Advertisement

Synergy: Sharing-Aware Component Composition for Distributed Stream Processing Systems

  • Thomas Repantis
  • Xiaohui Gu
  • Vana Kalogeraki
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4290)

Abstract

Many emerging on-line data analysis applications require applying continuous query operations such as correlation, aggregation, and filtering to data streams in real-time. Distributed stream processing systems allow in-network stream processing to achieve better scalability and quality-of-service (QoS) provision. In this paper we present Synergy, a distributed stream processing middleware that provides sharing-aware component composition. Synergy enables efficient reuse of both data streams and processing components, while composing distributed stream processing applications with QoS demands. Synergy provides a set of fully distributed algorithms to discover and evaluate the reusability of available data streams and processing components when instantiating new stream applications. For QoS provision, Synergy performs QoS impact projection to examine whether the shared processing can cause QoS violations on currently running applications. We have implemented a prototype of the Synergy middleware and evaluated its performance on both PlanetLab and simulation testbeds. The experimental results show that Synergy can achieve much better resource utilization and QoS provision than previously proposed schemes, by judiciously sharing streams and processing components during application composition.

Keywords

Distributed Stream Processing Component Composition Shared Processing Quality-of-Service Resource Management 

References

  1. 1.
    Chandrasekaran, S., et al.: TelegraphCQ: Continuous dataflow processing for an uncertain world. In: Proceedings of CIDR, Asilomar, CA (2003)Google Scholar
  2. 2.
    Arasu, A., Babcock, B., Babu, S., Cieslewicz, J., Datar, M., Ito, K., Motwani, R., Srivastava, U., Widom, J.: STREAM: The Stanford data stream management system (to appear, 2005)Google Scholar
  3. 3.
    Golab, L., Ozsu, M.: Update-pattern-aware modeling and processing of continuous queries. In: Proceedings of 24th ACM SIGMOD Conference, Baltimore, MD, USA (2005)Google Scholar
  4. 4.
    Abadi, D., et al.: The design of the borealis stream processing engine. In: Proceedings of CIDR, Asilomar, CA (2005)Google Scholar
  5. 5.
    Chen, L., Reddy, K., Agrawal, G.: GATES: A grid-based middleware for distributed processing of data streams. In: Proceedings of IEEE HPDC-13, Honolulu, HI (2004)Google Scholar
  6. 6.
    Gu, X., Yu, P., Nahrstedt, K.: Optimal component composition for scalable stream processing. In: 25th IEEE ICDCS, Columbus, OH (2005)Google Scholar
  7. 7.
    Kumar, V., Cooper, B., Cai, Z., Eisenhauer, G., Schwan, K.: Resource-aware distributed stream management using dynamic overlays. In: 25th IEEE ICDCS, Columbus, OH (2005)Google Scholar
  8. 8.
    Pietzuch, P., Ledlie, J., Shneidman, J., Roussopoulos, M., Welsh, M., Seltzer, M.: Network-aware operator placement for stream-processing systems. In: Proc. of 22nd ICDE (2006)Google Scholar
  9. 9.
    Jain, N., Amini, L., Andrade, H., King, R., Park, Y., Selo, P., Venkatramani, C.: Design, implementation, and evaluation of the linear road benchmark on the stream processing core. In: Proceedings of 25th ACM SIGMOD Conference, Chicago, IL, USA (2006)Google Scholar
  10. 10.
    PlanetLab Consortium (2004), http://www.planet-lab.org/
  11. 11.
    Arabshian, K., Schulzrinne, H.: An ontology-based hierarchical peer-to-peer global service discovery system. Journal of Ubiquitous Computing and Intelligence (JUCI) (2005)Google Scholar
  12. 12.
    Abdelzaher, T.: An automated profiling subsystem for QoS-aware services. In: Proc. 6th IEEE RTAS, Real-Time Technology and Applications Symposium, Washington, DC (2000)Google Scholar
  13. 13.
    Gu, X., Nahrstedt, K., Yu, B.: SpiderNet: An integrated peer-to-peer service composition framework. In: Proceedings of IEEE HPDC-13, Honolulu, HI (2004)Google Scholar
  14. 14.
    Rowstron, A., Druschel, P.: Pastry: Scalable, distributed object location and routing for large-scale peer-to-peer systems. In: Proceedings of IFIP/ACM International Conference on Distributed Systems Platforms, Heidelberg, Germany (2001)Google Scholar
  15. 15.
    Chen, F., Repantis, T., Kalogeraki, V.: Coordinated media streaming and transcoding in peer-to-peer systems. In: Proceedings of 19th IPDPS, Denver, CO. (2005)Google Scholar
  16. 16.
    Hu, N., Steenkiste, P.: Exploiting internet route sharing for large scale available bandwidth estimation. In: Proc. of Internet Measurement Conference, IMC, New Orleans, LA (2005)Google Scholar
  17. 17.
    Tang, C., McKinley, P.: A distributed approach to topology-aware overlay path monitoring. In: Proceedings of 24th IEEE ICDCS, Tokyo, Japan (2004)Google Scholar
  18. 18.
    Seshadri, S., Kumar, V., Cooper, B.: Optimizing multiple queries in distributed data stream systems. In: 2nd Int. IEEE Workshop on Networking Meets Databases, NetDB (2006)Google Scholar
  19. 19.
    Cherkasova, L., Gupta, M.: Analysis of enterprise media server workloads: Access patterns, locality, content evolution, and rates of change. IEEE/ACM Transactions on Networking, TON 12(5), 781–794 (2004)CrossRefGoogle Scholar
  20. 20.
    Zegura, E., Calvert, K., Bhattacharjee, S.: How to model an internetwork. In: Proceedings of IEEE INFOCOM, San Francisco, CA, USA (1996)Google Scholar
  21. 21.
    Gedik, B., Liu, L.: PeerCQ: A decentralized and self-configuring peer-to-peer information monitoring system. In: Proceedings of 23rd IEEE ICDCS, Providence, RI, USA (May 2003)Google Scholar
  22. 22.
    Xing, Y., Zdonik, S., Hwang, J.: Dynamic load distribution in the borealis stream processor. In: Proc. of 21st International Conference on Data Engineering, ICDE, Tokyo, Japan (2005)Google Scholar
  23. 23.
    Kon, F., Campbell, R., Nahrstedt, K.: Using dynamic configuration to manage a scalable multimedia distributed system. Computer Communications Journal 24, 105–123 (2001)CrossRefGoogle Scholar
  24. 24.
    Cai, W., Coulson, G., Grace, P., Blair, G., Mathy, L., Yeung, W.: The gridkit distributed resource management framework. In: Sloot, P.M.A., Hoekstra, A.G., Priol, T., Reinefeld, A., Bubak, M. (eds.) EGC 2005. LNCS, vol. 3470, pp. 786–795. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  25. 25.
    Oppenheimer, D., Albrecht, J., Patterson, D., Vahdat, A.: Design and implementation tradeoffs for wide-area resource discovery. In: Proceedings of 14th IEEE HPDC-14 (2005)Google Scholar
  26. 26.
    Tai, S., Khalaf, R., Mikalsen, T.: Composition of coordinated web services. In: Jacobsen, H.-A. (ed.) Middleware 2004. LNCS, vol. 3231, pp. 294–310. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  27. 27.
    Bartoli, A., Jimenez-Peris, R., Kemme, B., Pautasso, C., Patarin, S., Wheater, S., Woodman, S.: The adapt framework for adaptable and composable web services. IEEE Distributed Systems On Line, Web Systems Section (2005)Google Scholar
  28. 28.
    Amza, C., Cox, A., Zwaenepoel, W.: A comparative evaluation of transparent scaling techniques for dynamic content servers. In: Proceedings of 21st ICDE, Tokyo, Japan (2005)Google Scholar
  29. 29.
    Colajanni, M., Grieco, R., Malandrino, D., Mazzoni, F., Scarano, V.: A scalable framework for the support of advanced edge services. In: Yang, L.T., Rana, O.F., Di Martino, B., Dongarra, J. (eds.) HPCC 2005. LNCS, vol. 3726, pp. 1033–1042. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  30. 30.
    Karbhari, P., Rabinovich, M., Xiao, Z., Douglis, F.: ACDN: A content delivery network for applications. In: Proceedings of 21st ACM SIGMOD Conference, Madison, WI (2002)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2006

Authors and Affiliations

  • Thomas Repantis
    • 1
  • Xiaohui Gu
    • 2
  • Vana Kalogeraki
    • 1
  1. 1.Dept. of Computer Science & EngineeringUniversity of CaliforniaRiversideUSA
  2. 2.IBM T.J. Watson Research CenterHawthorneUSA

Personalised recommendations