingkan dengan Genetic Algorithm. Penggunaan Opposition-Based Learning (OBL) pada algoritma Firefly memberikan hasil yang sedikit lebih baik, kecuali pada Total Energy Consumption, namun tidak secara signifikan.
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With the rapid advancement of cloud computing technology and the increasing complexity in resource management within the cloud environment, a significant improvement in task scheduling efficiency is required. This research aims to enhance task scheduling efficiency in the cloud environment by integrating the Firefly Algorithm (FA) and Opposition Based Learning (OBL). Experiments and simulations using CloudSim are utilized to evaluate the performance of the proposed algorithms, with experimental parameters encompassing total wait time, average start time, average execution time, average finish time, throughput, makespan, imbalance degree, total scheduling length, resource utilization, and total rnergy consumption. The experiments reveal that FA successfully delivers a significant improvement in total energy consumption and imbalance degree compared to the Genetic Algorithm. The incorporation of Opposition-Based Learning (OBL) into the Firefly algorithm yields slightly better results, except for total energy consumption, but is not significant.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	Cloud Computing, Firefly Algorithm, Opposition-Based Learning, Task Scheduling, CloudSim
Subjects:	T Technology > T Technology (General) > T58.5 Information technology. IT--Auditing
Divisions:	Faculty of Intelligent Electrical and Informatics Technology (ELECTICS) > Information Technology > 59201-(S1) Undergraduate Thesis
Depositing User:	Nida'ul Faizah
Date Deposited:	06 Feb 2024 18:09
Last Modified:	06 Feb 2024 19:41
URI:	http://repository.its.ac.id/id/eprint/106332

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