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SPIN: Service Performance Isolation Infrastructure in Multi-tenancy Environment

  • Xin Hui Li
  • Tian Cheng Liu
  • Ying Li
  • Ying Chen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5364)

Abstract

The flourish of SaaS brings about a pressing requirement for Multi-tenancy to avoid dedicated installation for each tenant and benefit from reduced service delivery costs. Multi-tenancy’s intention is to satisfy requests from different tenants concurrently by a single service instance over shared hosting resources. However, extensive resource sharing easily causes inter-tenant performance interference. Therefore, Performance isolation is crucial for Multi-tenancy environment to prevent the potentially bad behaviors of one tenant from adversely affecting the performance of others in an unpredictable manner and prevent the unbalanced situation where some tenants achieve very high performance at the cost of others. Current technologies fail to achieve the goals of both performance isolation and resource share. This paper proposes a Service Performance Isolation Infrastructure (SPIN) which allows extensive resource sharing on hosting systems. Once some aggressive tenants interfere with others’ performance, SPIN gives anomaly report, identifies the aggressive tenants, and enables a self-adaptive moderation to remove their negative impacts on others. We have implemented SPIN prototype and demonstrate its isolation efficiency on the Trade6 benchmark which is revised to support Multi-tenancy. SPIN fits industry practice for a performance overhead less than 5%.

Keywords

Multi-tenancy performance monitoring resource accounting and management byte code instrumentation 

References

  1. 1.
    Carraro, G., Chong, F.: Software as a Service (SaaS): An Enterprise Perspective, Microsoft2. Corporation (October 2006), http://msdn2.microsoft.com/
  2. 2.
    Gianforte, G.: Multiple-Tenancy Hosted Applications: The Death and Rebirth of the Software Industry. RightNow Technologies Inc. (2005), http://www.rightnow.com
  3. 3.
    Chong, F., Carraro, G., Wolter, R.: Multi-Tenant Data Architecture, Microsoft Corporation (2006), http://msdn2.microsoft.com/
  4. 4.
    Tsai, C.-H., Ruan, Y., Sahu, S., Shaikh, A., Shin, K.G.: Virtualization Based Techniques for Enabling Multi-tenant Management Tools. DSOM, 171–182 (2007)Google Scholar
  5. 5.
    Czajkowski, G., Daynes, L.: Multitasking without compromise: a virtual machine evolution. In: Object-Oriented Programming, Systems, Languages, and Applications, OOPSLA 2001 (November 2001)Google Scholar
  6. 6.
    Foster, I., Kesselman, C.: The Grid: Blueprint for a New Computing Infrastructure. Morgan Kaufmann, San Francisco (1999)Google Scholar
  7. 7.
  8. 8.
    IBM. WebSphere Application Server, Trade6 benchmark, https://www14.software.ibm.com/webapp/iwm/web/preLogin.do?source=trade6
  9. 9.
    Waldspurger, C.A.: Memory resource management in vmware esx server. SIGOPS Operating Systems Review 36, 181–194 (2002)CrossRefGoogle Scholar
  10. 10.
    Jones, S.T., Arpaci-Dusseau, A.C., Arpaci-Dusseau, R.H.: Geiger: Monitoring the buffer cache in a virtual machine environment. In: The 12th International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS-XII), pp. 14–24 (2006)Google Scholar
  11. 11.
    Hopp, W.J.: Single Server Queueing Models. In: Chhajed, D., Lowe, T. (eds.) When Intuition Fails: Insights From Basic Operations Management Models and Principles. Springer, Heidelberg (scheduled for publication, 2007)Google Scholar
  12. 12.
    Hall, R.W.: Queueing methods for services and manufacturing. Prentice-Hall, Englewood Cliffs (1990)Google Scholar
  13. 13.
    Feng, W.: Improving Service for Service Systems with Different Arriving Rate, PDCATapos. In: Proceedings of the Fourth International Conference on Volume, pp. 315–318 (August 2003)Google Scholar
  14. 14.
    Renaud, O., Starck, J.L., Murtagh, F.: Wavelet-based Forecasting of short and long memory time series [EB/OL]Google Scholar
  15. 15.
    Czajkowski, G., Eicken, T.V.: Internet Servers, Safe-Language Extensions, and Structured Resource Control. In: Proceedings of the Technology of Object-Oriented Languages and Systems, Nancy, France, pp. 295–304 (1999)Google Scholar
  16. 16.
    Hulaas, J., Kalas, D.: Monitoring of Resource Consumption in Java-based Application Servers. In: Proceedings of the 10th HP OpenView University Association Plenary Worksop (HPOVUA 2003), Geneva, Swizerland (2003)Google Scholar
  17. 17.
    Liang, S., Viswanathan, D.: Comprehensive Profiling Support in the Java Virtual Machine. In: Proceedings of the 5th USENIX Conference on Object-Oriented Technologies and Systems (COOTS 1999), San Diego, CA, pp. 229–240 (1999)Google Scholar
  18. 18.
    Sutherland, D.F., Greenhouse, A., Scherlis, W.L.: The Code of Many Colors: Relating Threads to Code and Shared State. ACM SIGSOFT Software Engineering Notes 28(1), 77–83 (2002)CrossRefGoogle Scholar
  19. 19.
    Liu, Z.-X.: Short-term load forecasting method based on wavelet and reconstructed phase space. Machine Learning and Cybernetics 8, 4813–4817 (2005)Google Scholar
  20. 20.
    XiangXu, B., XinMing, Y., Hai, J.: Network Traffic predicting based on wavelet transform and autoregressive model. In: Tsui, F.-C., Sun, M., Li, C.-C., Sclabassi, R.J. (eds.) Recurrent neural networks and discrete wavelet transform for time series modeling and prediction, ICASSP, vol. 5(9-12), pp. 3359–3362 (May 1995)Google Scholar
  21. 21.
    Akaike, H.: Fitting autoregressive models for prediction. Annals of the Institute of Statistical Mathematics 23(1) (December 1971)Google Scholar
  22. 22.
    Mallat, S.G.: A Theory for Multiresolution Signal Decomposition: The Wavelet Representation. IEEE Transactions on pattern analysis and machine intelligence 11(7), 674–693 (1989)CrossRefzbMATHGoogle Scholar
  23. 23.
    Sun Microsystems, Inc. JVM Tool Interface (JVMTI), http://java.sun.com/-j2se/1.5.0/docs/guide/jvmti/
  24. 24.
    Barham, P., Dragovic, B., Fraser, K., et al.: Xen and the art of virtualization. In: Proceedings of the 19th ACM Symposium on Operating Systems Principles, SOSP 2003 (2003)Google Scholar
  25. 25.
    Jordan, M.J., Czajkowski, G., Kouklinski, K., et al.: Extending a J2EETM Server with Dynamic and Flexible Resource Management International Middleware Conference, Middleware 2004 (2004)Google Scholar
  26. 26.
    Czajkowski, G.: Application isolation in the Java Virtual Machine. In: Proceedings of the 15th ACM SIGPLAN conference on Object-oriented programming, systems, languages, and applications, OOPSLA 2000 (2000)Google Scholar
  27. 27.
    Back, G., Hsieh, W., Lepreau, J.: Processes in KaffeOS: Isolation, Resource Management, and Sharing in Java. In: Proceedings of the 4th International Conference on Operating System Design and Implementation (OSDI), San Diego, CA, pp. 334–346 (2000)Google Scholar
  28. 28.
    Provos, N., Lever, C.: Scalable Network I/O in Linux. In: Proceedings of the USENIX Technical Conference, FREENIX track (2000)Google Scholar
  29. 29.
    Sundaram, V., Chandra, A., Goyal, P., et al.: Application performance in the QLinux multimedia operating system. In: Proceedings of the 8th ACM International Conference on Multimedia 2000 (2000)Google Scholar
  30. 30.
    Poellabauer, C., Schwan, K., West, R., et al.: Flexible User/Kernel Communication For Real-Time Applications In Elinux. In: Proceedings of the Workshop on Real Time Operating Systems and Applications (2000)Google Scholar
  31. 31.
    West, R., Schwan, K.: Dynamic Window-Constrained Scheduling for Multimedia Applications. In: Proceedings of the IEEE International Conference on Multimedia Computing and Systems, ICMCS 1999 (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Xin Hui Li
    • 1
  • Tian Cheng Liu
    • 1
  • Ying Li
    • 1
  • Ying Chen
    • 1
  1. 1.IBM China Research LabBeijingChina

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