Analisis Komparatif dan Evaluatif terhadap Algoritma First-Come First-Served (FCFS) dalam Penjadwalan CPU di Era Komputasi Modern
DOI:
https://doi.org/10.54082/jupin.1957Kata Kunci:
First-Come First-Served, Komputasi Modern, Penjadwalan CPU, Round Robin, Shortest Job First, Sistem OperasiAbstrak
Tinjauan literatur ini bertujuan untuk mengevaluasi kembali kredibilitas dan relevansi Algoritma First-Come, First-Served (FCFS) dalam konteks lingkungan komputasi modern. Penelitian ini menggunakan pendekatan kajian literatur sistematis (Systematic Literature Review) dengan menganalisis literatur ilmiah yang diterbitkan dalam lima tahun terakhir. Analisis komparatif dilakukan dengan membandingkan performa FCFS terhadap algoritma penjadwalan CPU lain yang populer, yaitu Shortest Job First (SJF) dan Round Robin (RR), berdasarkan metrik efisiensi, keadilan, dan kompleksitas implementasi. Hasil kajian menunjukkan bahwa FCFS, meskipun fundamental dan unggul dalam kesederhanaan, memiliki keterbatasan serius di lingkungan multitasking modern akibat efek konvoi yang signifikan. Sementara itu, SJF menawarkan efisiensi waktu tunggu terbaik namun berisiko starvation, dan RR memberikan keadilan yang tinggi dengan mengorbankan overhead context switching. Temuan ini menegaskan bahwa tidak ada satu algoritma tunggal yang optimal. Setiap algoritma merepresentasikan trade-off unik. Kontribusi penelitian ini adalah menyoroti pentingnya pemahaman terhadap FCFS sebagai fondasi konseptual yang berkelanjutan, yang kini bertindak sebagai blok bangunan penting dalam pengembangan algoritma hibrida dan sistem penjadwalan adaptif di era komputasi modern.
Referensi
Abdel-Basset, M., Mohamed, R., Abd Elkhalik, W., Sharawi, M., & Sallam, K. M. (2022). Task Scheduling Approach in Cloud Computing Environment Using Hybrid Differential Evolution. Mathematics, 10(21), 4049. https://doi.org/10.3390/math10214049
Arora, N. K., & Banyal, R. K. (2021). Workflow Scheduling Using Particle Swarm Optimization and Gray Wolf Optimization Algorithm in Cloud Computing. Concurrency and Computation Practice and Experience, 33(16). https://doi.org/10.1002/cpe.6281
Banerjee, S., & Hecker, J. P. (2016). A Multi-Agent System Approach to Load-Balancing and Resource Allocation for Distributed Computing. 41–54. https://doi.org/10.1007/978-3-319-45901-1_4
Dirdal, D. O., Vo, D., Feng, Y., & Davidrajuh, R. (2024). Developing a Platform Using Petri Nets and GPenSIM for Simulation of Multiprocessor Scheduling Algorithms. Applied Sciences, 14(13), 5690. https://doi.org/10.3390/app14135690
Eltaib, M. O., Alshammari, H., Boukrara, A., Gasmi, K., & Hrizi, O. (2022). Choosing the Best Quality of Service Algorithm Using OPNET Simulation. International Journal of Electrical and Computer Engineering (Ijece), 12(4), 4079. https://doi.org/10.11591/ijece.v12i4.pp4079-4089
Ghaffar, A., Akbari, M., & Yousufzai, M. (2021). Milad Scheduling Algorithm (MSA). Kjet. https://doi.org/10.31841/kjet.2022.22
Jamil, B., Yar, A., & Ijaz, H. (2024). Dynamic Time Quantum Computation for Improved Round Robin Scheduling Algorithm Using Quartiles and Randomization (IRRQR). Sukkur Iba Journal of Computing and Mathematical Sciences, 7(2), 25–37. https://doi.org/10.30537/sjcms.v7i2.1340
Ju, L., Yin, Z., Zhou, Q., Liu, L., Pan, Y., & Tan, Z. (2022). Near-Zero Carbon Stochastic Dispatch Optimization Model for Power-to-Gas-Based Virtual Power Plant Considering Information Gap Status Theory. International Journal of Climate Change Strategies and Management, 15(2), 105–127. https://doi.org/10.1108/ijccsm-02-2022-0018
Kitchenham, B., & Charters, S. (2007). Guidelines for performing Systematic Literature Reviews in Software Engineering | BibSonomy (2.3). https://www.bibsonomy.org/bibtex/aed0229656ada843d3e3f24e5e5c9eb9
Kruekaew, B., & Kimpan, W. (2022). Multi-Objective Task Scheduling Optimization for Load Balancing in Cloud Computing Environment Using Hybrid Artificial Bee Colony Algorithm With Reinforcement Learning. Ieee Access, 10, 17803–17818. https://doi.org/10.1109/access.2022.3149955
Mostafa, S. M., & Amano, H. (2020). Dynamic Round Robin CPU Scheduling Algorithm Based on K-Means Clustering Technique. Applied Sciences, 10(15), 5134. https://doi.org/10.3390/app10155134
Omar, H. K., Jihad, K. H., & Hussein, S. F. (2021). Comparative Analysis of the Essential CPU Scheduling Algorithms. Bulletin of Electrical Engineering and Informatics, 10(5), 2742–2750. https://doi.org/10.11591/eei.v10i5.2812
Putra, T. D. (2022). Analysis of Priority Preemptive Scheduling Algorithm: Case Study. Ijarcce, 11(1). https://doi.org/10.17148/ijarcce.2022.11105
Shakor, M. Y. (2021). Scheduling and Synchronization Algorithms in Operating System: A Survey. Journal of Studies in Science and Engineering, 1(2), 1–16. https://doi.org/10.53898/josse2021121
Singh, H., Tyagi, S., Kumar, P., Gill, S. S., & Buyya, R. (2021). Metaheuristics for Scheduling of Heterogeneous Tasks in Cloud Computing Environments: Analysis, Performance Evaluation, and Future Directions. Simulation Modelling Practice and Theory, 111, 102353. https://doi.org/10.1016/j.simpat.2021.102353
Tani, H. G., & Amrani, C. E. (2016). Cloud Computing CPU Allocation and Scheduling Algorithms Using CloudSim Simulator. International Journal of Electrical and Computer Engineering (Ijece), 6(4), 1866. https://doi.org/10.11591/ijece.v6i4.pp1866-1879
Thangakumar, J., & Sambath, M. (2021). Performance Analysis of CPU Scheduling Algorithms – A Problem Solving Approach. International Journal of Science and Management Studies (Ijsms), 411–416. https://doi.org/10.51386/25815946/ijsms-v4i4p138
Unduhan
Diterbitkan
Cara Mengutip
Terbitan
Bagian
Lisensi
Hak Cipta (c) 2025 Zulfahmi Indra, Arsandi Aulia Zidan, Ahmad Affandi Silaen, Ahmad Naufal Habibi, Kalpin Palendeo Sitepu
Artikel ini berlisensi Creative Commons Attribution 4.0 International License.
