Abstract
Cloud computing offers an online, on-demand and pay-as-you-go access to computing resources. The cloud enables users to adjust their consumption to their needs. Users deploy their application code, libraries and operating systems on the provider’s hardware. The resources can be allocated under the form of virtual machines (VMs). Predicting the runtime of VMs can be useful to optimize the resource allocation. We propose a formulation of this objective as a multi-class classification problem by using as much features as available when launching a VM. Experimentation carried out on real traces from the public cloud provider Outscale show that the inclusion of features extracted from tags, which are freely-typed pieces of text used to describe VMs for human operators, improve the model performance.
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Bernstein, D., Vidovic, N., Modi, S.: A cloud PaaS for high scale, function, and velocity mobile applications - with reference application as the fully connected car. In: 2010 Fifth International Conference on Systems and Networks Communications, pp. 117–123, August 2010
Bobroff, N., Kochut, A., Beaty, K.: Dynamic placement of virtual machines for managing SLA violations. In: 2007 10th IFIP/IEEE International Symposium on Integrated Network Management, pp. 119–128, May 2007
Chen, M., Mao, S., Liu, Y.: Big Data: a survey. Mob. Netw. Appl. 19(2), 171–209 (2014)
Cortez, E., Bonde, A., Muzio, A., Russinovich, M., Fontoura, M., Bianchini, R.: Resource central: understanding and predicting workloads for improved resource management in large cloud platforms. In: Proceedings of the 26th Symposium on Operating Systems Principles, SOSP 2017, pp. 153–167. ACM, New York (2017)
Di, S., Kondo, D., Cirne, W.: Google hostload prediction based on Bayesian model with optimized feature combination. J. Parallel Distrib. Comput. 74(1), 1820–1832 (2014)
Gaussier, E., Glesser, D., Reis, V., Trystram, D.: Improving backfilling by using machine learning to predict running times. In: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2015, pp. 64:1–64:10. ACM, New York (2015)
Gong, Z., Gu, X., Wilkes, J.: Press: predictive elastic resource scaling for cloud systems. In: 2010 International Conference on Network and Service Management, pp. 9–16, October 2010
Herbst, N.R., Huber, N., Kounev, S., Amrehn, E.: Self-adaptive workload classification and forecasting for proactive resource provisioning. In: Proceedings of the 4th ACM/SPEC International Conference on Performance Engineering, ICPE 2013, pp. 187–198. ACM, New York (2013)
Herbst, N.R., Kounev, S., Reussner, R.: Elasticity in cloud computing: what it is, and what it is not. In: Proceedings of the 10th International Conference on Autonomic Computing (ICAC 2013), San Jose, CA, pp. 23–27. USENIX (2013)
Khan, A., Yan, X., Tao, S., Anerousis, N.: Workload characterization and prediction in the cloud: a multiple time series approach. In: 2012 IEEE Network Operations and Management Symposium, pp. 1287–1294, April 2012
LaCurts, K., Mogul, J.C., Balakrishnan, H., Turner, Y.: Cicada: introducing predictive guarantees for cloud networks. In: 6th USENIX Workshop on Hot Topics in Cloud Computing (HotCloud 2014), Philadelphia, PA. USENIX Association (2014)
Liu, J., Shen, H., Narman, H.S.: CCRP: customized cooperative resource provisioning for high resource utilization in clouds. In: 2016 IEEE International Conference on Big Data (Big Data), pp. 243–252, December 2016
Marx, V.: The big challenges of Big Data. Nature 498, 255–260 (2013)
Mell, P., Grance, T., et al.: The NIST definition of cloud computing (2011)
Nguyen, H., Shen, Z., Gu, X., Subbiah, S., Wilkes, J.: AGILE: elastic distributed resource scaling for Infrastructure-as-a-Service. In: Proceedings of the 10th International Conference on Autonomic Computing (ICAC 2013), San Jose, CA, pp. 69–82. USENIX (2013)
Perennou, L., Callau-Zori, M., Lefebvre, S.: Understanding scheduler workload on non-hyperscale cloud platform. In: Proceedings of the 19th International Middleware Conference (Posters), Middleware 2018, pp. 23–24. ACM, New York (2018)
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, May 2016
Tsafrir, D., Etsion, Y., Feitelson, D.G.: Backfilling using system-generated predictions rather than user runtime estimates. IEEE Trans. Parallel Distrib. Syst. 18(6), 789–803 (2007)
Vogels, W.: Beyond server consolidation. Queue 6(1), 20–26 (2008)
Xue, J., Yan, F., Birke, R., Chen, L.Y., Scherer, T., Smirni, E.: PRACTISE: robust prediction of data center time series. In: 2015 11th International Conference on Network and Service Management (CNSM), pp. 126–134, November 2015
Yadwadkar, N.J., Ananthanarayanan, G., Katz, R.: Wrangler: predictable and faster jobs using fewer resources. In: Proceedings of the ACM Symposium on Cloud Computing, SOCC 2014, pp. 26:1–26:14. ACM, New York (2014)
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Perennou, L., Chiky, R. (2019). Applying Supervised Machine Learning to Predict Virtual Machine Runtime for a Non-hyperscale Cloud Provider. In: Nguyen, N., Chbeir, R., Exposito, E., Aniorté, P., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2019. Lecture Notes in Computer Science(), vol 11684. Springer, Cham. https://doi.org/10.1007/978-3-030-28374-2_58
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