E. Alpaydin, Introduction to Machine Learning, 2014.

E. Angelou, N. Papailiou, I. Konstantinou, D. Tsoumakos, and N. Koziris, Automatic scaling of selective sparql joins using the tiramola system, Proceedings of the 4th International Workshop on Semantic Web Information Management, p.1, 2012.

M. Armbrust, R. S. Xin, and C. Lian, Spark sql: relational data processing in spark, Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, ACM, pp.1383-1394, 2015.

E. Barrett, E. Howley, and J. Duggan, Applying reinforcement learning towards automating resource allocation and application scalability in the cloud, Concurrency and Computation: Practice and Experience, vol.25, issue.12, pp.1656-1674, 2013.

T. C. Chieu, A. Mohindra, and A. A. Karve, Scalability and performance of web applications in a compute cloud, 2011 IEEE 8th International Conference on, pp.317-323, 2011.

J. Dean and S. Ghemawat, Mapreduce: simplified data processing on large clusters, Communications of the ACM, vol.51, issue.1, pp.107-113, 2008.

J. Dean and S. Ghemawat, Mapreduce: a flexible data processing tool, Communications of the ACM, vol.53, issue.1, pp.72-77, 2010.

X. Dutreilh, S. Kirgizov, O. Melekhova, J. Malenfant, N. Rivierre et al., Using reinforcement learning for autonomic resource allocation in clouds: towards a fully automated workflow, The Seventh International Conference on Autonomic and Autonomous Systems, pp.67-74, 2011.
URL : https://hal.archives-ouvertes.fr/hal-01122123

A. Gandhi, S. Thota, P. Dube, A. Kochut, and L. Zhang, Autoscaling for hadoop clusters, 2016 IEEE International Conference on, pp.109-118, 2016.

H. Ghanbari, B. Simmons, M. Litoiu, and G. Iszlai, Exploring alternative approaches to implement an elasticity policy, 2011 IEEE International Conference on, pp.716-723, 2011.

M. Grounds and D. Kudenko, Parallel reinforcement learning with linear function approximation, Adaptive Agents and Multi-Agent Systems III. Adaptation and Multi-Agent Learning, pp.60-74, 2008.

R. Han, L. Guo, M. M. Ghanem, and Y. Guo, Lightweight resource scaling for cloud applications, Cluster, Cloud and Grid Computing (CCGrid), pp.644-651, 2012.

M. Z. Hasan, E. Magana, A. Clemm, L. Tucker, and S. L. Gudreddi, Integrated and autonomic cloud resource scaling', Network Operations and Management Symposium (NOMS), pp.1327-1334, 2012.

J. Hromkovi, Algorithmics for hard problems: introduction to combinatorial optimization, randomization, approximation, and heuristics, 2013.

C. Huang, C. Shih, W. Hu, B. Lin, and C. Cheng, The improvement of auto-scaling mechanism for distributed database-a case study for mongodb, Network Operations and Management Symposium (APNOMS), 2013 15th Asia-Pacific, pp.1-3, 2013.

M. M. Kandi, S. Yin, and A. Hameurlain, An integer linear-programming based resource allocation method for SQL-like queries in the cloud, ACM Symposium on Applied Computing (SAC), 2018.

S. Khatua, A. Ghosh, and N. Mukherjee, Optimizing the utilization of virtual resources in cloud environment', Virtual Environments Human-Computer Interfaces and Measurement Systems (VECIMS), IEEE, pp.82-87, 2010.

H. Kllapi, E. Sitaridi, M. M. Tsangaris, and Y. Ioannidis, Schedule optimization for data processing flows on the cloud, Proceedings of the 2011 ACM SIGMOD International Conference on Management of data, pp.289-300, 2011.

I. Konstantinou, E. Angelou, D. Tsoumakos, C. Boumpouka, N. Koziris et al., Tiramola: elastic nosql provisioning through a cloud management platform, Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data, ACM, pp.725-728, 2012.

R. M. Kretchmar, Parallel reinforcement learning, The 6th World Conference on Systemics, Cybernetics, and Informatics, 2002.

E. L. Lawler and D. E. Wood, Branch-and-bound methods: a survey, Operations Research, vol.14, issue.4, pp.699-719, 1966.

T. C. Mills, Time Series Techniques for Economists, 1991.

A. Naskos, A. Gounaris, and P. Katsaros, Cost-aware horizontal scaling of nosql databases using probabilistic model checking, Cluster Computing, vol.20, issue.3, pp.2687-2701, 2017.

A. Naskos, A. Gounaris, and I. Konstantinou, Elton: a cloud resource scaling-out manager for nosql databases', IEEE 34th International Conference on Data Engineering, pp.1641-1644, 2018.

A. Naskos, E. Stachtiari, A. Gounaris, P. Katsaros, D. Tsoumakos et al., Dependable horizontal scaling based on probabilistic model checking, 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid), pp.31-40, 2015.

J. Rao, X. Bu, C. Xu, and K. Wang, A distributed selflearning approach for elastic provisioning of virtualized cloud resources', Modeling, Analysis and Simulation of Computer and Telecommunication Systems (MASCOTS), IEEE 19th International Symposium on, pp.45-54, 2011.

J. Rao, X. Bu, C. Xu, L. Wang, and G. Yin, Vconf: a reinforcement learning approach to virtual machines autoconfiguration, Proceedings of the 6th international conference on Autonomic computing, ACM, pp.137-146, 2009.

B. Saha, H. Shah, S. Seth, G. Vijayaraghavan, A. Murthy et al., Apache tez: A unifying framework for modeling and building data processing applications, Proceedings of the 2015 ACM SIGMOD international conference on Management of Data, ACM, pp.1357-1369, 2015.

B. Simmons, H. Ghanbari, M. Litoiu, and G. Iszlai, Managing a saas application in the cloud using paas policy sets and a strategy-tree, Proceedings of the 7th International Conference on Network and Services Management, International Federation for Information Processing, pp.343-347, 2011.

G. Tesauro, N. K. Jong, R. Das, and M. N. Bennani, A hybrid reinforcement learning approach to autonomic resource allocation, Autonomic Computing, 2006. ICAC'06. IEEE International Conference on, pp.65-73, 2006.

D. Tsoumakos, I. Konstantinou, C. Boumpouka, S. Sioutas, and N. Koziris, Automated, elastic resource provisioning for nosql clusters using tiramola, Cluster, Cloud and Grid Computing (CCGrid), pp.34-41, 2013.

V. K. Vavilapalli, A. C. Murthy, and C. Douglas, Apache hadoop yarn: yet another resource negotiator, Proceedings of the 4th annual Symposium on Cloud Computing, ACM, p.5, 2013.

C. J. Watkins, Learning from Delayed Rewards, 1989.

S. Yin, A. Hameurlain, and F. Morvan, SLA definition for multi-tenant dbms and its impact on query optimization, IEEE Transactions on Knowledge and Data Engineering, vol.30, issue.11, pp.2213-2226, 2018.
URL : https://hal.archives-ouvertes.fr/hal-02319756

, Note 1 There are some differences between Hive/Tez and SparkSQL on the technical side but the output of query complication is similar