Characterization of Dynamic Resource Consumption for Interference-Aware Consolidation

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10034)

Abstract

Nowadays, our daily live concerns the usage of Information Technology, increasingly. As a result, a huge amount of data has to be processed which is outsourced from local devices to data centers. Due to fluctuating demands these are not fully utilized all the time and consume a significant amount of energy while idling. A common approach to avoid unnecessary idle times is to consolidate running services on a subset of machines and switch off the remaining ones. Unfortunately, the services on a single machine interfere with each other due to the competition for shared resources such as caches after the consolidation, which leads to a degradation of performance. Hence, data centers have to trade off between reducing the energy consumption and certain performance criteria defined in the Service Level Agreement. In order to make the trade off in advance, it is necessary to characterize services and quantify the impact to each other after a potential consolidation. Our approach is to use random variables for characterization, which includes the fluctuations of the resource consumptions. Furthermore, we would like to model the interference of services to provide a probability of exceeding a certain performance criterion.

Keywords

Dynamic workload Characterization Resource consumption Consolidation Interference Energy-efficient computing HAEC 

Notes

Acknowledgement

This work is supported by the German Research Foundation (DFG) within the Collaborative Research Center SFB 912 – HAEC. Special thanks to my supervisor Dr. Waltenegus Dargie and my colleague Frehiwot Melak Arega for their constructive feedback and inspiring discussions.

References

  1. 1.
    Berger, T.G.: Service Level Agreements. VDM (2007)Google Scholar
  2. 2.
    Brown, A.S.: Keep it cool! inside the world’s most efficient data center. The Bent of Tau Beta Pi (2014)Google Scholar
  3. 3.
    Chen, G., He, W., Liu, J., Nath, S., Rigas, L., Xiao, L., Zhao, F.: Energyaware server provisioning and load dispatching for connection-intensive internet services. In: USENIX Symposium on Networked Systems Design and Implementation (NSDI) (2008)Google Scholar
  4. 4.
    Elnozahy, E.N.M., Kistler, M., Rajamony, R.: Energy-efficient server clusters. In: Falsafi, B., Vijaykumar, T.N. (eds.) PACS 2002. LNCS, vol. 2325, pp. 179–197. Springer, Heidelberg (2003). doi:10.1007/3-540-36612-1_12 CrossRefGoogle Scholar
  5. 5.
    Fettweis, G., Nagel, W.E., Lehner, W.: Pathways to servers of the future: highly adaptive energy efficient computing (HAEC). In: Conference on Design, Automation and Test in Europe (DATE 2012) (2012)Google Scholar
  6. 6.
    Gmach, D., Rolia, J., Cherkasova, L., Kemper, A.: Workload analysis and demand prediction of enterprise data center applications. In: IEEE International Symposium on Workload Characterization (IISWC) (2007)Google Scholar
  7. 7.
    Govindan, S., Liu, J., Kansal, A., Sivasubramaniam, A.: Cuanta: quantifying effects of shared on-chip resource interference for consolidated virtual machines. In: 2nd ACM Symposium on Cloud Computing (2011)Google Scholar
  8. 8.
    Kim, S.G., Eom, H., Yeom, H.Y.: Virtual machine consolidation based on interference modeling. J. Supercomput. 66(3), 1489–1506 (2013)CrossRefGoogle Scholar
  9. 9.
    Roytman, A., Kansal, A., Govindan, S., Liu, J., Nath, S.: PACMan: performance aware virtual machine consolidation. In: 10th International Conference on Autonomic Computing (ICAC 2013) (2013)Google Scholar
  10. 10.
    Rybina, K., Dargie, W., Umashankar, S., Schill, A.: Modelling the live migration time of virtual machines. In: Debruyne, C., et al. (eds.) On the Move to Meaningful Internet Systems: OTM 2015 Conferences. LNCS, pp. 575–593. Springer, Cham (2015). doi:10.1007/978-3-319-26148-5_39 CrossRefGoogle Scholar
  11. 11.
    Srikantaiah, S., Kansal, A., Zhao, F.: Energy aware consolidation for cloud computing. In: Power Aware Computing and Systems (2008)Google Scholar
  12. 12.
    Srinivasan, S.P., Bellur, U.: Watttime: novel system power model and completion time model for DVFS-enabled servers. In: IEEE 21st International Conference on Parallel and Distributed Systems (ICPADS) (2015)Google Scholar
  13. 13.
    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)CrossRefGoogle Scholar
  14. 14.
    Zhang, W., Rajasekaran, S., Wood, T.: Big data in the background: maximizing productivity while minimizing virtual machine interference. In: Workshop on Architectures and Systems for Big Data (2013)Google Scholar
  15. 15.
    Zhu, Q., Zhu, J., Agrawal, G.: Power-aware consolidation of scientific workflows in virtualized environments. In: ACM/IEEE International Conference for High Performance Computing, Networking, Storage and Analysis (2010)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Chair for Computer Networks, Faculty of Computer ScienceTechnical University of DresdenDresdenGermany

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