Iuhasz Gabriel
Citado por
Citado por
DICE: quality-driven development of data-intensive cloud applications
G Casale, D Ardagna, M Artac, F Barbier, E Di Nitto, A Henry, G Iuhasz, ...
2015 IEEE/ACM 7th International Workshop on Modeling in Software Engineering …, 2015
Neural network predictions of stock price fluctuations
G Iuhasz, M Tirea, V Negru
2012 14th International Symposium on Symbolic and Numeric Algorithms for …, 2012
Neuroevolution based multi-agent system for micromanagement in real-time strategy games
I Gabriel, V Negru, D Zaharie
Proceedings of the fifth balkan conference in informatics, 32-39, 2012
An overview of monitoring tools for big data and cloud applications
G Iuhasz, I Dragan
Symbolic and Numeric Algorithms for Scientific Computing (SYNASC), 2015 17th …, 2015
Tuning logstash garbage collection for high throughput in a monitoring platform
DN Doan, G Iuhasz
2016 18th International Symposium on Symbolic and Numeric Algorithms for …, 2016
Support services for applications execution in multi-clouds environments
D Pop, G Iuhasz, C Craciun, S Panica
2016 IEEE International Conference on Autonomic Computing (ICAC), 343-348, 2016
Architecture of a scalable platform for monitoring multiple big data frameworks
G Iuhasz, D Pop, I Dragan
Scalable Computing: Practice and Experience 17 (4), 313-321, 2016
Applying self-* principles in heterogeneous cloud environments
I Drăgan, TF Fortiş, G Iuhasz, M Neagul, D Petcu
Cloud Computing, 255-274, 2017
Data mining considerations for knowledge acquisition in real time strategy games
G Iuhasz, VI Munteanu, V Negru
2013 IEEE 11th International Symposium on Intelligent Systems and …, 2013
Neuroevolution based multi-agent system with ontology based template creation for micromanagement in real-time strategy games
I Gabriel, V Negru, D Zaharie
Information Technology and Control 43 (1), 98-109, 2014
On processing extreme data
D Petcu, G Iuhasz, D Pop, D Talia, J Carretero, R Prodan, T Fahringer, ...
Scalable Computing. Practice and Experience 16 (4), 467-489, 2016
Run-time adaptation policies
G Casale, F Kalo, JF Pérez, W Wang, D Ardagna, M Ciavotta, G Iuhasz
MODAClouds EU Project Deliverable, 2014
Overview of machine learning tools and libraries
D Pop, G Iuhasz
Inst. e-Austria Timisoara, 0
A scalable platform for monitoring data intensive applications
I Drăgan, G Iuhasz, D Petcu
Journal of Grid Computing 17 (3), 503-528, 2019
Load balancing for multi-cloud
G Iuhasz, P Jamshidi, W Wang, G Casale
Model-Driven Development and Operation of Multi-Cloud Applications, 53-58, 2017
Distributed platforms and cloud services: Enabling machine learning for big data
D Pop, G Iuhasz, D Petcu
Data Science and Big Data Computing, 139-159, 2016
Perspectives on anomaly and event detection in exascale systems
G Iuhasz, D Petcu
2019 IEEE 5th Intl Conference on Big Data Security on Cloud (BigDataSecurity …, 2019
An Architecture for a Management Agency for Cloud Resources
M Erascu, G Iuhasz, F Micota
2018 20th International Symposium on Symbolic and Numeric Algorithms for …, 2018
Runtime environment final release
G Iuhasz, S Panica, G Casale, W Wang
Modaclouds Deliverable D 6, 2015
A survey of adaptive game AI: Considerations for cloud deployment
G Iuhasz, VI Munteanu, V Negru
Intelligent distributed computing VII, 309-315, 2014
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Artículos 1–20