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RECEP: selection-based reuse for distributed complex event processing

Authors:

Beate Ottenwälder,

Boris Koldehofe,

Kurt Rothermel,

Umakishore RamachandranAuthors Info & Claims

DEBS '14: Proceedings of the 8th ACM International Conference on Distributed Event-Based Systems

Pages 59 - 70

https://doi.org/10.1145/2611286.2611297

Published: 26 May 2014 Publication History

Abstract

An appealing use case of complex event processing (CEP) systems is for mobile users to react in real-time to events in their environment, e.g., to the occurrence of a dangerous situation such as an accident. Maintaining mobile CEP systems is highly resource intensive since in many cases events need to be detected in a consumer-centric manner to ensure low latency event detection and high quality of results. In this paper we propose the RECEP system to increase the scalability of mobile CEP systems. In the presence of mobile users with partially overlapping interest, the RECEP system offers methods to efficiently reuse computations and this way reduces the resource requirements of mobile CEP. Since reuse of computations happens with respect to well defined quality metrics, RECEP can be easily tailored to specific mobile applications and maximize the resource savings for their desired quality in terms of precision and recall of the processed events from the user's environment.

References

[1]

www.rita.dot.gov/bts/sites/rita.dot.gov.bts/files/publications/national_transportation_statistics/html/table_01_36.html. online, 2011. {online; accessed 2014-04-10}.

[2]

http://www.capgemini.com/resources/cars-online-1213. online, 2013. {online; accessed 2014-04-10}.

[3]

A. Assefa and F. Getahun. Multi-query Optimization for Semantic News Feed Query. In Proc. of Int. Conf. on Management of Emergent Digital EcoSystems, MEDES '12, pages 150--157. ACM, 2012.

Digital Library

[4]

M. Behrisch, L. Bieker, J. Erdmann, and D. Krajzewicz. SUMO - Simulation of Urban MObility: An Overview. In Proc. of 3rd Int. Conference on Advances in System Simulation (SIMUL), pages 63--68, Oct. 2011.

[5]

F. Bonomi, R. Milito, J. Zhu, and S. Addepalli. Fog Computing and Its Role in the Internet of Things. In Proc. of 1st MCC workshop on Mobile Cloud Computing, pages 13--16. ACM, 2012.

Digital Library

[6]

J. Chen, D. J. DeWitt, F. Tian, and Y. Wang. NiagaraCQ: A Scalable Continuous Query System for Internet Databases. In Proc. of 2000 ACM SIGMOD Int. Conf. on Management of Data, SIGMOD '00, pages 379--390. ACM, 2000.

Digital Library

[7]

T. H. Cormen, C. Stein, R. L. Rivest, and C. E. Leiserson. Introduction to Algorithms. McGraw-Hill Higher Education, 2nd edition, 2001.

Digital Library

[8]

G. Cugola and A. Margara. TESLA: A Formally Defined Event Specification Language. In Proc. of 4th ACM Int. Conf. on Distributed Event-Based Systems, DEBS '10, pages 50--61. ACM, 2010.

Digital Library

[9]

B. Gedik and L. Liu. MobiEyes: A Distributed Location Monitoring Service Using Moving Location Queries. IEEE Transactions on Mobile Computing, 5:1384--1402, 2006.

Digital Library

[10]

B. Glavic, K. Sheykh Esmaili, P. M. Fischer, and N. Tatbul. Ariadne: Managing Fine-grained Provenance on Data Streams. In Proc. of 7th ACM Int. Conf. on Distributed Event-based Systems, DEBS '13, pages 39--50. ACM, 2013.

Digital Library

[11]

A. M. Hendawi and M. F. Mokbel. Panda: A Predictive Spatio-Temporal Query Processor. In Proc. of 20th Int. Conf. on Advances in Geographic Information Systems, SIGSPATIAL '12, pages 13--22. ACM, 2012.

Digital Library

[12]

K. Hong, D. Lillethun, U. Ramachandran, B. Ottenwälder, and B. Koldehofe. Opportunistic Spatio-temporal Event Processing for Mobile Situation Awareness. In Proc. of 7th ACM Int. Conf. on Distributed Event-Based Systems, DEBS '13, pages 195--206. ACM, 2013.

Digital Library

[13]

M. Hong, M. Riedewald, C. Koch, J. Gehrke, and A. Demers. Rule-based Multi-query Optimization. In Proc. of 12th Int. Conf. on Extending Database Technology: Advances in Database Technology, EDBT '09, pages 120--131. ACM, 2009.

Digital Library

[14]

A. Ishii and T. Suzumura. Elastic Stream Computing with Clouds. In Proc. of 2011 IEEE Int. Conf. on Cloud Computing, CLOUD, pages 195--202, 2011.

Digital Library

[15]

D. S. Johnson. Approximation Algorithms for Combinatorial Problems. In Proc. of 5th annual ACM Symp. on Theory of Computing, STOC '73, pages 38--49. ACM, 1973.

Digital Library

[16]

G. G. Koch, B. Koldehofe, and K. Rothermel. Cordies: Expressive event correlation in distributed systems. In Proc. of the 4th ACM Int. Conf. on Distributed Event-Based Systems, DEBS '10, pages 26--37. ACM, 2010.

Digital Library

[17]

B. Koldehofe, R. Mayer, U. Ramachandran, K. Rothermel, and M. Völz. Rollback-Recovery without Checkpoints in Distributed Event Processing Systems. In Proc. of 7th ACM Int. Conf. on Distributed Event-Based Systems, DEBS '13, pages 27--38. ACM, 2013.

Digital Library

[18]

B. Koldehofe, B. Ottenwälder, K. Rothermel, and U. Ramachandran. Moving Range Queries in Distributed Complex Event Processing. In Proc. of 6th ACM Int. Conf. on Distributed Event-Based Systems, DEBS '12, pages 201--212. ACM, 2012.

Digital Library

[19]

S. Krishnamurthy, M. J. Franklin, J. M. Hellerstein, and G. Jacobson. The Case for Precision Sharing. In Proc. of 30th Int. Conf. on Very Large Data Bases, VLDB '04, pages 972--984. VLDB Endowment, 2004.

Digital Library

[20]

S. Krishnamurthy, C. Wu, and M. Franklin. On-the-fly Sharing for Streamed Aggregation. In Proc. of 2006 ACM SIGMOD Int. Conf. on Management of Data, SIGMOD '06, pages 623--634. ACM, 2006.

Digital Library

[21]

R. Lange, H. Weinschrott, L. Geiger, A. Blessing, F. Dürr, K. Rothermel, and H. Schütze. On a Generic Uncertainty Model for Position Information. In QuaCon, LNCS 5786, pages 76--87. Springer, 2009.

Digital Library

[22]

D. C. Luckham. The Power of Events: An Introduction to Complex Event Processing in Distributed Enterprise Systems. Addison-Wesley Longman Publishing Co., Inc., 2001.

Digital Library

[23]

S. Madden, M. Shah, J. M. Hellerstein, and V. Raman. Continuously Adaptive Continuous Queries over Streams. In Proc. of 2002 ACM SIGMOD Int. Conf. on Management of Data, SIGMOD '02, pages 49--60. ACM, 2002.

Digital Library

[24]

A. Meka and A. Singh. DIST: A Distributed Spatiotemporal Index Structure for Sensor Networks. In Proc. of 14th ACM Int. Conf. on Information and Knowledge Management, CIKM '05, pages 139--146. ACM, 2005.

Digital Library

[25]

B. Ottenwälder, B. Koldehofe, K. Rothermel, and U. Ramachandran. MigCEP: Operator Migration for Mobility Driven Distributed Complex Event Processing. In Proc. of 7th ACM Int. Conf. on Distributed Event-Based Systems, DEBS '13, pages 183--194. ACM, 2013.

Digital Library

[26]

P. Roy, S. Seshadri, S. Sudarshan, and S. Bhobe. Efficient and Extensible Algorithms for Multi Query Optimization. In Proc. of 2000 ACM SIGMOD Int. Conf. on Management of Data, SIGMOD '00, pages 249--260. ACM, 2000.

Digital Library

[27]

M. A. Tariq, B. Koldehofe, G. G. Koch, I. Khan, and K. Rothermel. Meeting subscriber-defined QoS constraints in publish/subscribe systems. Concurrency and Computation: Practice and Experience, 23(17):2140--2153, 2011.

Digital Library

[28]

A. Varga and R. Hornig. An Overview of the OMNeT++ Simulation Environment. In Proc. of the 1st Int. Conf. on Simulation Tools and Techniques for Communications, Networks and Systems & Workshops, Simutools '08, pages 1--10. ICST, 2008.

Digital Library

[29]

T. Verbelen, P. Simoens, F. De Turck, and B. Dhoedt. Leveraging Cloudlets for Immersive Collaborative Applications. Pervasive Computing, IEEE, 12(4):30--38, 2013.

Digital Library

[30]

S. Xiang, H. B. Lim, and K.-L. Tan. Impact of Multi-query Optimization in Sensor Networks. In Proc. of 3rd Workshop on Data Management for Sensor Networks: In Conjunction with VLDB 2006, DMSN '06, pages 7--12. ACM, 2006.

Digital Library

[31]

X. Xiong, M. Mokbel, and W. Aref. SEA-CNN: Scalable Processing of Continuous K-Nearest Neighbor Queries in Spatio-temporal Databases. In Proc. of 21st Int. Conf. on Data Engineering, ICDE 2005, pages 643--654, 2005.

Digital Library

[32]

Z. Xu and H.-A. Jacobsen. Expressive Location-Based Continuous Query Evaluation with Binary Decision Diagrams. In Proc. of 2009 IEEE Int. Conf. on Data Engineering, ICDE '09, pages 1155--1158. IEEE, 2009.

Digital Library

[33]

J. Zhang, G. Zhang, and L. Liu. GeoGrid: A Scalable Location Service Network. In Proc of 27th Int. Conf. on Distributed Computing Systems, ICDCS '07, page 60. IEEE, June 2007.

Digital Library

Cited By

Amadeo MCampolo CLia GMolinaro ARuggeri G(2023)In-network Placement of Reusable Computing Tasks in an SDN-based Network EdgeIEEE Transactions on Mobile Computing10.1109/TMC.2023.3237765(1-16)Online publication date: 2023
https://doi.org/10.1109/TMC.2023.3237765
Röger HMayer R(2019)A Comprehensive Survey on Parallelization and Elasticity in Stream ProcessingACM Computing Surveys10.1145/330384952:2(1-37)Online publication date: 30-Apr-2019
https://dl.acm.org/doi/10.1145/3303849
Lee JMtibaa AMastorakis S(2019)A Case for Compute Reuse in Future Edge Systems: An Empirical Study2019 IEEE Globecom Workshops (GC Wkshps)10.1109/GCWkshps45667.2019.9024587(1-6)Online publication date: Dec-2019
https://doi.org/10.1109/GCWkshps45667.2019.9024587
Show More Cited By

Index Terms

RECEP: selection-based reuse for distributed complex event processing

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Published In

cover image ACM Conferences

DEBS '14: Proceedings of the 8th ACM International Conference on Distributed Event-Based Systems

May 2014

371 pages

ISBN:9781450327374

DOI:10.1145/2611286

General Chairs:
Umesh Bellur
IIT Bombay, India
,
Ravi Kothari
IBM India Research Labs

Copyright © 2014 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 26 May 2014

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DEBS '14: The 8th ACM International Conference on Distributed Event-Based Systems

May 26 - 29, 2014

Mumbai, India

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DEBS '14 Paper Acceptance Rate 16 of 174 submissions, 9%;

Overall Acceptance Rate 145 of 583 submissions, 25%

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Cited By

Amadeo MCampolo CLia GMolinaro ARuggeri G(2023)In-network Placement of Reusable Computing Tasks in an SDN-based Network EdgeIEEE Transactions on Mobile Computing10.1109/TMC.2023.3237765(1-16)Online publication date: 2023
https://doi.org/10.1109/TMC.2023.3237765
Röger HMayer R(2019)A Comprehensive Survey on Parallelization and Elasticity in Stream ProcessingACM Computing Surveys10.1145/330384952:2(1-37)Online publication date: 30-Apr-2019
https://dl.acm.org/doi/10.1145/3303849
Lee JMtibaa AMastorakis S(2019)A Case for Compute Reuse in Future Edge Systems: An Empirical Study2019 IEEE Globecom Workshops (GC Wkshps)10.1109/GCWkshps45667.2019.9024587(1-6)Online publication date: Dec-2019
https://doi.org/10.1109/GCWkshps45667.2019.9024587
Aggarwal SKumar N(2019)Fog Computing for 5G-Enabled Tactile Internet: Research Issues, Challenges, and Future Research DirectionsMobile Networks and Applications10.1007/s11036-019-01430-428:2(690-717)Online publication date: 20-Nov-2019
https://doi.org/10.1007/s11036-019-01430-4
Bok KKim DYoo J(2018)Complex Event Processing for Sensor Stream DataSensors10.3390/s1809308418:9(3084)Online publication date: 13-Sep-2018
https://doi.org/10.3390/s18093084
Anawar MWang SAzam Zia MJadoon AAkram URaza S(2018)Fog ComputingWireless Communications & Mobile Computing10.1155/2018/71571922018Online publication date: 7-May-2018
https://dl.acm.org/doi/10.1155/2018/7157192
ZHANG WFlores HHUI P(2018)Towards collaborative multi-device computing2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)10.1109/PERCOMW.2018.8480262(22-27)Online publication date: Mar-2018
https://doi.org/10.1109/PERCOMW.2018.8480262
Bruschi RDavoli FLombardo CSanchez O(2018)Evaluating the Impact of Micro-Data Center (μDC) Placement in an Urban Environment2018 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN)10.1109/NFV-SDN.2018.8725627(1-7)Online publication date: Nov-2018
https://doi.org/10.1109/NFV-SDN.2018.8725627
He SCheng BHuang YDuan LChen J(2017)Proactive Personalized Services in Large-Scale IoT-Based Healthcare Application2017 IEEE International Conference on Web Services (ICWS)10.1109/ICWS.2017.96(808-813)Online publication date: Jun-2017
https://doi.org/10.1109/ICWS.2017.96
Starks FGoebel VKristiansen SPlagemann T(2017)Mobile Distributed Complex Event Processing—Ubi Sumus? Quo Vadimus?Mobile Big Data10.1007/978-3-319-67925-9_7(147-180)Online publication date: 1-Nov-2017
https://doi.org/10.1007/978-3-319-67925-9_7
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