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Cluster Computing

, Volume 17, Issue 4, pp 1253–1264 | Cite as

Scalable and efficient workload hotspot detection in virtualized environment

  • Zhou LeiEmail author
  • Bolin Hu
  • Jianhua Guo
  • Luokai Hu
  • Wenfeng Shen
  • Yu Lei
Article

Abstract

Workload hotspot detection is a key component of virtual machine (VM) management in virtualized environment. One of its challenges is how to effectively collect the resource usage of VMs. Also, since data centers usually have hundreds or even thousands of nodes, workload hotspot detection must be able to handle a large amount of monitoring data. In this paper, we address these two challenges. We first present a novel approach to VM memory monitoring. This approach collects memory usage data by walking through the page tables of VMs and by checking the present bit of page table entry. Second, we present a MapReduce-based approach to efficiently analyze a large amount of resource usage data of VMs and nodes. Leveraging the power of parallelism and robustness of MapReduce can significantly accelerate the detection of hotspots. Extensive simulations have been performed to evaluate the proposed approaches. The simulation results show that our approach can achieve effective estimation of memory usage with low overhead and can quickly detect workload hotspots.

Keywords

Resource monitoring Memory usage monitoring Workload computing Hotspot detection Virtualization MapReduce 

References

  1. 1.
    Barham, P., Dragovic, B., Fraser, K.: Xen and the Art of Virtualization. In: Proceedings the nineteenth ACM symposium on operating systems principles, Vol. 37 No. 5, Dec 2003Google Scholar
  2. 2.
    Hwang, T., Shin, Y., Son, K., Park, H.: Design of a hypervisor-based rootkit detection method for virtualized systems in cloud computing environments In: Proceedings AASRI Winter International Conference on Engineering and Technology (aasri-weit 2013), Atlantis Press (2013)Google Scholar
  3. 3.
    Ayad, A., Dippel, U.: Agent-based monitoring of virtual machines. In: Proceedings 2010 International Symposium in Information Technology (ITSim), June 2010Google Scholar
  4. 4.
    Viratanapanu, A., Hamid, A.K.A., Kawahara, Y., Asami, T.: On Demand Fine Grain Resource Monitoring System for Server Consolidation. In: Proceedings kaleidoscope: Beyond the Internet? - Innovations for Future Networks and Services, Dec 2010Google Scholar
  5. 5.
    Wood, T., Shenoy, P., Venkataramani, A., Yousif, M.: Black-box and gray-box strategies for virtual machine migration. In: Proceedings The 4th USENIX conference on Networked Systems Design & Implementation, 2007Google Scholar
  6. 6.
    Challa, N.: Detecting workload hotspots and dynamic provisioning of virtual machines in clouds In: Proceedings 2012 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM), 2012Google Scholar
  7. 7.
    Tusa, F., Paone, M., Villari, M.: CLEVER: A cloud-enabled virtual environment. In: Proceedings 2010 IEEE Symposium on Computers and Communications (ISCC), 2010Google Scholar
  8. 8.
    Wang, C., Schwan, K., Talwar, V., Eisenhauer, G.: A flexible architecture integrating monitoring and analytics for managing large-scale data centers. In: Proceedings 8th ACM international conference on Autonomic computing. ACM, 2011Google Scholar
  9. 9.
    Bohm, S., Engelmann, C., Scott, S.L.: Aggregation of real-time system monitoring data for analyzing large-scale parallel and distributed computing environments. In: Proceedings The 12th International Conference on High Performance Computing and Communications, Sept 2010Google Scholar
  10. 10.
  11. 11.
    Shvachko, K., Kuang, H., Radia, S., Chansler, R.: The hadoop distributed file system. In: Proceedings The 26th Symposium on Mass Storage Systems and Technologies (MSST), May 2010Google Scholar
  12. 12.
    Shafer, J., Rixner, S., Cox, A.L.: The hadoop distributed silesystem: Balancing portability and performance. In: Proceedings Performance Analysis of Systems & Software (ISPASS), March 2010Google Scholar
  13. 13.
    Borthakur, D.: The hadoop distributed file system: Architecture and design, http://hadoop.apache.org/common/docs/r0.18.0/
  14. 14.
    Dean, J., Ghemawat, S.: MapReduce: Simplified data processing on large clusters. In: Proceedings The sixth Symposium on Operating System Design and Implementation, Dec 2004Google Scholar
  15. 15.
    Shen, Q., Wan, M., Zhang, Z., Qing, S.: ”A covert channel using event channel state on xen hypervisor” Information and Communications Security, pp. 125–134. Springer International Publishing, (2013)Google Scholar
  16. 16.
    Waldspurger, C.A.: Memory resource management in VMware ESX Server. In: Proceedings The 5th symposium on Operating systems design and implementation, 2002Google Scholar
  17. 17.
    Ye, L., Lu, G., Kumar, S., Gniady, C., Hartman, J.: ”Energy-efficient storage in virtual machine environments” ACM Sigplan Notices. Vol. 45. No. 7. ACM, 2010Google Scholar
  18. 18.
    Zhao, W., Wang, Z.: Dynamic memory balancing for virtual machines. In: Proceedings The 2009 ACM SIGPLAN/SIGOPS International Conference on Virtual Execution Environments, 2009Google Scholar
  19. 19.
    Niu, Y., Yang, C., Cheng, X.: Dynamic memory demand estimating based on the guest operating system behaviors for virtual machines. In: Proceedings The ninth International Symposium on Parallel and Distributed Processing with Applications, May 2011Google Scholar
  20. 20.
  21. 21.
    Xen Architecture Overview: http://wiki.xensource.com/xenwiki/
  22. 22.
    Garcia, A., Kalva, H., Furht, B.: A study of transcoding on cloud environments for video content delivery. In: Proceedings The 2010 ACM multimedia workshop on Mobile cloud media computing, 2010Google Scholar
  23. 23.
    Boulon, J., Konwinski, A., Qi, R., Rabkin, A., Yang, E., Yang, M.: Chukwa: A large-scale monitoring system. In: Proceedings Cloud Computing and its Applications, 2008 Google Scholar
  24. 24.
    Payne, B.D., Carbone, M.D.P.A., Lee, W.: Secure and flexible monitoring of virtual machines. In: Proceedings The Twenty-Third Annual Computer Security Applications Conference, Dec 2007Google Scholar
  25. 25.
    Gorman, M.: Understanding the linux virtual memory, Manager, Feb 2004Google Scholar
  26. 26.

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Zhou Lei
    • 1
    Email author
  • Bolin Hu
    • 1
  • Jianhua Guo
    • 1
  • Luokai Hu
    • 2
  • Wenfeng Shen
    • 1
  • Yu Lei
    • 3
  1. 1.School of Computer Engineering and ScienceShanghai UniversityShanghai People’s Republic of China
  2. 2.School of ComputerHubei University of EducationWuhanPeople’s Republic of China
  3. 3.Department of Computer Science and EngineeringThe University of Texas at ArlingtonArlingtonUS

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