.. .. Discussion, , vol.87

. .. Future-directions, , p.90

. .. Dsp-systems, 90 6.2.5 DSP System, and Failure and Energy Consumption Models, p.91

. .. Scalability, 91 the challenge of (re)configuring Data Stream Processing (DSP) applications on infrastructure combining cloud and edge computing resources. We focused on mechanisms that can improve the application performance. Solutions for DSP application (re)configuration need to meet QoS requirements such as end-to-end application latency, monetary cost, WAN traffic, reconfiguration overhead, among others. We investigated existing work on DSP elasticity, and (re)configuration, and enumerated several characteristics of existing systems such as their architectural views, data management and structure

, ? An extensive literature on DSP application elasticity, and (re)configuration on the cloud

, Some efforts on modelling the (re)configuration of DSP applications; and ? Attempts to improve the execution of DSP applications by exploring the edge computing. The investigation also showed a lack of mechanisms that build on these efforts, that enable placing DSP applications on highly distributed infrastructure, and that meet either a single or multiple Quality of Service (QoS) requirements simultaneously. These lessons led to a proposed set of mechanisms for (re)configuring DSP applications on highly distributed infrastructure

Y. Ahmad and U. Çetintemel, Network-aware Query Processing for Stream-based Applications, 13th International Conference on Very Large Data Bases, vol.30, pp.456-467, 2004.

T. Akidau, A. Balikov, K. Bekiro?lu, S. Chernyak, J. Haberman et al., MillWheel: Faulttolerant Stream Processing at Internet Scale, Proceedings of the VLDB Endowment, vol.6, pp.1033-1044, 2013.

S. T. Allen, M. Jankowski, and P. Pathirana, Storm Applied: Strategies for Real-time Event Processing, 2015.

A. Cloudwatch, , 2015.

A. Service, , 2015.

A. Kinesis-firehose, , 2015.

L. Amini, H. Andrade, R. Bhagwan, F. Eskesen, R. King et al., SPC: A Distributed, Scalable Platform for Data Mining, 4th International Workshop on Data Mining Standards, Services and Platforms. DMSSP '06, pp.27-37, 2006.

L. Aniello, R. Baldoni, and L. Querzoni, Adaptive Online Scheduling in Storm, Proceedings of the 7th ACM International Conference on Distributed Eventbased Systems. DEBS '13, pp.207-218, 2013.

A. Activemq, , 2016.

A. Beam, , 2016.

A. Edgent, , 2017.

A. Flink, , 2015.

, Apache Flink -Iterative Graph Processing, 2017.

A. Kafka, , 2016.

A. Nifi, , 2019.

A. Thrift, , 2016.

, APPENDIX . BIBLIOGRAPHY

A. Zookeeper, , 2016.

M. Armbrust, A. Fox, R. Griffith, A. D. Joseph, R. H. Katz et al., Above the Clouds: A Berkeley View of Cloud Computing. Technical report UCB/EECS-2009-28, 2009.

M. Dias-de-assuncao, R. N. Calheiros, S. Bianchi, A. S. Marco, R. Netto et al., Big Data Computing and Clouds: Trends and Future Directions, In: Journal of Parallel and Distributed Computing, pp.743-7315, 2015.

M. Dias-de-assunção, A. Da, S. Veith, and R. Buyya, Distributed data stream processing and edge computing: A survey on resource elasticity and future directions, In: Journal of Net. and Computer Applications, vol.103, pp.1084-8045, 2018.

L. Atzori, A. Iera, and G. Morabito, The Internet of Things: A survey, In: Computer Net, vol.54, pp.2787-2805, 2010.

G. S. Aujla, N. Kumar, A. Y. Zomaya, and R. Ranjan, Optimal Decision Making for Big Data Processing at Edge-Cloud Environment: An SDN Perspective, IEEE Transactions on Industrial Informatics, vol.14, issue.2, pp.1551-3203, 2018.

, AWS IoT Core Pricing

, Azure IoT Hub, 2016.

, Azure Stream Analytics, 2015.

B. Babcock, S. Babu, M. Datar, R. Motwani, and J. Widom, Models and Issues in Data Stream Systems, 21st ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems. PODS '02, pp.1-16, 2002.

A. Benoit, A. Dobrila, J. Nicod, and L. Philippe, Scheduling Linear Chain Streaming Applications on Heterogeneous Systems with Failures, In: Future Gener. Comput. Syst, vol.29, issue.5, pp.167-739, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00926146

L. Bittencourt, R. Immich, R. Sakellariou, N. Fonseca, E. Madeira et al., The Internet of Things, Fog and Cloud continuum: Integration and challenges, In: Internet of Things, vol.3, issue.4, pp.2542-6605, 2018.

D. Borthakur, J. Gray, . Joydeep-sen, K. Sarma, N. Muthukkaruppan et al., Apache Hadoop Goes Realtime at Facebook, Proceedings of the ACM SIGMOD International Conference on Management of Data, pp.1071-1080, 2011.

O. Boykin, S. Ritchie, O. Ian, J. Connell, and . Lin, Summingbird: A Framework for Integrating Batch and Online MapReduce Computations, Proceedings of the VLDB Endowment, vol.7, pp.2150-8097, 2014.

B. Brehmer, The Dynamic OODA Loop : Amalgamating Boyd ' s OODA Loop and the Cybernetic Approach to Command and Control ASSESSMENT , TOOLS AND METRICS, 2005.

T. Buddhika and S. Pallickara, NEPTUNE: Real Time Stream Processing for Internet of Things and Sensing Environments, IEEE Int. Parallel and Distributed Proc. Symp, pp.1143-1152, 2016.

J. Byrne, S. Svorobej, A. Gourinovitch, D. M. Elango, P. Liston et al., RECAP simulator: Simulation of cloud/edge/fog computing scenarios, 2017 Winter Simulation Conference (WSC), pp.4568-4569, 2017.

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

V. Cardellini, V. Grassi, F. L. Presti, and M. Nardelli, Optimal Operator Placement for Distributed Stream Processing Applications, 10th ACM International Conference on Distributed and Event-based Systems. DEBS '16, pp.978-979, 2016.

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

M. Centenaro, L. Vangelista, A. Zanella, and M. Zorzi, Long-range communications in unlicensed bands: the rising stars in the IoT and smart city scenarios, IEEE Wireless Communications, vol.23, pp.1536-1284, 2016.

C. Cloud,

S. C. Quarks, W. , and S. Analytics, Saving the World, One Smart Sprinkler at a Time. Bluemix Blog, 2016.

W. Chen, I. Paik, and Z. Li, Cost-Aware Streaming Workflow Allocation on Geo-Distributed Data Centers, IEEE Transactions on Computers, vol.66, pp.256-271, 2017.

Y. Chen, S. Alspaugh, D. Borthakur, and R. Katz, Energy Efficiency for Large-Scale MapReduce Workloads with Significant Interactive Analysis, 7th ACM European Conference on Computer Systems, pp.978-979, 2012.

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

, APPENDIX . BIBLIOGRAPHY

S. Clifford and Q. Hardy, Attention, Shoppers: Store Is Tracking Your Cell, New York Times, 2013.

F. Dabek, R. Cox, F. Kaashoek, and R. Morris, Vivaldi: A Decentralized Network Coordinate System, In: SIGCOMM Comput. Commun. Rev, vol.34, issue.4, pp.146-4833, 2004.

F. Daniel and L. Guida, A Service-Oriented Perspective on Blockchain Smart Contracts, IEEE Internet Computing, vol.23, pp.1089-7801, 2019.

J. Dean and S. Ghemawat, MapReduce: Simplified Data Processing on Large Clusters, Communications of the ACM, vol.51, issue.1, 2008.

. Distributedlog, , 2016.

R. Eidenbenz and T. Locher, Task allocation for distributed stream processing, IEEE INFOCOM 2016, pp.1-9, 2016.

M. S. Elbamby, M. Bennis, and W. Saad, Proactive edge computing in latency-constrained fog networks, European Conf. on Net. and Comm, pp.1-6, 2017.

B. Ellis, Real-time analytics: Techniques to Analyze and Visualize Streaming Data, 2014.

R. Castro-fernandez, M. Migliavacca, E. Kalyvianaki, and P. Pietzuch, Integrating Scale out and Fault Tolerance in Stream Processing Using Operator State Management, ACM SIGMOD International Conference on Management of Data. SIGMOD '13, pp.725-736, 2013.

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, p.2, 2018.

K. Gai, M. Qiu, and H. Zhao, Cost-Aware Multimedia Data Allocation for Heterogeneous Memory Using Genetic Algorithm in Cloud Computing, IEEE Transactions on Cloud Computing PP, vol.99, pp.2168-7161, 2016.

K. Gai, M. Qiu, H. Zhao, L. Tao, and Z. Zong, Dynamic Energy-Aware Cloudlet-Based Mobile Cloud Computing Model for Green Computing, In: Journal of Network and Computer Applications 59.Supplement C, pp.1084-8045, 2016.

J. Gedeon, M. Stein, J. Krisztinkovics, P. Felka, K. Keller et al., From Cell Towers to Smart Street Lamps: Placing Cloudlets on Existing Urban Infrastructures, IEEE/ACM Symposium on Edge Computing (SEC), 2018.

. Oct, , pp.187-202, 2018.

B. Gedik, H. G. Özsema, and Ö. Öztürk, Pipelined Fission for Stream Programs with Dynamic Selectivity and Partitioned State, In: Journal of Parallel and Distributed Computing, vol.96, 2016.

B. Gedik, S. Schneider, M. Hirzel, and K. Wu, Elastic Scaling for Data Stream Processing, IEEE Transactions on Parallel and Distributed Systems, vol.25, issue.6, pp.1447-1463, 2014.

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.

R. Ghosh, S. Prakash-reddy-komma, and Y. Simmhan, Adaptive Energyaware Scheduling of Dynamic Event Analytics Across Edge and Cloud Resources, Proceedings of the 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing. CCGrid '18, pp.72-82, 2018.

L. Golab and M. Özsu, Issues in Data Stream Management, SIGMOD Record, vol.32, pp.163-5808, 2003.

G. Cloud-dataflow, , 2015.

G. Cloud-storage, , 2015.

L. Gu, D. Zeng, S. Guo, Y. Xiang, and J. Hu, A General Communication Cost Optimization Framework for Big Data Stream Processing in Geo-Distributed Data Centers, IEEE Transactions on Computers, vol.65, issue.1, pp.19-29, 2016.

V. Gulisano, R. Jiménez-peris, M. Patiño-martínez, C. Soriente, and P. Valduriez, StreamCloud: An Elastic and Scalable Data Streaming System, IEEE Transactions on Parallel and Distributed Systems, vol.23, pp.2351-2365, 2012.
URL : https://hal.archives-ouvertes.fr/lirmm-00748992

H. Gupta, A. Vahid-dastjerdi, S. K. Ghosh, and R. Buyya, iFogSim: A toolkit for modeling and simulation of resource management techniques in the Internet of Things, Edge and Fog computing environments, In: Software: Practice and Experience, vol.47, pp.1275-1296, 2017.

D. Gyllstrom, E. Wu, H. Chae, Y. Diao, P. Stahlberg et al., SASE: Complex Event Processing over Streams (Demo), Third Biennial Conference on Innovative Data Systems Research, pp.407-411, 2007.

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.

, APPENDIX . BIBLIOGRAPHY

K. Ha, Z. Chen, W. Hu, W. Richter, P. Pillai et al., Towards Wearable Cognitive Assistance, 12th Annual International Conference on Mobile Systems, Applications, and Services. MobiSys '14. Bretton Woods, pp.68-81, 2014.

J. Han, E. Haihong, G. Le, and J. Du, Survey on NoSQL Database, 6th International Conference on Pervasive Computing and Applications, pp.363-366, 2011.

B. He, M. Yang, Z. Guo, R. Chen, B. Su et al., Comet: Batched Stream Processing for Data Intensive Distributed Computing, 1st ACM Symposium on Cloud Computing. SoCC '10, pp.63-74, 2010.

S. Heidari, Y. Simmhan, R. N. Calheiros, and R. Buyya, Scalable Graph Processing Frameworks: A Taxonomy and Open Challenges, In: ACM Comput. Surv, vol.51, issue.3, 2018.

T. Heinze, L. Aniello, L. Querzoni, and Z. Jerzak, Cloudbased Data Stream Processing, Proceedings of the 8th ACM International Conference on Distributed Event-Based Systems. DEBS '14, pp.238-245, 2014.

T. Heinze, Z. Jerzak, G. Hackenbroich, and C. Fetzer, Latencyaware Elastic Scaling for Distributed Data Stream Processing Systems, 8th ACM International Conference on Distributed Event-Based Systems. DEBS '14, pp.13-22, 2014.

N. Hidalgo, D. Wladdimiro, and E. Rosas, Self-Adaptive Processing Graph with Operator Fission for Elastic Stream Processing, In: Journal of Systems and Software, pp.164-1212, 2016.

B. Hindman, A. Konwinski, M. Zaharia, A. Ghodsi, A. D. Joseph et al., Mesos: A Platform for Fine-Grained Resource Sharing in the Data Center, In: NSDI, vol.11, pp.22-22, 2011.

M. Hirzel, S. Schneider, and B. Gedik, SPL: An Extensible Language for Distributed Stream Processing, In: ACM Transactions on Programming Languages and Systems, vol.39, issue.1, 2017.

M. Hirzel, R. Soulé, S. Schneider, B. Gedik, and R. Grimm, A Catalog of Stream Processing Optimizations, In: ACM Computing Surveys, vol.46, pp.360-0300, 2014.

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. Enterprise Distributed Object Computing Conf, pp.1-11, 2016.

L. Hu, K. Schwan, H. Amur, and X. Chen, ELF: Efficient Lightweight Fast Stream Processing at Scale, USENIX Annual Technical Conference. USENIX Association, pp.25-36, 2014.

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. APSys '16. Hong Kong, vol.5, 2016.

Y. Hu, M. Patel, D. Sabella, N. Sprecher, and V. Young, Mobile Edge Computing: A Key Technology Towards 5G. Whitepaper ETSI White Paper No, European Telecommunications Standards Institute (ETSI), 2015.

N. Kaur, K. Sandeep, and . Sood, Efficient Resource Management System Based on 4Vs of Big Data Streams, In: Big Data Research, vol.9, pp.2214-5796, 2017.

. Kestrel, , 2016.

D. Kreutz, M. V. Fernando, P. E. Ramos, C. E. Verissimo, S. Rothenberg et al., Software-Defined Networking: A Comprehensive Survey, Proceedings of the IEEE 103, pp.18-9219, 2015.

B. Krishnamurthy, S. Sen, Y. Zhang, and Y. Chen, Sketch-based Change Detection: Methods, Evaluation, and Applications, 3rd ACM SIGCOMM Conference on Internet Measurement. IMC '03, pp.234-247, 2003.

S. Kulkarni, N. Bhagat, M. Fu, V. Kedigehalli, C. Kellogg et al., Twitter Heron: Stream Processing at Scale, ACM SIGMOD International Conference on Management of Data. SIGMOD '15, pp.978-979, 2015.

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.

T. Li, Z. Xu, J. Tang, and Y. Wang, Model-free Control for Distributed Stream Data Processing Using Deep Reinforcement Learning, Proc. VLDB Endow. 11.6 (Feb. 2018), pp.705-718

X. Liu, A. Vahid-dastjerdi, and R. Buyya, In: ed. by Rajkumar Buyya and Amir Vahid Dastjerdi, Stream Processing in IoT: Foundations, State-ofthe-art, and Future Directions, 2016.

B. Lohrmann, P. Janacik, and O. Kao, Elastic Stream Processing with Latency Guarantees, 35th IEEE International Conference on Distributed Computing Systems (ICDCS), pp.399-410, 2015.

F. Lombardi, L. Aniello, S. Bonomi, and L. Querzoni, Elastic Symbiotic Scaling of Operators and Resources in Stream Processing Systems, IEEE Transactions on Parallel and Distributed Systems, vol.29, issue.3, pp.1045-9219, 2018.

, APPENDIX . BIBLIOGRAPHY

T. Lorido-botran, J. Miguel-alonso, and J. A. Lozano, A Review of Auto-scaling Techniques for Elastic Applications in Cloud Environments, In: Journal of Grid Computing, vol.12, pp.1572-9184, 2014.

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, In: Sensors, vol.18, p.2830, 2018.

F. Mehdipour, B. Javadi, and A. Mahanti, FOG-Engine: Towards Big Data Analytics in the Fog, IEEE 14th Intl Conf on Dependable, Autonomic and Secure Computing, 14th Int. Conf on Pervasive Intelligence and Computing, 2nd Int. Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress, pp.640-646, 2016.

G. Mencagli, P. Dazzi, and N. Tonci, SpinStreams: A Static Optimization Tool for Data Stream Processing Applications, Proceedings of the 19th International Middleware Conference. Middleware '18, pp.66-79, 2018.

R. Van-der-meulen, Gartner Says 8.4 Billion Connected "Things" Will Be in Use in 2017, 2017.

, Microsoft Azure IoT Hub Pricing

J. Morales, E. Rosas, and N. Hidalgo, Symbiosis: Sharing mobile resources for stream processing, 2014 IEEE Symposium on Computers and Communications (ISCC)

, , pp.1-6, 2014.

Y. Nan, W. Li, W. Bao, F. C. Delicato, P. F. Pires et al., A dynamic tradeoff data processing framework for delay-sensitive applications in Cloud of Things systems, In: Journal of Parallel and Distributed Computing, vol.112, pp.743-7315, 2018.

A. S. Marco, C. Netto, R. Cardonha, M. Cunha, and . Dias-de-assuncao, Evaluating Auto-scaling Strategies for Cloud Computing Environments, 22nd IEEE International. Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems, pp.187-196, 2014.

L. Neumeyer, B. Robbins, A. Nair, and A. Kesari, S4: Distributed Stream Computing Platform, IEEE International Conference on Data Mining Workshops (ICDMW), pp.170-177, 2010.

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.2327-4662, 2017.

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

B. Ottenwälder, B. Koldehofe, K. Rothermel, and U. Ramachandran, MigCEP: Operator Migration for Mobility Driven Distributed Complex Event Processing, 7th ACM International Conference on Distributed Event-based Systems. DEBS '13, pp.183-194, 2013.

C. Pahl and B. Lee, Containers and Clusters for Edge Cloud Architectures -A Technology Review, 3rd International Conference on Future Internet of Things and Cloud, pp.379-386, 2015.

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.978-979, 2015.

P. Pietzuch, J. Ledlie, J. Shneidman, M. Roussopoulos, M. Welsh et al., Network-Aware Operator Placement for Stream-Processing Systems, 22nd International Conference on Data Engineering (ICDE'06), pp.49-49, 2006.

F. Pisani, J. Rech-brunetta, V. Martins-do, R. , and E. Borin, Beyond the Fog: Bringing Cross-Platform Code Execution to Constrained IoT Devices, 29th International Symposium on Computer Architecture and High Performance Computing, pp.17-24, 2017.

P. Buffers, , 2016.

Z. Qian, Y. He, C. Su, Z. Wu, H. Zhu et al., TimeStream: Reliable Stream Computation in the Cloud, 8th ACM European Conference on Computer Systems. EuroSys '13, pp.1-14, 2013.

. Rabbitmq, , 2016.

E. G. Renart, D. Balouek, -. , and M. Parashar, Edge Based Data-Driven Pipelines, 2018.

E. G. Renart, D. Balouek-thomert, and M. Parashar, Edge Based Data-Driven Pipelines, 2018.

L. Rettig, M. Khayati, P. Cudré-mauroux, and M. Piórkowski, Online Anomaly Detection Over Big Data Streams, IEEE International Conference on Big Data, pp.1113-1122, 2015.

H. Roh, C. Jung, K. Kim, S. Pack, and W. Lee, Joint flow and virtual machine placement in hybrid cloud data centers, In: Journal of Network and Computer Applications, vol.85, pp.1084-8045, 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, In: Algorithms, vol.11, p.134, 2018.

B. Saha, H. Shah, S. Seth, G. Vijayaraghavan, A. Murthy et al., Apache Tez: A Unifying Framework for Modeling and Building Data Processing Applications, ACM SIGMOD International Conference on Management of Data. SIGMOD '15, pp.1357-1369, 2015.

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.

M. A. Salahuddin, A. Al-fuqaha, and M. Guizani, Reinforcement learning for resource provisioning in the vehicular cloud, IEEE Wireless Communications, vol.23, pp.1536-1284, 2016.

S. Sarkar, S. Chatterjee, and S. Misra, Assessment of the Suitability of Fog Computing in the Context of Internet of Things, IEEE Transactions on Cloud Computing PP, pp.2168-7161, 2015.

K. Sattler and F. Beier, Towards Elastic Stream Processing: Patterns and Infrastructure, 1st International Workshop on Big Dynamic Distributed Data (BD3)

R. Del and G. , , pp.49-54, 2013.

M. Satyanarayanan, In: Edge Computing: Vision and Challenges, 2017.

B. Satzger, W. Hummer, P. Leitner, and S. Dustdar, Esc: Towards an Elastic Stream Computing Platform for the Cloud, IEEE International Conference on Cloud Computing (CLOUD), pp.348-355, 2011.

. Sensorbee, , 2019.

A. Mehul, J. M. Shah, S. Hellerstein, M. J. Chandrasekaran, and . Franklin, Flux: An Adaptive Partitioning Operator for Continuous Query Systems, 19th International Conference on Data Engineering, pp.25-36, 2003.

Z. Shen, V. Kumaran, M. J. Franklin, S. Krishnamurthy, A. Bhat et al., CSA: Streaming Engine for Internet of Things, In: IEEE Data Engineering Bulletin, vol.38, pp.39-50, 2015.

A. Shukla and Y. Simmhan, Toward Reliable and Rapid Elasticity for Streaming Dataflows on Clouds, IEEE 38th International Conference on Distributed Computing Systems (ICDCS), pp.1096-1106, 2018.

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

A. Da, S. Veith, M. Dias-de-assunção, and L. Lefèvre, Latency-Aware Placement of Data Stream Analytics on Edge Computing, pp.215-229, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01875936

H. Sun, P. Stolf, J. Pierson, and G. D. Costa, Multi-objective Scheduling for Heterogeneous Server Systems with Machine Placement, 14th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, pp.334-343, 2014.

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

S. System, , 2008.

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 Management (IM), pp.1222-1228, 2017.

Y. Tang and B. Gedik, Autopipelining for Data Stream Processing, IEEE Transactions on Parallel and Distributed Systems, vol.24, pp.1045-9219, 2013.

A. Toshniwal, S. Taneja, A. Shukla, K. Ramasamy, J. M. Patel et al., Nikunj Bhagat, Sailesh Mittal, and Dmitriy Ryaboy, ACM SIGMOD International Conference on Management of Data. SIGMOD '14, pp.147-156, 2014.

R. Tudoran, A. Costan, O. Nano, I. Santos, H. Soncu et al., JetStream: Enabling high throughput live event streaming on multi-site clouds, Future Generation Computer Systems, vol.54, pp.274-291, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01239124

N. Tziritas, T. Loukopoulos, S. U. Khan, C. Z. Xu, and A. Y. Zomaya, On Improving Constrained Single and Group Operator Placement Using Evictions in Big Data Environments, IEEE Transactions on Services Computing, vol.9, issue.5, pp.1939-1374, 2016.

D. Stratis, J. F. Viglas, and . Naughton, Rate-based Query Optimization for Streaming Information Sources, ACM SIGMOD International Conference on Management of Data. SIGMOD '02, pp.37-48, 2002.

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

E. Wu, Y. Diao, and S. Rizvi, High-performance Complex Event Processing over Streams, ACM SIGMOD International Conference on Management of Data. SIGMOD '06, pp.407-418, 2006.

Y. Wu and K. L. Tan, ChronoStream: Elastic stateful stream computation in the cloud, IEEE 31st Int. Conf. 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.

, APPENDIX . BIBLIOGRAPHY

L. Xu, B. Peng, and I. Gupta, Stela: Enabling Stream Processing Systems to Scale-in and Scale-out On-demand, IEEE International Conference on Cloud Engineering, pp.22-31, 2016.

R. Xu, Y. Seyed, Y. Nikouei, E. Chen, A. Blasch et al., BlendMAS: A BLockchain-ENabled Decentralized Microservices Architecture for Smart Public Safety, 2019.

J. Yang, Z. Lu, and J. Wu, Smart-toy-edge-computing-oriented data exchange based on blockchain, In: Journal of Systems Architecture, vol.87, pp.1383-7621, 2018.

Y. Yang, S. Zhao, W. Zhang, Y. Chen, X. Luo et al., DEBTS: Delay Energy Balanced Task Scheduling in Homogeneous Fog Networks, IEEE Internet of Things Journal, vol.5, issue.3, pp.2327-4662, 2018.

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

A. Yousefpour, G. Ishigaki, and J. P. Jue, Fog Computing: Towards Minimizing Delay in the Internet of Things, 2017 IEEE International Conference on Edge Computing (EDGE), pp.17-24, 2017.

J. Zacho, Unlocking Game-Changing Wireless Capabilities: Cisco and SITA help Copenhagen Airport Develop New Services for Transforming the Passenger Experience. Customer Case Study, 2012.

M. Zaharia, M. Chowdhury, T. Das, A. Dave, J. Ma et al., Resilient Distributed Datasets: A Fault-tolerant Abstraction for In-memory Cluster Computing, Proc. of the 9th USENIX Conf. on Networked Systems Design and Implementation. NSDI'12, pp.2-2, 2012.

M. Zaharia, T. Das, H. Li, T. Hunter, S. Shenker et al., Discretized Streams: Fault-tolerant Streaming Computation at Scale, 24th ACM Symposium on Operating Systems Principles. SOSP '13, pp.423-438, 2013.

X. Zhao, S. Garg, C. Queiroz, and R. Buyya, Software Architecture for Big Data and the Cloud, Taxonomy and Survey of Stream Processing Systems, 2017.

J. Zheng, X. Dong, T. Zhang, J. Chen, W. Tong et al., MicrothingsChain: Edge Computing and Decentralized IoT Architecture Based on Blockchain for Cross-Domain Data Shareing, 2018 International Conference on Networking and Network Applications, pp.350-355, 2018.

Y. Zhou, C. Beng, K. Ooi, J. Tan, and . Wu, Efficient Dynamic Operator Placement in a Locally Distributed Continuous Query System, On the Move to Meaningful Internet Systems, pp.54-71, 2006.