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Erschienen in:
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2019 | OriginalPaper | Buchkapitel

An Energy Efficient and Interference Aware Virtual Machine Consolidation Algorithm Using Workload Classification

verfasst von : Rachael Shaw, Enda Howley, Enda Barrett

Erschienen in: Service-Oriented Computing

Verlag: Springer International Publishing

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Abstract

Inefficient resource usage is one of the greatest causes of high energy consumption in cloud data centers. Virtual Machine (VM) consolidation is an effective method for improving energy related costs and environmental sustainability for modern data centers. While dynamic VM consolidation can improve energy efficiency, virtualisation technologies cannot guarantee performance isolation between co-located VMs resulting in interference issues. We address the problem by introducing a energy and interference aware VM consolidation algorithm. The proposed algorithm utilizes the predictive capabilities of a Machine Learning (ML) model in an attempt to classify VM workloads to make more informed consolidation decisions. Furthermore, using recent workload data from Microsoft Azure we present a comparative study of two popular classification algorithms and select the model with the best performance to incorporate into our proposed approach. Our empirical results demonstrate how our approach improves energy efficiency by 31% while also reducing service violations by 69%.

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Vorheriges Kapitel CSI2: Cloud Server Idleness Identification by Advanced Machine Learning in Theories and Practice
Nächstes Kapitel Thread-Level CPU and Memory Usage Control of Custom Code in Multi-tenant SaaS
Literatur
1.
Moreno, I.S., Yang, R., Xu, J., Wo, T.: Improved energy-efficiency in cloud datacenters with interference-aware virtual machine placement. In: 2013 IEEE Eleventh International Symposium on Autonomous Decentralized Systems (ISADS), pp. 1–8. IEEE (2013)
2.
Shaw, R., Howley, E., Barrett, E.: Predicting the available bandwidth on intra cloud network links for deadline constrained workflow scheduling in public clouds. In: Maximilien, M., Vallecillo, A., Wang, J., Oriol, M. (eds.) ICSOC 2017. LNCS, vol. 10601, pp. 221–228. Springer, Cham (2017). https://​doi.​org/​10.​1007/​978-3-319-69035-3_​15CrossRef
3.
Whitney, J., Delforge, P.: Scaling up energy efficiency across the data center industry: evaluating key drivers and barriers. Technical report, Natural Resources Defense Council (2014)
4.
Lee, Y.C., Zomaya, A.Y.: Energy efficient utilisation of resources in cloud computing systems. J. Supercomput. 60, 268–280 (2012)CrossRef
5.
6.
Beloglazov, A., Abawajy, J., Buyya, R.: Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Future Gener. Comput. Syst. 28, 755–768 (2012)CrossRef
7.
Shaw, R., Howley, E., Barrett, E.: An advanced reinforcement learning approach for energy-aware virtual machine consolidation in cloud data centers. In: Proceedings of the 12th International Conference for Internet Technology and Secured Transactions, pp. 61–66. IEEE, December 2017
8.
Farahnakian, F., Pahikkala, T., Liljeberg, P., Plosila, J., Hieu, N.T., Tenhunen, H.: Energy-aware VM consolidation in cloud data centers using utilisation prediction model. IEEE Trans. Cloud Comput. 7, 524–536 (2016) CrossRef
9.
10.
Sampaio, A.M., Barbosa, J.G., Prodan, R.: PIASA: a power and interference aware resource management strategy for heterogeneous workloads in cloud data centers. Simul. Model. Pract. Theory 57, 142–160 (2015)CrossRef
11.
Beloglazov, A., Buyya, R.: Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in cloud data centers. Concurr. Comput. Pract. Exp. 24, 1397–1420 (2012)CrossRef
12.
Xu, F., Liu, F., Liu, L., Jin, H., Li, B., Li, B.: iAware: making live migration of virtual machines interference-aware in the cloud. IEEE Trans. Comput. 63, 3012–3025 (2014)MathSciNetCrossRef
13.
Jersak, L.C., Ferreto, T.: Performance-aware server consolidation with adjustable interference levels. In: Proceedings of the 31st Annual ACM Symposium on Applied Computing, pp. 420–425. ACM, April 2016
14.
Verboven, S., Vanmechelen, K., Broeckhove, J.: Black box scheduling for resource intensive virtual machine workloads with interference models. Future Gener. Comput. Syst. 29(8), 1871–1884 (2013)CrossRef
15.
16.
Zhang, J., Figueiredo, R.J.: Application classification through monitoring and learning of resource consumption patterns. In: 20th International Parallel and Distributed Processing Symposium, IPDPS 2006, pp. 10–20. IEEE (2006)
17.
Nikravesh, A.Y., Ajila, S.A., Lung, C.H.: Towards an autonomic auto-scaling prediction system for cloud resource provisioning. In: Proceedings of the 10th International Symposium on Software Engineering for Adaptive and Self-Managing Systems, pp. 35–45. IEEE (2015)
18.
Mason, K., Duggan, J., Howley, E.: Evolving multi-objective neural networks using differential evolution for dynamic economic emission dispatch. In: Proceedings of the Genetic and Evolutionary Computation Conference Companion, pp. 1287–1294. ACM, July 2017
19.
Meyer, D., Leisch, F., Hornik, K.: The support vector machine under test. Neurocomputing 55(1–2), 169–186 (2003)CrossRef
20.
Dixon, S.J., Brereton, R.G.: Comparison of performance of five common classifiers represented as boundary methods: Euclidean distance to centroids, linear discriminant analysis, quadratic discriminant analysis, learning vector quantization and support vector machines, as dependent on data structure. Chemometr. Intell. Lab. Syst. 95(1), 1–17 (2009)CrossRef
21.
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, pp. 153–167. ACM, October 2017
Metadaten
Titel
An Energy Efficient and Interference Aware Virtual Machine Consolidation Algorithm Using Workload Classification
verfasst von
Rachael Shaw
Enda Howley
Enda Barrett
Copyright-Jahr
2019
Buch
Service-Oriented Computing

Print ISBN: 978-3-030-33701-8

Electronic ISBN: 978-3-030-33702-5

Copyright-Jahr: 2019

DOI
https://doi.org/10.1007/978-3-030-33702-5_20

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