Private Cloud Hpc Untuk Geologi Dan Aplikasi Eksplorasi Geofisika (G&GE)
Abstract
Penggunaan High-Performance Computing (HPC) selama pandemi COVID-19 menemui kendala; banyak pekerja mengeluhkan kinerja aplikasi yang lambat dan proses grafis yang buruk. Studi ini menjelaskan bagaimana para peneliti telah melakukan eksperimen ekstensif untuk mengusulkan desain HPC Private Cloud. Studi ini menganalisis beberapa aspek penting yang mendasari proposal desain berdasarkan kebutuhan bisnis perusahaan. Metode yang digunakan dalam penelitian ini adalah agile requirements engineering yang berguna untuk meningkatkan kompleksitas dalam pengembangan sistem. Hasil dari penelitian ini adalah implementasi jenis dan model komputasi awan, optimalisasi penggunaan sumber daya komputasi HPC yang tersedia, dan pengaturan optimalisasi protokol tampilan jarak jauh. Performa aplikasi G&GE meningkat pesat hingga 12,79 kali lipat. Kesimpulannya, total waktu pemrosesan komputasi dan kualitas grafis relatif sama saat pengguna bekerja dari kantor (WFO) atau rumah (WFH).
References
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Alsahlany, A. M. (2014). Performance Analysis of VOIP Traffic Over Integrating Wireless LAN and WAN Using Different Codecs. International Journal of Wireless & Mobile Networks, 6(3), 79–89. https://doi.org/10.5121/ijwmn.2014.6306
Bokhari, M. U., Makki, Q., & Tamandani, Y. K. (2018). A Survey on Cloud Computing (pp. 149–164). https://doi.org/10.1007/978-981-10-6620-7_16
Casas, P., & Schatz, R. (2014). Quality of Experience in Cloud services: Survey and measurements. Computer Networks, 68, 149–165. https://doi.org/10.1016/j.comnet.2014.01.008
Chrobak, P. (2014). Implementation of Virtual Desktop Infrastructure in academic laboratories. 1139–1146. https://doi.org/10.15439/2014F213
Cunha, J., Pereira, T. E., Pereira, E., Rufino, I., Galvão, C., Valente, F., & Brasileiro, F. (2020). A high-throughput shared service to estimate evapotranspiration using Landsat imagery. Computers & Geosciences, 134, 104341. https://doi.org/10.1016/j.cageo.2019.104341
Fernandes, F., Beserra, D., Moreno, E. D., Schulze, B., & Pinto, R. C. G. (2016). A virtual machine scheduler based on CPU and I/O-bound features for energy-aware in high performance computing clouds. Computers & Electrical Engineering, 56, 854–870. https://doi.org/10.1016/j.compeleceng.2016.09.003
Goyal, S. (2014). Public vs Private vs Hybrid vs Community - Cloud Computing: A Critical Review. International Journal of Computer Network and Information Security, 6(3), 20–29. https://doi.org/10.5815/ijcnis.2014.03.03
Gupta, A., & Milojicic, D. (2011). Evaluation of HPC Applications on Cloud. 2011 Sixth Open Cirrus Summit, 22–26. https://doi.org/10.1109/OCS.2011.10
Hassan, H. A., Mohamed, S. A., & Sheta, W. M. (2016). Scalability and communication performance of HPC on Azure Cloud. Egyptian Informatics Journal, 17(2), 175–182. https://doi.org/10.1016/j.eij.2015.11.001
Helmi, A. M., Farhan, M. S., & Nasr, M. M. (2018). A framework for integrating geospatial information systems and hybrid cloud computing. Computers & Electrical Engineering, 67, 145–158. https://doi.org/10.1016/j.compeleceng.2018.03.027
Herrera, A. (2015). NVIDIA GRID vGPU: Delivering Scalable Graphics-Rich Virtual Desktops. Retrieved Aug, 10(June), 2015.
Hossain, M. S., & Ahmed, N. (2018). Application of Petrel Software in Reserve Estimation of Titas Gas Field ( B & C Sand ). International Conference on Energy and Environment, September, 3–7.
Kang, J., & Yu, H. (2018). Mitigation technique for performance degradation of virtual machine owing to GPU pass-through in fog computing. Journal of Communications and Networks, 20(3), 257–265. https://doi.org/10.1109/JCN.2018.000038
Kim, J. Y., Kang, J.-S., & Joh, M. (2021). GPU acceleration of MPAS microphysics WSM6 using OpenACC directives: Performance and verification. Computers & Geosciences, 146, 104627. https://doi.org/10.1016/j.cageo.2020.104627
Kurkure, U., Sivaraman, H., & Vu, L. (2017). Machine Learning Using Virtualized GPUs in Cloud Environments (pp. 591–604). https://doi.org/10.1007/978-3-319-67630-2_41
Li, J.-Y., Kuo, C.-F., Wang, Y.-T., Lee, C.-F., Chen, T.-Y., Yang, C.-T., Lai, C.-L., & Kuo, C.-C. (2017). The Implementation of a GPU-Accelerated Virtual Desktop Infrastructure Platform. 2017 International Conference on Green Informatics (ICGI), 85–92. https://doi.org/10.1109/ICGI.2017.42
Lian, J.-W. (2017). Establishing a Cloud Computing Success Model for Hospitals in Taiwan. INQUIRY: The Journal of Health Care Organization, Provision, and Financing, 54, 004695801668583. https://doi.org/10.1177/0046958016685836
Lynn, R., Contis, D., Hossain, M., Huang, N., Tucker, T., & Kurfess, T. (2017). Voxel model surface offsetting for computer-aided manufacturing using virtualized high-performance computing. Journal of Manufacturing Systems, 43, 296–304. https://doi.org/10.1016/j.jmsy.2016.12.005
Netto, M. A. S., Calheiros, R. N., Rodrigues, E. R., Cunha, R. L. F., & Buyya, R. (2019). HPC Cloud for Scientific and Business Applications. ACM Computing Surveys, 51(1), 1–29. https://doi.org/10.1145/3150224
Ponraj, A. (2019). Optimistic virtual machine placement in cloud data centers using queuing approach. Future Generation Computer Systems, 93, 338–344. https://doi.org/10.1016/j.future.2018.10.022
Quang-Hung, N., Thoai, N., & Son, N. T. (2014). EPOBF: Energy Efficient Allocation of Virtual Machines in High Performance Computing Cloud (pp. 71–86). https://doi.org/10.1007/978-3-662-45947-8_6
Räss, L., Kolyukhin, D., & Minakov, A. (2019). Efficient parallel random field generator for large 3-D geophysical problems. Computers & Geosciences, 131, 158–169. https://doi.org/10.1016/j.cageo.2019.06.007
Ren, J., Qi, Y., Dai, Y., Xuan, Y., & Shi, Y. (2017). Nosv: A lightweight nested-virtualization VMM for hosting high performance computing on cloud. Journal of Systems and Software, 124, 137–152. https://doi.org/10.1016/j.jss.2016.11.001
Roh, H., Jung, C., Kim, K., Pack, S., & Lee, W. (2017). Joint flow and virtual machine placement in hybrid cloud data centers. Journal of Network and Computer Applications, 85, 4–13. https://doi.org/10.1016/j.jnca.2016.12.006
Ruhela, A., Subramoni, H., Chakraborty, S., Bayatpour, M., Kousha, P., & (DK) Panda, D. K. (2019). Efficient design for MPI asynchronous progress without dedicated resources. Parallel Computing, 85, 13–26. https://doi.org/10.1016/j.parco.2019.03.003
Schön, E.-M., Thomaschewski, J., & Escalona, M. J. (2017). Agile Requirements Engineering: A systematic literature review. Computer Standards & Interfaces, 49, 79–91. https://doi.org/10.1016/j.csi.2016.08.011
Sterling, T., Brodowicz, M., & Anderson, M. (2017). High Performance Computing: Modern Systems and Practices. Katey Birtcher.
Vega-RodrÃguez, M. A., & Santander-Jiménez, S. (2019). Parallel computing in bioinformatics: a view from high-performance, heterogeneous, and cloud computing. The Journal of Supercomputing, 75(7), 3369–3373. https://doi.org/10.1007/s11227-019-02934-2
Wilieyanto, E., & Marcel. (2018). Performance analysis of vdesktop using PCoIP accelerator vs vSGA-based on VMware environment — A case study at UKRIDA University. 2018 International Conference on Information and Communications Technology (ICOIACT), 705–708. https://doi.org/10.1109/ICOIACT.2018.8350798
Wu, D., Liu, X., Hebert, S., Gentzsch, W., & Terpenny, J. (2017). Democratizing digital design and manufacturing using high performance cloud computing: Performance evaluation and benchmarking. Journal of Manufacturing Systems, 43, 316–326. https://doi.org/10.1016/j.jmsy.2016.09.005
Xuan, P., Ligon, W. B., Srimani, P. K., Ge, R., & Luo, F. (2017). Accelerating big data analytics on HPC clusters using two-level storage. Parallel Computing, 61, 18–34. https://doi.org/10.1016/j.parco.2016.08.001
Zheng, K. (2019). 3D Reservoir Modeling Based on Mobile Platform and OpenGL ES. Journal of Physics: Conference Series, 1237(5). https://doi.org/10.1088/1742-6596/1237/5/052016
Published
2023-05-02
Section
Articles
