Analisis Perbandingan Kinerja Mekanisme Paging, Segmentation, dan Kombinasinya melalui Pendekatan Simulasi

Authors

  • Lita Dwi Setianingsih Dwi Program Studi Teknik Komputer, Fakultas Teknik Elektro, Universitas Negeri Semarang, Jl. Raya Banaran, Kota Semarang, Jawa Tengah 50229, Indonesia
  • Rifdah Mayhasna Nur Alayya Program Studi Teknik Komputer, Fakultas Teknik Elektro, Universitas Negeri Semarang, Jl. Raya Banaran, Kota Semarang, Jawa Tengah 50229, Indonesia
  • Djuniadi Djuniadi Program Studi Teknik Komputer, Fakultas Teknik Elektro, Universitas Negeri Semarang, Jl. Raya Banaran, Kota Semarang, Jawa Tengah 50229, Indonesia
  • Alfian Ardhiansyah Program Studi Teknik Komputer, Fakultas Teknik Elektro, Universitas Negeri Semarang, Jl. Raya Banaran, Kota Semarang, Jawa Tengah 50229, Indonesia

DOI:

https://doi.org/10.21009/pinter.10.1.1

Keywords:

memory management, paging, segmentation

Abstract

systems that directly affects system performance and stability, especially in multitasking environments. Differences in memory management mechanisms can result in varying performance characteristics in terms of memory allocation, fragmentation, and system management overhead. This study aims to analyze and compare the performance of paging, segmentation, and the combination of paging-segmentation mechanisms thru a simulation approach. The method used is event-driven simulation on a multitasking operating system with a single processing unit (single CPU). Performance evaluation is conducted based on the success rate of process allocation, memory fragmentation, and page fault frequency as indicators of memory management overhead. Simulation results show that the paging mechanism can maintain a high success rate of process allocation across all system load scenarios, but with an increase in page fault frequency. The segmentation mechanism shows optimal performance under low load, but experiences a decrease in allocation success under high load due to external fragmentation. Meanwhile, the paging-segmentation combination mechanism provides the most stable performance with a consistent allocation success rate and more controlled memory fragmentation. The results of this study indicate that the combination mechanism is the most balanced approach for multitasking systems with dynamic workload variations.

References

Alaei, M., & Yazdanpanah, F. (2024). A survey on heterogeneous CPU–GPU architectures and simulators. Concurrency and Computation: Practice and Experience, 37(1). https://doi.org/10.1002/cpe.8318

Alam, F., Mifthak, M. M., Purohit, S. B., Shadab, M., Byrd, G. T., & Harfoush, K. (2026). HyperShield: An automated evaluation platform for security and performance trade-offs in virtual systems. Journal of Cybersecurity and Privacy, 6(2), 56. https://doi.org/10.3390/jcp6020056

Ali, Z., Aslam, N., Marotta, A., Tiberti, W., & Cassioli, D. (2026). A systematic review of kernel-level security mechanisms, vulnerability detection and mitigation in modern operating systems. Sensors, 26(8), 2452. https://doi.org/10.3390/s26082452

Boubakri, M., & Zouari, B. (2025). A survey of RISC-V secure enclaves and trusted execution environments. Electronics, 14(21), 4171. https://doi.org/10.3390/electronics14214171

Chou, H., Hsieh, C.-H., & Wang, Y.-C. (2026). Towards enabling a containerized virtual desktop system. IEEE Access, 1–1. https://doi.org/10.1109/access.2026.3721962

Gupta, D., Sharda, D., Kaur, N., & Bansal, R. (2026). Evaluating the effectiveness of the medium of instruction on learning outcomes in technical subjects among Indian undergraduates: A quasi-experimental cross-over study. International Journal of Educational Development, 123, 103589. https://doi.org/10.1016/j.ijedudev.2026.103589

Huang, B., Yu, W., Ma, M., Wei, X., & Wang, G. (2025). Artificial-intelligence-based energy management strategies for Hybrid Electric Vehicles: A comprehensive review. Energies, 18(14), 3600. https://doi.org/10.3390/en18143600

Jiang, Z., Song, R., O’Neill, Z., & Dong, B. (2026). BESTOpt: A modular, physics-informed runtime environment for building energy modeling, simulation, and control optimization. Building Simulation. https://doi.org/10.1007/s12273-026-1472-6

Khan, M., Mushtaq, M., Pacalet, R., & Apvrille, L. (2026). Toward secure RISC-V microarchitecture: Vulnerability classes and defenses. IEEE Access, 14, 51504–51519. https://doi.org/10.1109/access.2026.3679984

Li, M. (2026). Bridging theory and practice: A packet tracer-based simulation approach to computer network education. Journal of Education and Educational Research, 18(2), 122–125. https://doi.org/10.54097/mhaz9p29

Liu, X., Yang, Y., Zhu, C., Hu, Y., & Zhao, W. (2026). Le os: A lightweight edge operating system for industrial internet of things under resource constraints. Future Generation Computer Systems, 179, 108360. https://doi.org/10.1016/j.future.2025.108360

Maas, W., & Lorenzon, A. (2025). Exploring cloud instance options for optimal performance-cost efficiency. Cluster Computing, 28(15). https://doi.org/10.1007/s10586-025-05702-5

Marinelli, T., Gómez Pérez, J. I., Tenllado, C., & Catthoor, F. (2023). COMPAD: A heterogeneous cache-scratchpad CPU architecture with data layout compaction for embedded loop-dominated applications. Journal of Systems Architecture, 145, 103022. https://doi.org/10.1016/j.sysarc.2023.103022

Siavashi, M., Sanaee, A., Sharifi, M., & Antichi, G. (2026). Phoenix - A novel technique for performance-aware orchestration of thread and page table placement in NUMA systems. SN Computer Science, 7(5). https://doi.org/10.1007/s42979-026-05157-4

Thyagaturu, A. S., Shantharama, P., Nasrallah, A., & Reisslein, M. (2022). Operating systems and hypervisors for network functions: A survey of enabling technologies and research studies. IEEE Access, 10, 79825–79873. https://doi.org/10.1109/access.2022.3194913

Xiong, C., Lin, W., Huang, H., Lin, J., & Li, K. (2026). Interference modeling and scheduling for compute-intensive batch applications. Future Generation Computer Systems, 179, 108355. https://doi.org/10.1016/j.future.2025.108355

Zhao, Y., Liu, F., Hu, Y., Wang, Z., Gao, M., Mutlu, O., Xian, H., Dong, H., Jing, N., Liang, X., Guan, H., Xu, Q., Chen, C., Quan, S., Yang, T., & Jiang, L. (2026). GUMPIM: Unitary and malleable memory for processing-in-memory with guaranteed PIM Pages. ACM Transactions on Architecture and Code Optimization. https://doi.org/10.1145/3833426

Zhu, Y., Hu, Y., Wan, C., & Wan, Q. (2026). Neuromorphic hardware materials for intelligent artificial perception and multimodal fusion: From sensing to cognition. Interdisciplinary Materials. https://doi.org/10.1002/idm2.70070

Downloads

Published

2026-06-30