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Aggregating and Managing Memory Across Computing Nodes in Cloud Environments

  • Luis A. Garrido
  • Paul Carpenter
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10524)

Abstract

Managing memory capacity in cloud environments is a challenging problem, mainly due to the variability in virtual machine (VM) memory demand that sometimes can’t be met by the memory of one node. New architectures have introduced hardware support for a shared global address space that, together with fast interconnects, enables resource sharing among multiple nodes. Thus, more memory is globally available to a computing node avoiding the costly swaps or migrations. This paper presents a solution to aggregate the memory capacity of multiple nodes in a virtualized cloud computing infrastructure. It is based on the Transcendent Memory (Tmem) abstraction and uses a user-space process to manage the memory available to a node, and distribute the aggregated memory across the computing infrastructure. We evaluate our solution using CloudSuite 3.0 benchmarks on Linux and Xen.

Keywords

Virtualization Simulation modeling and visualization 

Notes

Acknowledgements

This research has received funding from the European Union’s 7th Framework Programme (FP7/2007-2013) under grant agreement number 610456 (Euroserver). The research was also supported by the Ministry of Economy and Competitiveness of Spain under the contract TIN2012-34557, HiPEAC-3 Network of Excellence (ICT- 287759), and the FI-DGR Grant Program (file number 2016FI_B 00947) of the Government of Catalonia.

References

  1. 1.
    Magenheimer, D., Mason, C., McCracken, D., Hackel, K.: Transcendent memory and Linux. In: Proceedings of the Linux Symposium, pp. 191–200. Citeseer (2009)Google Scholar
  2. 2.
    Dong, J., Hou, R., Huang, M., Jiang, T., Zhao, B., Mckee, S., Wang, H., Cui, X., Zhang, L.: Venice: exploring server architectures for effective resource sharing. In: IEEE International Symposium on High-Performance Computer Architecture (HPCA) (2016)Google Scholar
  3. 3.
    Durand, Y., Carpenter, P., Adami, S., Bilas, A., Dutoit, D., Farcy, A., Gaydadjiev, G., Goodacre, J., Katevenis, M., Marazakis, M., Matus, E., Mavroidis, I., Thomson, J.: Euroserver: energy efficient node for european micro-servers. In: 17th Euromicro Conference on Digital System Design (DSD), pp. 206–2013. IEEE (2014)Google Scholar
  4. 4.
    Katrinis, K., Syrivelis, D., Pnevmatikatos, D., Zervas, G., Theodoropoulos, D., Koutsopoulos, I., Hasharoni, K., Raho, D., Pinto, C., Espina, F., Lopez-Buedo, S., Chen, Q., Nemirovsky, M., Roca, D., Klosx, H., Berends, T.: Rack-scale disaggregated cloud data centers: the dReDBox project vision. In: Design, Automation & Test in Europe Conference & Exhibition (DATE). IEEE (2016)Google Scholar
  5. 5.
    Thacker, C.: Beehive: a many-core computer for FPGAs. In: MSR, Silicon Valley (2010)Google Scholar
  6. 6.
    Ferdman, M., Adileh, A., Kocberber, O., Volos, S., Alisafaee, M., Jevdjic, D., Kaynak, C., Popescu, A.D., Ailamaki, A., Falsafi, B.: Clearing the clouds: a study of emerging scale-out workloads on modern hardware. In: Proceedings of the 17th International Conference on Architectural Support for Programming Languages and Operating Systems, pp. 37–48. ACM (2012)Google Scholar
  7. 7.
    Harper, F.M., Konstan, J.A.: The MovieLens datasets: history and context. In: ACM Transactions on Interactive Intelligent Systems, pp. 19:1–19:19. ACM (2015)Google Scholar
  8. 8.
    Rossi, R.A., Ahmed, N.K.: SOC-twitter-follows - Social Networks. http://networkrepository.com/soc-twitter-follows.php
  9. 9.
    Ross, R.A., Ahmed, N.K.: The network data repository with interactive graph analytics and visualization. In: Proceedings of the 29th AAAI Conference on AI (2015)Google Scholar
  10. 10.
    Ross, R.A., Ahmed, N.K.: An interactive data repository with visual analytics. SIGKDD Explor. 17(2), 37–41 (2016)CrossRefGoogle Scholar
  11. 11.
    Magenheimer, D.: Zcache and RAMster (oh, and frontswap too) overview and some benchmarking (2012). https://oss.oracle.com/projects/tmem/dist/documentation/presentations/LSFMM12-zcache-final.pdf
  12. 12.
    Ousterhout, J., Agrawal, P., Erickson, D., Kozyrakis, C., Leverich, K., Mazières, D., Mitra, S., Narayanan, A., Parulkar, G., Rosenblum, M., Rumble, S., Stratmann, E., Stutsman, R.: The case for RAMClouds: scalable high-performance storage entirely in DRAM. In: SIGOPS Operating Systems Review, vol. 43, pp. 92–105. ACM (2010)Google Scholar
  13. 13.
    Svärd, P., Hudzia, B., Tordsson, J., Elmroth, E.: Hecatonchire: towards multi-host virtual machines by server disaggregation. In: Lopes, L., et al. (eds.) Euro-Par 2014. LNCS, vol. 8806, pp. 519–529. Springer, Cham (2014). doi: 10.1007/978-3-319-14313-2_44 Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Barcelona Supercomputing CenterBarcelonaSpain

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