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Journal of Network and Systems Management
Published in: Journal of Network and Systems Management 4/2020

31-07-2020

Decomposition Based Cloud Resource Demand Prediction Using Extreme Learning Machines

Authors: Jitendra Kumar, Ashutosh Kumar Singh

Published in: Journal of Network and Systems Management | Issue 4/2020

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Abstract

Cloud computing has drastically transformed the means of computing in past few years. Apart from numerous advantages, it suffers with a number of issues including resource under-utilization, load balancing and power consumption. The workload prediction is being widely explored to solve these issues using time series analysis regression and neural networks based models. The time series analysis based models are unable to capture the dynamics in the workload behavior whereas neural network based models offer better accuracy on the cost of high training time. This paper presents a workload prediction model based on extreme learning machines (ELM) whose learning time is very low and forecasts the workload more accurately. The performance of the model is evaluated over two real world cloud server workloads i.e. CPU and Memory demand traces of Google cluster and compared with predictive models based on state-of-art techniques including Auto Regressive Integrated Moving Average (ARIMA), Support Vector Regression (SVR), Linear Regression (LR), Differential Evolution (DE), Blackhole Algorithm (BhA), and Propagation (BP). It is observed that the proposed model outperforms the state-of-art techniques by reducing the mean prediction error up to 100% and 99% on CPU and memory request traces respectively.
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Literature
1.
Pandey, S., Sammut, L., Calheiros, R.N., Melatos, A., Buyya, R.: Scalable deployment of a LIGO physics application on public clouds: workflow engine and resource provisioning techniques. pp. 3–25
2.
Assunção, M.D.D., Veith, A.D.S., Buyya, R.: Resource elasticity for distributed data stream processing: a survey and future directions. CoRR abs/1709.01363 (2017)
3.
Birke, R., Chen, L.Y., Smirni, E., Birke, R., Chen, L.Y., Smirni, E.: Data centers in the wild: a large performance study. In: Tech. rep, IBM Research—Zurich, Switzerland (2012)
4.
Reiss, C., Tumanov, A., Ganger, G.R., Katz, R.H., Kozuch, M.A.: Heterogeneity and dynamicity of clouds at scale: Google trace analysis. ACM Symp. Cloud Comput. 2012, 1–18 (2012)
5.
Barroso, L., Hölzle, U.: The datacenter as a computer an introduction to the design of Warehouse-Scale Machines, vol. 24. Morgan & Claypool Publishers, New York (2013)
6.
Li, X., Qian, Z., Lu, S., Wu, J.: Energy efficient virtual machine placement algorithm with balanced and improved resource utilization in a data center. Math. Comput. Model. 58(5), 1222–1235 (2013)MathSciNetCrossRef
7.
8.
Kumar, J., Singh, A.K.: Cloud datacenter workload estimation using error preventive time series forecasting models. Cluster Comput. 23, 1363–1379 (2020)CrossRef
9.
Cetinski, K., Juric, M.B.: AME-WPC: Advanced model for efficient workload prediction in the cloud. Journal of Network and Computer Applications 55, 191–201 (2015)CrossRef
10.
Huang, G.B., Zhu, Q.Y., Siew, C.K.: Extreme learning machine: theory and applications. Neurocomputing 70, 489–501 (2006)CrossRef
11.
Huang, G., Huang, G.B., Song, S., You, K.: Trends in extreme learning machines: a review. Neural Netw. 61, 32–48 (2015)MATHCrossRef
12.
Huang, G., Song, S., Gupta, J.N.D., Wu, C.: Semi-supervised and unsupervised extreme learning machines. IEEE Trans. Cybern. 44(12), 2405–2417 (2014)CrossRef
13.
Singh, S., Madan, T.K., Kumar, J., Singh, A.K.: Stock market forecasting using machine learning: Today and tomorrow. In: 2019 2nd International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT). vol. 1, pp. 738–745 (2019)
14.
Su, K., Xiao, B., Liu, B., Zhang, H., Zhang, Z.: TAP: a personalized trust-aware qos prediction approach for web service recommendation. Knowl. Based Syst. 115, 55–65 (2017)CrossRef
15.
Sharma, V., Kaur, S., Kumar, J., Singh, A.K.: A fast parkinson’s disease prediction technique using PCA and artificial neural network. In: 2019 International Conference on Intelligent Computing and Control Systems (ICCS). pp. 1491–1496 (2019)
16.
Jennings, B., Stadler, R.: Resource management in clouds: survey and research challenges. J. Netw. Syst. Manag. 23(3), 567–619 (2015)CrossRef
17.
Bi, J., Yuan, H., Zhang, L., Zhang, J.: SGW-SCN: an integrated machine learning approach for workload forecasting in geo-distributed cloud data centers. Inform. Sci. 481, 57–68 (2019)MathSciNetCrossRef
18.
Yin, J., Lu, X., Chen, H., Zhao, X., Xiong, N.N.: System resource utilization analysis and prediction for cloud based applications under bursty workloads. Inform. Sci. 279, 338–357 (2014)CrossRef
19.
Witt, C., Bux, M., Gusew, W., Leser, U.: Predictive performance modeling for distributed batch processing using black box monitoring and machine learning. Inform. Syst. 82, 33–52 (2019)CrossRef
20.
Zhang, H., Jiang, G., Yoshihira, K., Chen, H.: Proactive workload management in hybrid cloud computing. IEEE Trans. Netw. Service Manag 11(1), 90–100 (2014)CrossRef
21.
Dabbagh, M., Hamdaoui, B., Guizani, M., Rayes, A.: Energy-efficient resource allocation and provisioning framework for Cloud Data Centers. IEEE Trans. Netw. Service Manag 12(3), 377–391 (2015)CrossRef
22.
Wamba, G.M., Li, Y., Orgerie, A.C., Beldiceanu, N., Menaud, J.M.: Cloud workload prediction and generation models. In: Proceedings-29th International Symposium on Computer Architecture and High Performance Computing, SBAC-PAD 2017 pp. 89–96 (2017)
23.
Kumaraswamy, S., Nair, M.K.: Intelligent VMs prediction in cloud computing environment. In: 2017 International Conference On Smart Technologies For Smart Nation (SmartTechCon) pp. 288–294 (2017)
24.
Kumar, J., Singh, A.K.: Dynamic resource scaling in cloud using neural network and black hole algorithm. In: 2016 Fifth International Conference on Eco-friendly Computing and Communication Systems (ICECCS). pp. 63–67 (2016)
25.
Kumar, J., Singh, A.K.: An efficient machine learning approach for virtual machine resource demand prediction. Int. J. Adv. Sci. Technol. 123, 21–30 (2019)CrossRef
26.
Kumar, J., Singh, A.K., Buyya, R.: Self directed learning based workload forecasting model for cloud resource management. Inf. Sci. (2020)
27.
Ramezani, F.: A fuzzy virtual machine workload prediction method for cloud environments. In: 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). pp. 1–6 (2017)
28.
Kumar, J., Singh, A.K.: Workload prediction in cloud using artificial neural network and adaptive differential evolution. Future Gen. Comput. Syst. 81, 41–52 (2018)CrossRef
29.
Kumar, J., Saxena, D., Singh, A.K., Mohan, A.: Biphase adaptive learning based neural network model for cloud workload forecasting. Soft Comput. (2020)
30.
Kumar, J., Singh, A.K.: Adaptive learning based prediction framework for cloud datacenter networks’ workload anticipation. J. Inf. Sci. Eng. (2020)
31.
Tseng, F.H., Wang, X., Chou, L.D., Chao, H.C., Leung, V.C.M.: Dynamic resource prediction and allocation for Cloud Data Center using the multiobjective genetic algorithm. IEEE Syst. J. 12(2), 1688–1699 (2018)CrossRef
32.
Mason, K., Duggan, M., Barrett, E., Duggan, J., Howley, E.: Predicting host CPU utilization in the cloud using evolutionary neural networks. Future Gen. Comput. Syst. 86, 162–173 (2018)CrossRef
33.
Shen, H., Chen, L.: Resource demand misalignment: an important factor to consider for reducing resource over-provisioning in cloud datacenters. IEEE/ACM Trans. Netw. 10, 1–15 (2018)
34.
Singh, A.K., Kumar, J.: Secure and energy aware load balancing framework for cloud data centre networks. Elect. Lett. 55(1), 540–541 (2019)CrossRef
35.
36.
Kumar, J., Singh, A.K.: Cloud resource demand prediction using differential evolution based learning. In: 2019 7th International Conference on Smart Computing Communications (ICSCC). pp. 1–5 (2019)
37.
Prevost, J.J., Nagothu, K., Kelley, B., Jamshidi, M.: Prediction of cloud data center networks loads using stochastic and neural models. In: 2011 6th International Conference on System of Systems Engineering. pp. 276–281 (2011)
38.
Chang, Y.C., Chang, R.S., Chuang, F.W.: A predictive method for workload forecasting in the cloud environment, pp. 577–585. Springer, Dordrecht (2014)
39.
Lu, Y., Panneerselvam, J., Liu, L., Wu, Y.: RVLBPNN: a workload forecasting model for smart cloud computing. Sci. Prog. 2016, 1–9 (2016)
40.
Amiri, M., Mohammad-Khanli, L.: Survey on prediction models of applications for resources provisioning in cloud. J. Netw. Comput. Appl. 82, 93–113 (2017)CrossRef
41.
Qiu, F., Zhang, B., Guo, J.: A deep learning approach for VM workload prediction in the cloud. In: 2016 17th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD). pp. 319–324 (2016)
42.
Zhang, Y., Yao, J., Guan, H.: Intelligent cloud resource management with deep reinforcement learning. IEEE Cloud Computing 4(6), 60–69 (2017)CrossRef
43.
Kumar, J., Goomer, R., Singh, A.K.: Long short term memory recurrent neural network (LSTM-RNN) based workload forecasting model for Cloud Datacenters. Procedia Comput. Sci. 125, 676–682 (2018)CrossRef
44.
Liu, N., Li, Z., Xu, J., Xu, Z., Lin, S., Qiu, Q., Tang, J., Wang, Y.: A hierarchical framework of cloud resource allocation and power management using deep reinforcement learning. In: 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS). pp. 372–382 (2017)
45.
Zhang, Q., Yang, L.T., Yan, Z., Chen, Z., Li, P.: An efficient deep learning model to predict cloud workload for industry informatics. IEEE Trans. Ind. Inf. 20, 1–9 (2018)CrossRef
46.
Patel, Y.S., Misra, R.: Performance comparison of deep VM workload prediction approaches for Cloud. In: Pattnaik, P., Rautaray, S., Das, H., Nayak, J. (eds.) Progress in Computing, Analytics and Networking. Advances in Intelligent Systems and Computing, pp. 149–160. Springer, Singapore (2018)CrossRef
47.
Xiao, Z., Member, S., Song, W., Chen, Q.: Dynamic resource allocation using virtual machines for cloud computing environment. IEEE Trans. Parallel Distrib. Syst. 24(6), 1107–1117 (2013)CrossRef
48.
Kim, S., Kim, T., Yoo, C.: Workload prediction using run-length encoding for runtime processor power management. Elect. Lett. 51(22), 1759–1761 (2015)CrossRef
49.
Amiri, M., Mohammad-Khanli, L., Mirandola, R.: A sequential pattern mining model for application workload prediction in cloud environment. J. Netw. Comput. Appl. 105, 21–62 (2018)CrossRef
50.
Amiri, M., Mohammad-Khanli, L., Mirandola, R.: An online learning model based on episode mining for workload prediction in cloud. Future Gen. Comput. Syst. 87, 83–101 (2018)CrossRef
51.
Chen, Z., Zhu, Y., Di, Y., Feng, S.: Self-adaptive prediction of cloud resource demands using ensemble model and subtractive-fuzzy clustering based fuzzy neural network. Comput. Intell. Neurosci. 2015, 14 (2015)
52.
Singh, N., Rao, S.: Ensemble learning for large-scale workload prediction. IEEE Trans. Emerg. Topics Comput. 2(2), 149–165 (2014)CrossRef
53.
Messias, V.R., Estrella, J.C., Ehlers, R., Santana, M.J., Santana, R.C., Reiff-Marganiec, S.: Combining time series prediction models using genetic algorithm to autoscaling web applications hosted in the cloud infrastructure. Neural Comput. Appl. 27(8), 2383–2406 (2016)CrossRef
54.
Alipour, H., Liu, Y.: Online machine learning for cloud resource provisioning of microservice backend systems. In: 2017 IEEE International Conference on Big Data (Big Data). pp. 2433–2441 (2017)
55.
Alawe, I., Ksentini, A., Hadjadj-Aoul, Y., Bertin, P.: Improving traffic forecasting for 5g core network scalability: a machine learning approach. IEEE Netw. 32(6), 42–49 (2018)CrossRef
56.
Chen, M., Yuan, J., Liu, D., Li, T.: An adaption scheduling based on dynamic weighted random forests for load demand forecasting. J. Supercomput. 12, 1–19 (2017)
57.
Kim, I.K., Wang, W., Qi, Y., Humphrey, M.: CloudInsight: utilizing a council of experts to predict future cloud application workloads. In: In 10th IEEE International Conference on Cloud Computing (Cloud 2018), July 2 - July 7. pp. 1–8. San Francisco, USA (2018)
58.
Zhong, W., Zhuang, Y., Sun, J., Gu, J.: A load prediction model for cloud computing using PSO-based weighted wavelet support vector machine. Appl. Intell. 20, 1–12 (2018)
59.
Jiang, Y., Perng, C., Li, T., Chang, R.N.: Cloud analytics for capacity planning and instant VM provisioning. IEEE Trans. Netw. Service Manag. 10(3), 312–325 (2013)CrossRef
60.
Kumar, J., Singh, A.K., Buyya, R.: Ensemble learning based predictive framework for virtual machine resource request prediction. Neurocomputing 397, 20–30 (2020)CrossRef
61.
Jeddi, S., Sharifian, S.: A hybrid wavelet decomposer and gmdh-elm ensemble model for network function virtualization workload forecasting in cloud computing. Appl. Soft Comput. 88, 105940 (2020)CrossRef
62.
Hyndman, R., Khandakar, Y.: Automatic time series forecasting: the forecast package for R. J. Stat. Softw. 27(3), 1–22 (2008)CrossRef
63.
64.
de Aguiar, P., Bourguignon, B., Khots, M., Massart, D., Phan-Than-Luu, R.: D-optimal designs. Chemometr. Intell. Lab. Syst. 30(2), 199–210 (1995)CrossRef
65.
Ohno, S., Shiraki, T., Tariq, M.R., Nagahara, M.: Mean squared error analysis of quantizers with error feedback. IEEE Trans. Signal Process 65(22), 5970–5981 (2017)MathSciNetMATHCrossRef
66.
Zhang, L., Bi, J., Yuan, H.: Workload forecasting with hybrid stochastic configuration networks in clouds. In: 2018 5th IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS). pp. 112–116 (2018)
67.
Zhou, Y., Wu, Y.: Analyses on influence of training data set to neural network supervised learning performance. In: Jin, D., Lin, S. (eds.) Adv. Comput. Sci. Intell. Syst. Environ., pp. 19–25. Springer, Berlin (2011)CrossRef
68.
Friedman, M.: A comparison of alternative tests of significance for the problem of \(m\) rankings. Ann. Math. Stat. 11(1), 86–92 (1940)MathSciNetMATHCrossRef
69.
Metadata
Title
Decomposition Based Cloud Resource Demand Prediction Using Extreme Learning Machines
Authors
Jitendra Kumar
Ashutosh Kumar Singh
Publication date
31-07-2020
Publisher
Springer US
Published in
Journal of Network and Systems Management / Issue 4/2020
Print ISSN: 1064-7570
Electronic ISSN: 1573-7705
DOI
https://doi.org/10.1007/s10922-020-09557-6

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