Advertisement

Optimized Elastic Query Mesh for Cloud Data Streams

  • Fatma MohamedEmail author
  • Rasha M. Ismail
  • Nagwa L. Badr
  • M. F. Tolba
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9155)

Abstract

Many recent applications in several domains such as sensor networks, financial applications, network monitoring and click-streams generate continuous, unbounded, rapid, time varying datasets which are called data streams. In this paper we propose the optimized and elastic query mesh (OEQM) framework for data streams processing based on cloud computing to suit the changeable nature of data streams. OEQM processes the streams tuples over multiple query plans, each plan is suitable for a sub-set of data with the nearest properties and it provides elastic processing of data streams on the cloud environment. We also propose the Auto Scaling Cloud Query Mesh (AS-CQM) algorithm that supports streams processing with multiple plans and provides elastic scaling of the processing resources on demand. Our experimental results show that, the proposed solution OEQM reduces the cost for data streams processing on the cloud environment and efficiently exploits cloud resources.

Keywords

Data streams Cloud computing Elastic processing Continuous query optimization Query mesh 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Heinze, T., Pappalardo, V., Jerzak, Z., Fetzer, C.: Auto-Scaling Techniques for Elastic Data Stream Processing. In: 30th International Conference on Data Engineering Workshops, pp. 318–321. IEEE, Chicago (2014)Google Scholar
  2. 2.
    Mohamed, F., Ismail, R., Badr, N., Tolba, M.F.: Efficient Optimized Query Mesh for Data Streams. In: 9th International Conference on Computer Engineering & Systems (ICCES), pp. 157 –163. IEEE, Egypt (2014)Google Scholar
  3. 3.
    Chen, H.: Mining Top-K Frequent Patterns over Data Streams Sliding Window. Intelligent Information Systems 42, 111–131 (2014)Google Scholar
  4. 4.
    Ajwani, D., Ali, S., Katrinis, K., Li, C.H., Park, A.J., Morrison, J.P., Schenfeld, E.: Generating Synthetic Task Graphs for Simulating Stream Computing Systems. Parallel and Distributed Computing 73, 1362–1374 (2013)CrossRefGoogle Scholar
  5. 5.
    Anceaume, E., Busnel, Y.: A Distributed Information Divergence Estimation over Data Streams. IEEE Trans. on Parallel and Distributed Systems 25, 478–487 (2014)Google Scholar
  6. 6.
    Cao, J., Zhang, W., Tan, W.: Dynamic Control of Data Streaming and Processing In: A Virtualized Environment. IEEE Trans. on Automation Science and Engineering 9, 365–376 (2012)Google Scholar
  7. 7.
    Saleh, O., Gropengieβer, F., Betz, H., Mandarawi, W., Sattler, K.U.: Monitoring and Autoscaling IaaS Clouds: A Case for Complex Event Processing on Data Streams. In: 6th International Conference on Utility and Cloud Computing, pp. 387–392. IEEE Computer Society, Dresden (2013)Google Scholar
  8. 8.
    Nehme, R.V., Works, K., Lei, C., Rundensteiner, E.A., Bertino, E.: Multi-Route Query Processing and Optimization. Journal of Computer and System Sciences 79, 312–329 (2013)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Lei, C., Rundensteiner, E.A., Guttman, J.D.: Robust Distributed Stream Processing. In: 29th International Conference on Data Engineering, pp. 817–828. IEEE, Washington (2013)Google Scholar
  10. 10.
    Ding, L., Works, K., Rundensteiner, E.A.: Semantic Stream Query Optimization Exploiting Dynamic Metadata. In: 27th International Conference on Data Engineering, pp. 111–122. IEEE, Hannover (2011)Google Scholar
  11. 11.
    Lim, H., Babu, S.: Execution and Optimization of Continuous Queries with Cyclops. In: The 2013 SIGMOD International Conference on Management of Data, pp. 1069–1072. ACM, New York (2013)Google Scholar
  12. 12.
    Works, K., Rundensteiner, E.A., Agu, E.: Optimizing Adaptive Multi-Route Query Processing via time-partitioned indices. Journal of Computer and System Sciences 79, 330–348 (2013)Google Scholar
  13. 13.
    Dou, A., Lin, S., Kalogeraki, V., Gunopulos, D.: Supporting Historic Queries in Sensor Networks with Flash Storage. Information Systems 39, 217–232 (2014)CrossRefGoogle Scholar
  14. 14.
    Yin, B., Lin, Y., Yu, J., Luo, Q.: Energy-Efficient Filtering for Skyline Queries in Cluster-Based Sensor Networks. Computers and Electrical Engineering 40, 350–366 (2014)CrossRefGoogle Scholar
  15. 15.
    Zhang, Y., Cheng, R.: Probabilistic Filters: A Stream Protocol for Continuous Probabilistic Queries. Information Systems 38, 132–154 (2013)CrossRefGoogle Scholar
  16. 16.
    Qian, J., Li, Y., Wang, Y., Chen, H., Dong, Y.: An Embedded Co-processor for Accelerating Window Joins over Uncertain Data Streams. Microprocessors and Microsystems 36(6), 489–504 (2012)CrossRefGoogle Scholar
  17. 17.
    Ding, X., Lian, X., Chen, L., Jin, H.: Continuous Monitoring of Skylines over Uncertain Data Streams. Information Sciences 184, 196–214 (2012)CrossRefGoogle Scholar
  18. 18.
    Liu, Z., Wang, C., Wang, J.: Aggregate Nearest Neighbor Queries in Uncertain Graphs. World Wide Web 17, 161–188 (2014)zbMATHCrossRefGoogle Scholar
  19. 19.
    Fangzhou, Z., Guohui, L., Li, L., Xiaosong, Z., Cong, Z.: Probabilistic Nearest Neighbor Queries of Uncertain Data via Wireless Data Broadcast. Peer-to-Peer Networking and Applications 6, 363–379 (2013)CrossRefGoogle Scholar
  20. 20.
    Gulisano, V., Jimenez-Peris, R., Patino-Martinez, M., Soriente, C.: StreamCloud:An Elastic and Scalable Data Streaming System. IEEE Trans. on Parallel and Distributed Systems 23(12) (2012)Google Scholar
  21. 21.
    Cervino, J., Kalyvianaki, E., Salvachua, J., Pietzuch, P.: Adaptive Provisioning of Stream Processing Systems in the Cloud. In: 28th International Conference on Data Engineering Workshops, pp. 295–301. IEEE, Washington (2012)Google Scholar
  22. 22.
    Hu, R., Jiang, J., Liu, G., Wang, L.: Efficient Resources Provisioning Based on Load Forecasting in Cloud. The Scientific World Journal 2014, Article ID 10152, 14 pages (2014)Google Scholar
  23. 23.
    Kailasam, S., Gnanasambandam, N., Dharanipragada, J., Sharma, N.: Optimizing Ordered Throughput Using Autonomic Cloud Bursting Schedulers. IEEE Trans. on Software Engineering 39(11), 1564–1581 (2013)CrossRefGoogle Scholar
  24. 24.
    Castro Fernandez, R., Migliavacca, M., Kalyvianaki, E., Pietzuch, P.: Integrating Scale Out and Fault Tolerance in Stream Processing using Operator State Management. In: SIGMOD International Conference on Management of Data, pp.725–736. ACM, New York (2013)Google Scholar
  25. 25.
    Yogita, Y., Toshniwal, D.: Clustering Techniques for Streaming Data-a Survey. In: 3rd International in Advance Computing Conference, pp. 951–956. IEEE (2013)Google Scholar
  26. 26.
    Aggarwal, C.C.: A Survey of Stream Clustering Algorithms. In: Data Clustering: Algorithms and Applications, pp. 231–258 (2013)Google Scholar
  27. 27.
    Guo, T., Papaioannou, T.G., Aberer, K.: Efficient Indexing and Query Processing of Model-View Sensor Data in the Cloud. Big Data Research 1, 52–65 (2014)CrossRefGoogle Scholar
  28. 28.
  29. 29.
  30. 30.
    Kim, H.G.: A Structure for Sliding Window Equijoins in Data Stream Processing. In: 16th International Conference on Computational Science and Engineering, pp. 100–103. IEEE, Sydney (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Fatma Mohamed
    • 1
    Email author
  • Rasha M. Ismail
    • 1
  • Nagwa L. Badr
    • 1
  • M. F. Tolba
    • 1
  1. 1.Faculty of Computer and Information SciencesAin Shams UniversityCairoEgypt

Personalised recommendations