Design, implementation, and evaluation of the linear road bnchmark on the stream processing core

ABSTRACT

Stream processing applications have recently gained significant attention in the networking and database community. At the core of these applications is a stream processing engine that performs resource allocation and management to support continuous tracking of queries over collections of physically-distributed and rapidly-updating data streams. While numerous stream processing systems exist, there has been little work on understanding the performance characteristics of these applications in a distributed setup. In this paper, we examine the performance bottlenecks of streaming data applications, in particular the Linear Road stream data management benchmark, in achieving good performance in large-scale distributed environments, using the Stream Processing Core (SPC), a stream processing middleware we have developed. First, we present the design and implementation of the Linear Road benchmark on the SPC middleware. SPC has been designed to scale to tens of thousands of processing nodes, while supporting concurrent applications and multiple simultaneous queries. Second, we identify the main performance bottlenecks in the Linear Road application in achieving scalability and low query response latency. Our results show that data locality, buffer capacity, physical allocation of processing elements to infrastructure nodes, and packaging for transporting streamed data are important factors in achieving good application performance. Though we evaluate our system primarily for the Linear Road application, we believe it also provides useful insights into the overall system behavior for supporting other distributed and large-scale continuous streaming data applications. Finally, we examine how SPC can be used and tuned to enable a very efficient implementation of the Linear Road application in a distributed environment.

REFERENCES

• 1: [1] http://mit.edu/its/mitsimlab.html.
• 2: [2] http://www.cs.brandeis.edu/~linearroad.
• 3: [3] http://www.cs.brown.edu/research/aurora/main.html.
• 4: Daniel J. Abadi , Don Carney , Ugur Çetintemel , Mitch Cherniack , Christian Convey , Sangdon Lee , Michael Stonebraker , Nesime Tatbul , Stan Zdonik, Aurora: a new model and architecture for data stream management, The VLDB Journal — The International Journal on Very Large Data Bases, v.12 n.2, p.120-139, August 2003 [doi>10.1007/s00778-003-0095-z]
• 5: [5] D. J. Abadi, Y. Ahmad, M. Balazinska, U. Cetintemel, M. Cherniack, J.-H. Hwang, W. Lindner, A. S. Maskey, A. Rasin, E. Ryvkina, N. Tatbul, Y. Xing, and S. Zdonik. The design of the Borealis stream processing engine. In Proceedings of the 2005 Conference on Innovative Data Systems Research (CIDR 2005), Asilomar, CA, 2005.
• 6: [6] L. Amini, H. Andrade, F. Eskesen, R. King, Y. Park, P. Selo, and C. Venkatramani. The Stream Processing Core. Technical Report RSC 23798 (submitted for publication), IBM T. J. Watson Research Center, November 2005.
• 7: Lisa Amini , Navendu Jain , Anshul Sehgal , Jeremy Silber , Olivier Verscheure, Adaptive Control of Extreme-scale Stream Processing Systems, Proceedings of the 26th IEEE International Conference on Distributed Computing Systems, p.71, July 04-07, 2006 [doi>10.1109/ICDCS.2006.13]
• 8: Arvind Arasu , Brian Babcock , Shivnath Babu , Mayur Datar , Keith Ito , Itaru Nishizawa , Justin Rosenstein , Jennifer Widom, STREAM: the stanford stream data manager (demonstration description), Proceedings of the 2003 ACM SIGMOD international conference on Management of data, June 09-12, 2003, San Diego, California [doi>10.1145/872757.872854]
• 9: [9] A. Arasu, M. Cherniack, E. Galvez, D. Maier, A. S. Maskey, E. Ryvkina, M. Stonebraker, and R. Tibbetts. Linear Road: A stream data management benchmark. In Proceedings of the 30th International Conference on Very Large Data Bases Conference (VLDB 2004), Toronto, Canada, 2004.
• 10: Michael D. Beynon , Tahsin Kurc , Umit Catalyurek , Chialin Chang , Alan Sussman , Joel Saltz, Distributed processing of very large datasets with DataCutter, Parallel Computing, v.27 n.11, p.1457-1478, October 2001 [doi>10.1016/S0167-8191(01)00099-0 ]
• 11: [11] S. Chandrasekaran, O. Cooper, A. Deshpande, M. J. Franklin, J. M. Hellerstein, W. Hong, S. Krishnamurthy, S. Madden, V. Raman, F. Reiss, and M. Shah. TelegraphCQ: Continuous dataflow processing for an uncertain world. In Proceedings of the 2003 Conference on Innovative Data Systems Research (CIDR 2003), Asilomar, CA, 2003.
• 12: [12] N. Jain, L. Amini, H. Andrade, R. King, Y. Park, P. Selo, and C. Venkatramani. Design, Implementation, and Evaluation of the Linear Road Benchmark on the Stream Processing Core. Technical Report TR-06-18, Department of Computer Sciences, University of Texas at Austin, March 2006.
• 13: [13] K. Kuo, R. Rabbah, and S. Amarasinghe. A productive programming environment for stream computing. In Proceedings of the 2nd Second Workshop on Productivity and Performance in High-End Computing, San Francisco, CA, February 2005.
• 14: Samuel Madden , Mehul Shah , Joseph M. Hellerstein , Vijayshankar Raman, Continuously adaptive continuous queries over streams, Proceedings of the 2002 ACM SIGMOD international conference on Management of data, June 03-06, 2002, Madison, Wisconsin [doi>10.1145/564691.564698]
• 15: Calton Pu , Karsten Schwan , Jonathan Walpole, Infosphere project: system support for information flow applications, ACM SIGMOD Record, v.30 n.1, p.25-34, March 2001 [doi>10.1145/373626.373680]
• 16: [16] G. Swint, G. Jung, and C. Pu. Event-based QoS for a distributed continual query system. In Proceedings of the 2005 IEEE International Conference on Information Reuse and Integration (IRI 2005), Las Vegas, NV, August 2005.
• 17: William Thies , Michal Karczmarek , Saman P. Amarasinghe, StreamIt: A Language for Streaming Applications, Proceedings of the 11th International Conference on Compiler Construction, p.179-196, April 08-12, 2002
• 18: [18] S. Zdonik, M. Stonebraker, M. Cherniak, U. Cetintemel, M. Balazinska, and H. Balakrishnan. The Aurora and Medusa projects. Bulletin of the IEEE Technical Committee on Data Engineering, March 2003.

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.4 INFORMATION SYSTEMS APPLICATIONS
      H.4.0 General

General Terms:
Design, Experimentation, Measurement, Performance

Keywords:
bottleneck analysis, distributed stream processing systems, linear road, performance evaluation