Energy-Efficient Resource Allocation for Cloud Data Centres Using a Multi-way Data Analysis Technique

详细信息    查看全文

• 关键词：Workload prediction ; Cloud Data Centres ; Tensor factorization ; Energy Efficiency
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9731
• 期：1
• 页码：577-585
• 全文大小：113 KB
• 参考文献：1.Dutta, S., Gera, S., Verma, A., Viswanathan, B.: SmartScale: automatic application scaling in enterprise clouds. In: Proceedings of IEEE Conference on Cloud Computing, pp. 221–228 (2012)
  2.Delforge, P.: America’s data centre consuming and wasting growing amounts of energy. http://​www.​nrdc.​org/​energy/​data-centre-efficiency-assessment.​asp
  3.Reiss, C., Wilkes, J., Hellerstrin, J.L.: Google cluster-usage traces: format + schema. Google Inc. Technical report (2011)
  4.Li, X., Qian, Z., Lu, S., Wu, J.: Energy efficient virtual machine placement algorithm with balanced and improved resource utilization in a data centre. Math. Comput. Model. 58(5), 1222–1235 (2013)MathSciNet CrossRef
  5.Qazi, K., Li, Y., Sohn, A.: Workload prediction of virtual machines for harnessing data centre resources. In: Proceedings of IEEE International Conference on Cloud Computing, pp. 522–529 (2014)
  6.Dabbagh, M., Hamdaoui, B., Guizani, M., Rayes, A.: Energy-efficient cloud resource management. In: Proceedings of IEEE Conference on Computer Communications Workshops (INFOCOM), pp. 386–391 (2014)
  7.Ismaeel, S., Miri, A.: Energy-consumption clustering in cloud data centre. In: Proceedings of IEEE MEC International Conference on Big Data and Smart City (2016)
  8.Karim, R., Ding, C., Miri, A.: End-to-end performance prediction for selecting cloud service solutions. In: Proceedings of IEE Symposium On Service-oriented System Engineering, pp. 69–77 (2015)
  9.Karim, R., Ding, C., Miri, A.: End-to-end QoS prediction of vertically composed cloud services via tensor factorization. In: Proceedings of IEEE Cloud and Autonomic Computing, pp. 229–236 (2015)
  10.Acar, E., Yener, B.: Unsupervised multiway data analysis: a literature survey. IEEE Trans. Knowl. Data Eng. 21(1), 6–20 (2009)CrossRef
  11.Roy, N., Dubey, A., Gokhale, A.: Efficient autoscaling in the cloud using predictive models for workload forecasting. In: Proceedings of IEEE International Conference on Cloud Computing, pp. 500–507 (2011)
  
• 作者单位：Raed Karim (14)
  Salam Ismaeel (14)
  Ali Miri (14)
  
  14. Department of Computer Science, Ryerson University, 350 Victoria Street, Toronto, ON, M5B 2K3, Canada
  
• 丛书名：Human-Computer Interaction. Theory, Design, Development and Practice
• ISBN：978-3-319-39510-4
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349
• 卷排序：9731

文摘

Cloud Data Centres (CDCs) are facilities used to host large numbers of servers, networking and storage systems, along with other required infrastructure such as cooling, Unsupervised Power Supplies (UPS) and security systems. With the high proliferation of cloud computing and big data, more and more data and cloud-based service solutions are hosted and provisioned through these CDCs. The increasing number of CDCs used to meet enterprises’ needs has significant energy use implications, due to power use of these centres. In this paper, we propose a method to accurately predict workload in physical machines, so that energy consumption of CDCs can be reduced. We propose a multi-way prediction technique to estimate incoming workload at a CDC. We incorporate user behaviours to improve the prediction results. Our proposed prediction model produces more accurate prediction results, when compared with other well-known prediction models.