A. Benoit, A. Dobrila, J. Nicod, and L. Philippe, Scheduling Linear Chain Streaming Applications on Heterogeneous Systems with Failures, Future Gener. Comput. Syst, vol.29, pp.1140-1151, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00926146

V. Cardellini, V. Grassi, F. L. Presti, and M. Nardelli, Distributed QoS-aware Scheduling in Storm, 9th ACM Int. Conf. on Dstb Event-Based Systems (DEBS '15), pp.344-347, 2015.

V. Cardellini, F. L. Presti, M. Nardelli, and G. R. Russo, Optimal operator deployment and replication for elastic distributed data stream processing, vol.30, 2018.

B. Cheng, A. Papageorgiou, and M. Bauer, Geelytics: Enabling On-Demand Edge Analytics over Scoped Data Sources, IEEE BigData, pp.101-108, 2016.

D. Foroni, C. Axenie, S. Bortoli, M. Hassan, R. Acker et al., Moira: A goal-oriented incremental machine learning approach to dynamic resource cost estimation in distributed stream processing systems, Proceedings of the International Workshop on Real-Time Business Intelligence and Analytics, vol.2, 2018.

S. Gelly and D. Silver, Monte-Carlo tree search and rapid action value estimation in computer Go, Artificial Intelligence, vol.175, pp.1856-1875, 2011.

K. Ha, P. Pillai, G. Lewis, S. Simanta, S. Clinch et al., The Impact of Mobile Multimedia Applications on Data Center Consolidation, IEEE Int. Conf. on Cloud Engineering (IC2E), pp.166-176, 2013.

Z. He, D. Zhang, J. Cao, X. Liu, X. Fan et al., Exploiting Real-Time Traffic Light Scheduling with Taxi Traces, 45th Int. Conf. on Parallel Processing, pp.314-323, 2016.

T. Heinze, L. Roediger, A. Meister, Y. Ji, Z. Jerzak et al., Online Parameter Optimization for Elastic Data Stream Processing, Proceedings of the Sixth ACM Symposium on Cloud Computing (SoCC '15), pp.276-287, 2015.

C. Hochreiner, M. Vogler, P. Waibel, and S. Dustdar, VISP: An Ecosystem for Elastic Data Stream Processing for the Internet of Things, 20th IEEE Int. Ent. Dstb Object Comp. Conf. 1-11, 2016.

W. Hu, Y. Gao, K. Ha, J. Wang, B. Amos et al., Quantifying the Impact of Edge Computing on Mobile Applications, 7th ACM SIGOPS Asia-Pacific Wksp on Systems, 2016.

T. Geetika, Y. Lakshmanan, R. Li, and . Strom, Placement Strategies for Internet-Scale Data Stream Systems, IEEE Internet Computing, vol.12, issue.6, pp.50-60, 2008.

Z. Liu, H. Zhang, B. Rao, and L. Wang, A Reinforcement Learning Based Resource Management Approach for Time-critical Workloads in Distributed Computing Environment, 2018 IEEE International Conference on Big Data (Big Data), pp.252-261, 2018.

L. Mai, N. Dao, and M. Park, Real-Time Task Assignment Approach Leveraging Reinforcement Learning with Evolution Strategies for Long-Term Latency Minimization in Fog Computing, Sensors, vol.18, p.2830, 2018.

L. Ni, J. Zhang, C. Jiang, C. Yan, and K. Yu, Resource Allocation Strategy in Fog Computing Based on Priced Timed Petri Nets, IEEE IoT Journal, pp.1-1, 2017.

F. Alexandru-iulian-orhean, I. Pop, and . Raicu, New scheduling approach using reinforcement learning for heterogeneous distributed systems, J. Parallel and Distrib. Comput, vol.117, pp.292-302, 2018.

J. Panerati, F. Sironi, M. Carminati, M. Maggio, G. Beltrame et al., On self-adaptive resource allocation through reinforcement learning, 2013 NASA/ESA Conference on Adaptive Hardware and Systems (AHS-2013, 2013.

B. Peng, M. Hosseini, Z. Hong, R. Farivar, and R. Campbell, R-Storm: Resource-Aware Scheduling in Storm, 16th Annual Middleware Conf. (Middleware '15), pp.149-161, 2015.

D. Perez, S. Mostaghim, S. Samothrakis, and S. M. Lucas, Multiobjective Monte Carlo Tree Search for Real-Time Games, IEEE Transactions on Computational Intelligence and AI in Games, vol.7, issue.4, pp.347-360, 2015.

O. Runsewe and N. Samaan, Cloud Resource Scaling for Big Data Streaming Applications Using A Layered Multi-dimensional Hidden Markov Model, Proc. of the 17th IEEE/ACM Int. Symposium on Cluster, Cloud and Grid Computing (CCGrid '17), pp.848-857, 2017.

G. Russo-russo, M. Nardelli, V. Cardellini, and F. L. Presti, Multi-Level Elasticity for Wide-Area Data Streaming Systems: A Reinforcement Learning Approach, Algorithms, vol.11, p.134, 2018.

H. P. Sajjad, K. Danniswara, A. Al-shishtawy, and V. Vlassov, SpanEdge: Towards Unifying Stream Processing over Central and Near-the-Edge Data Centers, IEEE/ACM Symp. on Edge Comp, pp.168-178, 2016.

A. Shukla, S. Chaturvedi, and Y. Simmhan, RIoTBench: An IoT benchmark for distributed stream processing systems, CCPE, vol.29, p.4257, 2017.

R. S. Sutton and A. G. Barto, Reinforcement Learning: An introduction, 2018.

M. Taneja and A. Davy, Resource aware placement of IoT application modules in Fog-Cloud Computing Paradigm, IFIP/IEEE Symp. on Integrated Net. and Service Mgmt (IM, pp.1222-1228, 2017.

R. Tolosana-calasanz, J. Á. Bañares, C. Pham, and O. F. Rana, Resource management for bursty streams on multi-tenancy cloud environments, Future Generation Computer Systems, vol.55, pp.444-459, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01906856

A. Veith, M. Dias-de-assuncao, and L. Lefevre, LatencyAware Placement of Data Stream Analytics on Edge Computing, 16th International Conference on Service Oriented Computing (ICSOC, pp.215-229, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01875936

D. Vengerov, A reinforcement learning approach to dynamic resource allocation, Engineering Applications of Artificial Intelligence, vol.20, pp.383-390, 2007.

T. Vodopivec, S. Samothrakis, and B. Ster, On Monte Carlo Tree Search and Reinforcement Learning, Journal of Artificial Intelligence Research, vol.60, pp.881-936, 2017.

W. Wang and M. Sebag, Multi-objective Monte-Carlo Tree Search, Proceedings of the Asian Conference on Machine Learning (Proceedings of Machine Learning Research, vol.25, pp.507-522, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00758379

K. P. Yoon, P. K. Yoon, C. L. Hwang, S. Inc-sage, and . Publications, Multiple Attribute Decision Making: An Introduction. Number nos. 102-104 in Multiple Attribute Decision Making: An Introduction, 1995.