H. C. Andrade, Fundamentals of Stream Processing: Application Design, Systems, and Analytics, 2014.

L. F. Bittencourt, Scheduling in distributed systems: A cloud computing perspective, Computer Science Review, vol.30, 2018.

P. Carbone, Apache Flink : Stream and batch processing in a single engine, Bulletin of the IEEE Computer Society Technical Committee on Data Engineering, vol.36, p.4, 2015.

V. Cardellini, Distributed QoS-aware scheduling in storm, Proc. DEBS'15, 2015.

V. Cardellini, Optimal Operator Replication and Placement for Distributed Stream Processing Systems, ACM SIGMETRICS Performance Evaluation Review, vol.44, p.4, 2017.

V. Cardellini, Decentralized self-adaptation for elastic Data Stream Processing, Future Generation Computer Systems, vol.87, 2018.

V. Cardellini, New Landscapes of the Data Stream Processing in the era of Fog Computing, Future Generation Computer Systems, 2019.

A. Veith, Latency-Aware Placement of Data Stream Analytics on Edge Computing, Proc. ICSOC, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01875936

T. , DROPLET: Distributed Operator Placement for IoT Applications Spanning Edge and Cloud Resources, Proc. IEEE CLOUD, 2018.

B. Gautam and A. Basava, Performance prediction of data streams on high-performance architecture, Human-centric Computing and Information Sciences, vol.9, issue.2, 2019.

M. Hirzel, A catalog of stream processing optimizations, Comput. Surveys, vol.46, p.4, 2014.

Y. Huang, Operator Placement with QoS Constraints for Distributed Stream Processing, Proc. CNSM, 2011.

J. Karimov, Benchmarking Distributed Stream Processing Engines, Proc. ICDE, 2018.

J. Kroß and H. Krcmar, Model-Based Performance Evaluation of Batch and Stream Applications for Big Data, Proc. MASCOTS, 2017.

T. Li, Performance Modeling and Predictive Scheduling for Distributed Stream Data Processing, IEEE Trans. on Big Data, vol.2, p.4, 2016.

T. Li, Model-Free Control for Distributed Stream Data Processing using Deep Reinforcement Learning, Proc. VLDB Endow, 2018.

X. Liu and R. Buyya, Performance-Oriented Deployment of Streaming Applications on Cloud, IEEE Trans. on Big Data, vol.5, p.1, 2019.

G. Mencagli, SpinStreams: a Static Optimization Tool for Data Stream Processing Applications, Proc. Middleware, 2018.

M. Nardelli, Efficient Operator Placement for Distributed Data Stream Processing Applications, IEEE Trans. on Parallel and Distributed Systems, 2019.

P. Pietzuch, Network-aware operator placement for stream processing systems, Proc. ICDE, 2006.

H. Röger and R. Mayer, A Comprehensive Survey on Parallelization and Elasticity in Stream Processing, Comput. Surveys, vol.52, 2019.

E. Saurez, Incremental deployment and migration of geo-distributed situation awareness applications in the fog, Proc. DEBS, 2016.

A. Shukla and Y. Simmhan, Model-driven scheduling for distributed stream processing systems, J. Parallel and Distrib. Comput, vol.117, 2018.

Q. To, A Survey of State Management in Big Data Processing Systems, The VLDB Journal, vol.27, 2018.

A. , Proc. SIGMOD, 2014.

V. Shivaram, Ernest: Efficient Performance Prediction for Large-Scale Advanced Analytics, Proc. NSDI, 2016.

K. Wang and M. M. Khan, Performance Prediction for Apache Spark Platform, Proc. HPCC-CSS-ICESS '15, 2015.

. Wikipedia, Levenberg-Marquardt algorithm, 2019.

. Wondernetwork, Global ping statistics, 2019.

M. Zaharia, Resilient Distributed Datasets: A Fault-Tolerant Abstraction for In-Memory Cluster Computing, Proc. NSDI, 2012.