https://oldena.lpnu.ua/handle/ntb/52514| Title: | Cloud Datacenter Workload Prediction Using Complex-Valued Neural Networks |
| Authors: | Aizenberg, Igor Qazi, Kashifuddin |
| Affiliation: | Manhattan College |
| Bibliographic description (Ukraine): | Aizenberg I. Cloud Datacenter Workload Prediction Using Complex-Valued Neural Networks / Igor Aizenberg, Kashifuddin Qazi // Data stream mining and processing : proceedings of the IEEE second international conference, 21-25 August 2018, Lviv. — Львів : Lviv Politechnic Publishing House, 2018. — P. 315–321. — (Hybrid Systems of Computational Intelligence). |
| Bibliographic description (International): | Aizenberg I. Cloud Datacenter Workload Prediction Using Complex-Valued Neural Networks / Igor Aizenberg, Kashifuddin Qazi // Data stream mining and processing : proceedings of the IEEE second international conference, 21-25 August 2018, Lviv. — Lviv Politechnic Publishing House, 2018. — P. 315–321. — (Hybrid Systems of Computational Intelligence). |
| Is part of: | Data stream mining and processing : proceedings of the IEEE second international conference, 2018 |
| Conference/Event: | IEEE second international conference "Data stream mining and processing" |
| Issue Date: | 28-Feb-2018 |
| Publisher: | Lviv Politechnic Publishing House |
| Place of the edition/event: | Львів |
| Temporal Coverage: | 21-25 August 2018, Lviv |
| Keywords: | cloud datacenter workload prediction complexvalued neural networks |
| Number of pages: | 7 |
| Page range: | 315-321 |
| Start page: | 315 |
| End page: | 321 |
| Abstract: | Cloud computing infrastructures and datacenters depend on intelligent management of underlying CPU, memory, network, and storage resources. A variety of techniques such as load balancing, load consolidation, and remote memory allocation are used to maintain a fine balance between conflicting goals of high performance, and low costs and energy consumption. To meet these goals, successful prediction of the workloads is an important problem. By accurately predicting the resource utilization of host machines, datacenter owners can better manage the available resources. This paper presents a host resource usage prediction approach, based on a Multilayer Neural Network with Multi-Valued Neurons (MLMVN). An enhancement is further implemented to MLMVN to make it suitable for cloud datacenter applications. The approach is evaluated on real world load traces from Google’s cluster data, as well as two grid based load traces. The algorithm is compared against some current state-of-the-art host-load prediction algorithms to show its accuracy, as well as performance gains. |
| URI: | https://ena.lpnu.ua/handle/ntb/52514 |
| ISBN: | © Національний університет „Львівська політехніка“, 2018 © Національний університет „Львівська політехніка“, 2018 |
| Copyright owner: | © Національний університет “Львівська політехніка”, 2018 |
| URL for reference material: | http://googleresearch.blogspot.com/2011/11/moregoogle-cluster-data.html |
| References (Ukraine): | [1] I. Aizenberg, L. Sheremetov, L. Villa-Vargas, and J. Martinez-Munoz, ˜ “Multilayer neural network with multi-valued neurons in time series forecasting of oil production,” Neurocomputing, vol. 175, pp. 980–989, 2016. [2] I. Aizenberg and C. Moraga, “Multilayer feedforward neural network based on multi-valued neurons (MLMVN) and a backpropagation learning algorithm,” Soft Computing, vol. 11, no. 2, pp. 169–183, 2007. [3] N. N. Aizenberg and I. N. Aizenberg, “CNN based on multi-valued neuron as a model of associative memory for grey scale images,” in Cellular Neural Networks and their Applications, 1992. CNNA-92 Proceedings., Second International Workshop on. IEEE, 1992, pp. 36–41. [4] A. Hirose, Complex-valued neural networks. Springer Science & Business Media, 2012, vol. 400. [5] K. Qazi and I. Aizenberg, “Towards quantum computing algorithms for datacenter workload predictions,” in Cloud Computing (CLOUD), 2018 IEEE International Conference on. IEEE, 2018, p. In Press. [6] N. Herbst, A. Amin, A. Andrzejak, L. Grunske, S. Kounev, O. J. Mengshoel, and P. Sundararajan, “Online workload forecasting,” in SelfAware Computing Systems. Springer, 2017, pp. 529–553. 320 [7] Z. Gong, X. Gu, and J. Wilkes, “Press: Predictive elastic resource scaling for cloud systems,” in Network and Service Management (CNSM), 2010 International Conference on. IEEE, 2010, pp. 9–16. [8] K. Qazi, Y. Li, and A. Sohn, “Workload prediction of virtual machines for harnessing data center resources,” in Cloud Computing (CLOUD), 2014 IEEE 7th International Conference on. IEEE, 2014, pp. 522–529. [9] M. Ghorbani, Y. Wang, Y. Xue, M. Pedram, and P. Bogdan, “Prediction and control of bursty cloud workloads: a fractal framework,” in Proceedings of the 2014 International Conference on Hardware/Software Codesign and System Synthesis. ACM, 2014, p. 12. [10] S. Akioka and Y. Muraoka, “Extended forecast of cpu and network load on computational grid,” in Cluster Computing and the Grid, 2004. CCGrid 2004. IEEE International Symposium on. IEEE, 2004, pp. 765–772. [11] Y. Wu, Y. Yuan, G. Yang, and W. Zheng, “Load prediction using hybrid model for computational grid,” in Grid Computing, 2007 8th IEEE/ACM International Conference on. IEEE, 2007, pp. 235–242. [12] T. V. T. Duy, Y. Sato, and Y. Inoguchi, “Improving accuracy of host load predictions on computational grids by artificial neural networks,” International Journal of Parallel, Emergent and Distributed Systems, vol. 26, no. 4, pp. 275–290, 2011. [13] S. Di, D. Kondo, and W. Cirne, “Characterization and comparison of cloud versus grid workloads,” in Cluster Computing (CLUSTER), 2012 IEEE International Conference on. IEEE, 2012, pp. 230–238. [14] ——, “Host load prediction in a google compute cloud with a bayesian model,” in High Performance Computing, Networking, Storage and Analysis (SC), 2012 International Conference for. IEEE, 2012, pp. 1–11. [15] Q. Yang, C. Peng, H. Zhao, Y. Yu, Y. Zhou, Z. Wang, and S. Du, “A new method based on psr and ea-gmdh for host load prediction in cloud computing system,” The Journal of Supercomputing, vol. 68, no. 3, pp. 1402–1417, 2014. [16] B. Song, Y. Yu, Y. Zhou, Z. Wang, and S. Du, “Host load prediction with long short-term memory in cloud computing,” The Journal of Supercomputing, pp. 1–15, 2017. [17] Q. Yang, Y. Zhou, Y. Yu, J. Yuan, X. Xing, and S. Du, “Multi-stepahead host load prediction using autoencoder and echo state networks in cloud computing,” The Journal of Supercomputing, vol. 71, no. 8, pp. 3037–3053, 2015. [18] I. Aizenberg, C. Moraga, and D. Paliy, “A feedforward neural network based on multi-valued neurons,” in Computational Intelligence, Theory and Applications. Springer, 2005, pp. 599–612. [19] E. Aizenberg and I. Aizenberg, “Batch LLS-based learning algorithm for MLMVN with soft margins,” in Proceedings of the 2014 IEEE Symposium Series of Computational Intelligence (SSCI-2014). IEEE, 2014, pp. 48–55. [20] I. Aizenberg, A. Luchetta, and S. Manetti, “A modified learning algorithm for the multilayer neural network with multi-valued neurons based on the complex QR decomposition,” Soft Computing, vol. 16, no. 4, pp. 563–575, 2012. [21] J. Wilkes, “More Google cluster data,” Google research blog, Nov. 2011, posted at http://googleresearch.blogspot.com/2011/11/moregoogle-cluster-data.html. [22] P. A. Dinda, “The statistical properties of host load,” Scientific Programming, vol. 7, no. 3-4, pp. 211–229, 1999. |
| References (International): | [1] I. Aizenberg, L. Sheremetov, L. Villa-Vargas, and J. Martinez-Munoz, ˜ "Multilayer neural network with multi-valued neurons in time series forecasting of oil production," Neurocomputing, vol. 175, pp. 980–989, 2016. [2] I. Aizenberg and C. Moraga, "Multilayer feedforward neural network based on multi-valued neurons (MLMVN) and a backpropagation learning algorithm," Soft Computing, vol. 11, no. 2, pp. 169–183, 2007. [3] N. N. Aizenberg and I. N. Aizenberg, "CNN based on multi-valued neuron as a model of associative memory for grey scale images," in Cellular Neural Networks and their Applications, 1992. CNNA-92 Proceedings., Second International Workshop on. IEEE, 1992, pp. 36–41. [4] A. Hirose, Complex-valued neural networks. Springer Science & Business Media, 2012, vol. 400. [5] K. Qazi and I. Aizenberg, "Towards quantum computing algorithms for datacenter workload predictions," in Cloud Computing (CLOUD), 2018 IEEE International Conference on. IEEE, 2018, p. In Press. [6] N. Herbst, A. Amin, A. Andrzejak, L. Grunske, S. Kounev, O. J. Mengshoel, and P. Sundararajan, "Online workload forecasting," in SelfAware Computing Systems. Springer, 2017, pp. 529–553. 320 [7] Z. Gong, X. Gu, and J. Wilkes, "Press: Predictive elastic resource scaling for cloud systems," in Network and Service Management (CNSM), 2010 International Conference on. IEEE, 2010, pp. 9–16. [8] K. Qazi, Y. Li, and A. Sohn, "Workload prediction of virtual machines for harnessing data center resources," in Cloud Computing (CLOUD), 2014 IEEE 7th International Conference on. IEEE, 2014, pp. 522–529. [9] M. Ghorbani, Y. Wang, Y. Xue, M. Pedram, and P. Bogdan, "Prediction and control of bursty cloud workloads: a fractal framework," in Proceedings of the 2014 International Conference on Hardware/Software Codesign and System Synthesis. ACM, 2014, p. 12. [10] S. Akioka and Y. Muraoka, "Extended forecast of cpu and network load on computational grid," in Cluster Computing and the Grid, 2004. CCGrid 2004. IEEE International Symposium on. IEEE, 2004, pp. 765–772. [11] Y. Wu, Y. Yuan, G. Yang, and W. Zheng, "Load prediction using hybrid model for computational grid," in Grid Computing, 2007 8th IEEE/ACM International Conference on. IEEE, 2007, pp. 235–242. [12] T. V. T. Duy, Y. Sato, and Y. Inoguchi, "Improving accuracy of host load predictions on computational grids by artificial neural networks," International Journal of Parallel, Emergent and Distributed Systems, vol. 26, no. 4, pp. 275–290, 2011. [13] S. Di, D. Kondo, and W. Cirne, "Characterization and comparison of cloud versus grid workloads," in Cluster Computing (CLUSTER), 2012 IEEE International Conference on. IEEE, 2012, pp. 230–238. [14] --, "Host load prediction in a google compute cloud with a bayesian model," in High Performance Computing, Networking, Storage and Analysis (SC), 2012 International Conference for. IEEE, 2012, pp. 1–11. [15] Q. Yang, C. Peng, H. Zhao, Y. Yu, Y. Zhou, Z. Wang, and S. Du, "A new method based on psr and ea-gmdh for host load prediction in cloud computing system," The Journal of Supercomputing, vol. 68, no. 3, pp. 1402–1417, 2014. [16] B. Song, Y. Yu, Y. Zhou, Z. Wang, and S. Du, "Host load prediction with long short-term memory in cloud computing," The Journal of Supercomputing, pp. 1–15, 2017. [17] Q. Yang, Y. Zhou, Y. Yu, J. Yuan, X. Xing, and S. Du, "Multi-stepahead host load prediction using autoencoder and echo state networks in cloud computing," The Journal of Supercomputing, vol. 71, no. 8, pp. 3037–3053, 2015. [18] I. Aizenberg, C. Moraga, and D. Paliy, "A feedforward neural network based on multi-valued neurons," in Computational Intelligence, Theory and Applications. Springer, 2005, pp. 599–612. [19] E. Aizenberg and I. Aizenberg, "Batch LLS-based learning algorithm for MLMVN with soft margins," in Proceedings of the 2014 IEEE Symposium Series of Computational Intelligence (SSCI-2014). IEEE, 2014, pp. 48–55. [20] I. Aizenberg, A. Luchetta, and S. Manetti, "A modified learning algorithm for the multilayer neural network with multi-valued neurons based on the complex QR decomposition," Soft Computing, vol. 16, no. 4, pp. 563–575, 2012. [21] J. Wilkes, "More Google cluster data," Google research blog, Nov. 2011, posted at http://googleresearch.blogspot.com/2011/11/moregoogle-cluster-data.html. [22] P. A. Dinda, "The statistical properties of host load," Scientific Programming, vol. 7, no. 3-4, pp. 211–229, 1999. |
| Content type: | Conference Abstract |
| Appears in Collections: | Data stream mining and processing : proceedings of the IEEE second international conference |
| File | Description | Size | Format | |
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| 2018_Aizenberg_I-Cloud_Datacenter_Workload_315-321.pdf | 425.74 kB | Adobe PDF | View/Open | |
| 2018_Aizenberg_I-Cloud_Datacenter_Workload_315-321__COVER.png | 567.68 kB | image/png | View/Open |
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