D. J. Abadi, Y. Ahmad, M. Balazinska, U. Cherniack, M. Hwang et al., The design of the borealis stream processing engine, CIDR 2005, Second Biennial Conference on Innovative Data Systems Research, pp.277-289, 2005.

D. J. Abadi, D. Carney, U. Cherniack, M. Convey, C. Lee et al., Aurora: a new model and architecture for data stream management, VLDB J, vol.12, issue.2, pp.120-139, 2003.

L. Aniello, R. Baldoni, L. Querzoni, S. Chakravarthy, S. D. Urban et al., Adaptive online scheduling in storm, The 7th ACM International Conference on Distributed Event-Based Systems, DEBS '13, pp.207-218, 2013.

A. Flink,

A. Storm,

A. Arasu, B. Babcock, S. Babu, J. Cieslewicz, M. Datar et al., Data Stream Management-Processing High-Speed Data Streams, Data-Centric Systems and Applications, pp.317-336, 2016.

A. Arasu, S. Babu, and J. Widom, The CQL continuous query language: semantic foundations and query execution, VLDB J, vol.15, issue.2, pp.121-142, 2006.

,

M. Balazinska, H. Balakrishnan, and M. Stonebraker, Load management and high availability in the medusa distributed stream processing system, Proceedings of the 2004 ACM SIGMOD international conference on Management of data, pp.929-930, 2004.

A. Biem, E. Bouillet, H. Feng, A. Ranganathan, A. Riabov et al., IBM infosphere streams for scalable, real-time, intelligent transportation services, Proceedings of the ACM SIGMOD International Conference on Management of Data, SIGMOD 2010, pp.1093-1104, 2010.

G. Box, Box and Jenkins: Time Series Analysis, Forecasting and Control, pp.161-215, 2013.

,

L. Carter and M. N. Wegman, Universal classes of hash functions, J. Comput. Syst. Sci, vol.18, issue.2, pp.143-154, 1979.

S. Chandrasekaran, O. Cooper, A. Deshpande, M. J. Franklin, J. M. Hellerstein et al., Telegraphcq: Continuous dataflow processing, Proceedings of the 2003 ACM SIGMOD International Conference on Management of Data, p.668, 2003.

, , 2003.

M. Cherniack, H. Balakrishnan, M. Balazinska, D. Carney, U. Xing et al., Scalable distributed stream processing, CIDR 2003, First Biennial Conference on Innovative Data Systems Research, 2003.

G. Cormode and S. Muthukrishnan, An improved data stream summary: the count-min sketch and its applications, J. Algorithms, vol.55, issue.1, pp.58-75, 2005.

R. Das, G. Tesauro, and W. E. Walsh, Model-based and model-free approaches to autonomic resource allocation, 2005.

J. Dean and S. Ghemawat, Mapreduce: Simplified data processing on large clusters, 6th Symposium on Operating System Design and Implementation (OSDI 2004), pp.137-150, 2004.

B. Gedik, Partitioning functions for stateful data parallelism in stream processing, VLDB J, vol.23, issue.4, pp.517-539, 2014.

B. Gedik, S. Schneider, M. Hirzel, and K. Wu, Elastic scaling for data stream processing, IEEE Trans. Parallel Distrib. Syst, vol.25, issue.6, pp.1447-1463, 2014.

L. Golab, S. Garg, M. T. Ozsu, E. Bertino, S. Christodoulakis et al., On indexing sliding windows over online data streams, Advances in Database TechnologyEDBT 2004, 9th International Conference on Extending Database Technology, Heraklion, vol.2992, pp.712-729, 2004.

G. Cloud-dataflow,

T. Heinze, V. Pappalardo, Z. Jerzak, and C. Fetzer, Auto-scaling techniques for elastic data stream processing, The 8th ACM International Conference on Distributed Event-Based Systems, DEBS '14, pp.318-321, 2014.

M. Hirzel, R. Soulé, S. Schneider, B. Gedik, and R. Grimm, A catalog of stream processing optimizations, ACM Comput. Surv, vol.46, issue.4, p.34, 2013.

J. Kang, J. F. Naughton, and S. Viglas, Evaluating window joins over unbounded streams, Proceedings of the 19th International Conference on Data Engineering, pp.341-352, 2003.

R. K. Kombi, N. Lumineau, and P. Lamarre, A preventive auto-parallelization approach for elastic stream processing, 37th IEEE International Conference on Distributed Computing Systems, pp.1532-1542, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01585096

A. Mukhopadhyay and R. R. Mazumdar, Analysis of randomized join-theshortest-queue (JSQ) schemes in large heterogeneous processor-sharing systems, IEEE Trans. Control of Network Systems, vol.3, issue.2, pp.116-126, 2016.

,

M. A. Nasir, G. D. Morales, D. García-soriano, N. Kourtellis, M. Serafini et al., The power of both choices: Practical load balancing for distributed stream processing engines, 31st IEEE International Conference on Data Engineering, pp.137-148, 2015.

L. Neumeyer, B. Robbins, A. Nair, A. Kesari, W. Fan et al., S4: distributed stream computing platform, The 10th IEEE International Conference on Data Mining Workshops, pp.170-177, 2010.

B. Peng, M. Hosseini, Z. Hong, R. Farivar, R. H. Campbell et al., R-storm: Resourceaware scheduling in storm, Proceedings of the 16th Annual Middleware Conference, pp.149-161, 2015.

N. Rivetti, E. Anceaume, Y. Busnel, L. Querzoni, and B. Sericola, Proactive Online Scheduling for Shuffle Grouping in Distributed Stream Processing Systems, Proceedings of the 17th ACM/IFIP/USENIX International Middleware Conference. Middleware, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01246701

N. Rivetti, Y. Busnel, L. Querzoni, A. Gal, M. Weidlich et al., Load-aware shedding in stream processing systems, Proceedings of the 10th ACM International Conference on Distributed and Eventbased Systems, DEBS '16, pp.61-68, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01413212

N. Rivetti, L. Querzoni, E. Anceaume, Y. Busnel, and B. Sericola, Efficient key grouping for near-optimal load balancing in stream processing systems, Proceedings of the 9th ACM International Conference on Distributed Event-Based Systems, DEBS '15, pp.80-91, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01194518

K. Sattler, F. Beier, G. Cormode, K. Yi, A. Deligiannakis et al., Towards elastic stream processing: Patterns and infrastructure, Proceedings of the First International Workshop on Big Dynamic Distributed Data, vol.1018, pp.49-54, 2013.

S. Schneider, H. Andrade, B. Gedik, A. Biem, and K. Wu, Elastic scaling of data parallel operators in stream processing, 23rd IEEE International Symposium on Parallel and Distributed Processing, pp.1-12, 2009.

A. Senderovich, M. Weidlich, A. Gal, A. Mandelbaum, M. Jarke et al., Queue mining-predicting delays in service processes, Advanced Information Systems Engineering-26th International Conference, CAiSE, vol.8484, pp.42-57, 2014.

M. Stonebraker, U. Zdonik, and S. B. , The 8 requirements of real-time stream processing, SIGMOD Record, vol.34, issue.4, pp.42-47, 2005.

,

M. Sullivan and A. Heybey, Tribeca: A system for managing large databases of network traffic, USENIX Annual Technical Conference, 1998.

D. Vengerov, A. C. Menck, M. Za¨?tza¨?t, and S. Chakkappen, Join size estimation subject to filter conditions, PVLDB, vol.8, issue.12, pp.1530-1541, 2015.

,

Y. Wu and K. Tan, Chronostream: Elastic stateful stream computation in the cloud, 2015 IEEE 31st International Conference on Data Engineering, pp.723-734, 2015.

J. Xu, Z. Chen, J. Tang, and S. Su, T-storm: Traffic-aware online scheduling in storm, IEEE 34th International Conference on Distributed Computing Systems, ICDCS, pp.535-544, 2014.

L. Xu, B. Peng, and I. Gupta, Stela: Enabling stream processing systems to scale-in and scale-out on-demand, 2016 IEEE International Conference on Cloud Engineering, vol.2, pp.22-31, 2016.

M. Zaharia, T. Das, H. Li, T. Hunter, S. Shenker et al., Discretized streams: A fault-tolerant model for scalable stream processing, CALIFORNIA UNIV BERKELEY DEPT OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCE, 2012.