OPTIMASI DAYA DATA CENTER CLOUD COMPUTING PADA WORKLOAD HIGH PERFORMANCE COMPUTING (HPC) DENGAN SCHEDULING PREDIKTIF SECARA REALTIME
Abstract
Tantangan terbesar yang muncul pada data center cloud computing adalah meningkatnya biaya konsumsi daya. Pengembangan data center akan bertolak belakang dengan penghematan daya, semakin tinggi performa sebuah data center, maka semakin tinggi pula konsumsi energi yang dibutuhkan, hal ini disebabkan oleh kebutuhan jumlah server ataupun hardware pada data center yang semakin meningkat. Data center cloud computing yang berbasis High Performance Computing (HPC) merupakan sebuah teknologi yang dibangun dari kumpulan server dalam jumlah besar untuk menjamin ketersediaan tinggi dari sebuah cloud computing, namun sebenarnya beberapa server tersebut hanya direncanakan untuk beban puncak yang jarang atau tidak pernah ter-jadi. Ketika beban pada titik terendah, maka server tersebut akan berada dalam kondisi idle. Optimasi daya dengan DNS (Dynamics Shutdown) dengan memanfaatkan kondisi beban rendah server dapat menjadi solusi yang tepat untuk mengurangi konsumsi daya pada data center. Namun jika optimasi tersebut dilakukan dengan konvensional dan hanya berdasarkan data realtime, maka kemungkinan besar akan berpengaruh terhadap performa data center. Optimasi yang dilakukan pada penelitian ini adalah dengan metode prediksi menggunakan moving average untuk menentukan penjadwalan DNS. Hasil pengujian dengan komputer virtual menunjukkan bahwa dengan metode prediksi dapat mengurangi konsumsi daya sebesar 1,14 Watt dibandingkan dengan metode konvensional.Downloads
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References
[1] I. Sofana, Cloud Computing Teori dan Praktik. Bandung: Informatika, 2012.
[2] M. Fathurahman, T. Telekomunikasi, J. T. Elektro, and P. N. Jakarta, “Efisiensi Kinerja Pengelolaan Energi pada Arsitektur Data Center Komputasi Awan Menggunakan Greencloud,” J. Ilm. Elit. ELEKTRO, vol. 3, no. 1, pp. 6–14, 2012.
[3] D. Filani, J. He, S. Gao, M. Rajappa, A. Kumar, P. Shah, and R. Nagappan, “Dynamic Data Center Power Management Trends, Issues, and Solutions,” Intel Technol. J., vol. 12, no. 01, pp. 59–67, 2008.
[4] K. C. K. Choi, R. Soma, and M. Pedram, “Dynamic Voltage and Frequency Scaling Based on Workload Decomposition,” in Proceedings of the 2004 International Symposium on Low Power Electronics and Design, 2004.
[5] D. Kliazovich, P. Bouvry, and S. U. Khan, “GreenCloud : A Packet-level Simulator of Energy- aware Cloud Computing Data Centers,” J. Supercomput., vol. 62, no. 3, pp. 1263–1283, 2012.
[6] W. Y. Lee, “Energy-saving DVFS Scheduling of Multiple Periodic Real-time Tasks on Multi-core Processors,” in Proceedings - IEEE International Symposium on Distributed Simulation and Real-Time Applications, DS-RT, 2009, pp. 216–223.
[7] K. Lakshmanan, S. Kato, and R. (Raj) Rajkumar, “Scheduling Parallel Real-Time Tasks on Multi-core Processors,” 2010 31st IEEE Real-Time Syst. Symp., pp. 259–268, 2010.
[8] J. Yang, C. Liu, Y. Shang, Z. Mao, and J. Chen, “Workload Predicting-Based Automatic Scaling in Service Clouds,” 2013.
[9] N. Roy, A. Dubey, and A. Gokhale, “Efficient Autoscaling in the Cloud Using Predictive Models for Workload Forecasting,” 2011 IEEE 4th Int. Conf. Cloud Comput., pp. 500–507, Jul. 2011.
[10] E. L. Sueur and G. Heiser, “Dynamic voltage and frequency scaling: the laws of diminishing returns,” in Proceedings of the 2010 international conference on Power aware computing and systems, 2010, pp. 1–8.
[11] R. A. Yaffee and M. McGee, Introduction to Time Series Analysis and Forecasting: With Applications of SAS and SPSS. New York: Academic Press,2000.
[12] I. Zulkarnain, “Akurasi Grafik Main Chart Dalam Prediksi Harga Saham Harian : Kasus The Winnest Dan The Losest,” J. Ilm. STIE MDP, vol. 1, no. 2, pp. 74–83, 2012.
[13] T. Lorido-Botran, J. Miguel-Alonso, and J. A. Lozano, “Auto-scaling Techniques for Elastic Applications in Cloud Environments,” 2012.
[14] P. Wahyu W, T. B. Harsono, and N. Annggis S, “Analisis Performansi Sistem Dari Implementasi Auto-Scaling pada Cloud Computing Dengan Menggunakan Predictive System Simple Moving Average,” 2014, pp. 1–7.
[15] M. Ikram, Q. U. A. Babar, Z. Anwar, and A. W. Malik, “GSAN: Green cloud-simulation for storage area networks,” Proc. - 11th Int. Conf. Front. Inf. Technol. FIT 2013, pp. 265–270, 2013.
[16] “http://www.vmware.com/id.html,” 2016. [Online]. Available: http://www.vmware.com/id.html. [Accessed: 20-Jul-2016].
[17] “https://www.proxmox.com/,” 2016. [Online]. Available: https://www.proxmox.com/. [Accessed: 20-Jul-2016].
[18] “http://www.nas4free.org/,” 2016. [Online]. Available: http://www.nas4free.org/. [Accessed: 20-Jul-2016].
[19] “https://github.com/httperf/httperf,” 2016. [Online]. Available: https://github.com/httperf/httperf. [Accessed: 20-Jul-2016].
[2] M. Fathurahman, T. Telekomunikasi, J. T. Elektro, and P. N. Jakarta, “Efisiensi Kinerja Pengelolaan Energi pada Arsitektur Data Center Komputasi Awan Menggunakan Greencloud,” J. Ilm. Elit. ELEKTRO, vol. 3, no. 1, pp. 6–14, 2012.
[3] D. Filani, J. He, S. Gao, M. Rajappa, A. Kumar, P. Shah, and R. Nagappan, “Dynamic Data Center Power Management Trends, Issues, and Solutions,” Intel Technol. J., vol. 12, no. 01, pp. 59–67, 2008.
[4] K. C. K. Choi, R. Soma, and M. Pedram, “Dynamic Voltage and Frequency Scaling Based on Workload Decomposition,” in Proceedings of the 2004 International Symposium on Low Power Electronics and Design, 2004.
[5] D. Kliazovich, P. Bouvry, and S. U. Khan, “GreenCloud : A Packet-level Simulator of Energy- aware Cloud Computing Data Centers,” J. Supercomput., vol. 62, no. 3, pp. 1263–1283, 2012.
[6] W. Y. Lee, “Energy-saving DVFS Scheduling of Multiple Periodic Real-time Tasks on Multi-core Processors,” in Proceedings - IEEE International Symposium on Distributed Simulation and Real-Time Applications, DS-RT, 2009, pp. 216–223.
[7] K. Lakshmanan, S. Kato, and R. (Raj) Rajkumar, “Scheduling Parallel Real-Time Tasks on Multi-core Processors,” 2010 31st IEEE Real-Time Syst. Symp., pp. 259–268, 2010.
[8] J. Yang, C. Liu, Y. Shang, Z. Mao, and J. Chen, “Workload Predicting-Based Automatic Scaling in Service Clouds,” 2013.
[9] N. Roy, A. Dubey, and A. Gokhale, “Efficient Autoscaling in the Cloud Using Predictive Models for Workload Forecasting,” 2011 IEEE 4th Int. Conf. Cloud Comput., pp. 500–507, Jul. 2011.
[10] E. L. Sueur and G. Heiser, “Dynamic voltage and frequency scaling: the laws of diminishing returns,” in Proceedings of the 2010 international conference on Power aware computing and systems, 2010, pp. 1–8.
[11] R. A. Yaffee and M. McGee, Introduction to Time Series Analysis and Forecasting: With Applications of SAS and SPSS. New York: Academic Press,2000.
[12] I. Zulkarnain, “Akurasi Grafik Main Chart Dalam Prediksi Harga Saham Harian : Kasus The Winnest Dan The Losest,” J. Ilm. STIE MDP, vol. 1, no. 2, pp. 74–83, 2012.
[13] T. Lorido-Botran, J. Miguel-Alonso, and J. A. Lozano, “Auto-scaling Techniques for Elastic Applications in Cloud Environments,” 2012.
[14] P. Wahyu W, T. B. Harsono, and N. Annggis S, “Analisis Performansi Sistem Dari Implementasi Auto-Scaling pada Cloud Computing Dengan Menggunakan Predictive System Simple Moving Average,” 2014, pp. 1–7.
[15] M. Ikram, Q. U. A. Babar, Z. Anwar, and A. W. Malik, “GSAN: Green cloud-simulation for storage area networks,” Proc. - 11th Int. Conf. Front. Inf. Technol. FIT 2013, pp. 265–270, 2013.
[16] “http://www.vmware.com/id.html,” 2016. [Online]. Available: http://www.vmware.com/id.html. [Accessed: 20-Jul-2016].
[17] “https://www.proxmox.com/,” 2016. [Online]. Available: https://www.proxmox.com/. [Accessed: 20-Jul-2016].
[18] “http://www.nas4free.org/,” 2016. [Online]. Available: http://www.nas4free.org/. [Accessed: 20-Jul-2016].
[19] “https://github.com/httperf/httperf,” 2016. [Online]. Available: https://github.com/httperf/httperf. [Accessed: 20-Jul-2016].
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Published
2017-01-01
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How to Cite
[1]
A. Amirullah, R. M. Ijtihadie, and H. Studiawan, “OPTIMASI DAYA DATA CENTER CLOUD COMPUTING PADA WORKLOAD HIGH PERFORMANCE COMPUTING (HPC) DENGAN SCHEDULING PREDIKTIF SECARA REALTIME”, JUTI, vol. 15, no. 1, pp. 1–10, Jan. 2017, doi: 10.12962/j24068535.v15i1.a630.
