Autonomic Systems and e-Business Platforms


The goal of our research group is to explore the future of computing by performing high-level research in today’s eBusiness applications that have to deal with critical IT challenges in areas such as Cognitive Computing, Big Data, Cloud Computing, High Performance Computing and Sustainable Computing.


The group is conducting research on autonomic and intelligent resource management policies based on Self-Management strategies as the way to improve the computer middleware layers.

In these research fields the team produces top research publications as well as software components and resource management policies that can be applied at middleware level in order to improve their adaptability, efficiency and productivity. 

The group targets execution platforms composed of high-productivity heterogeneous multi-core systems with accelerators and advanced storage architectures deployed in large-scale distributed environments.

Research Lines: 

We are doing research in energy-aware computing which the goal of develop management algorithms for virtualised Data Centres in a large-scale distributed ecosystem running heterogeneous workloads that optimize their operation with respect to energy and ecological efficiency.  The work in this area is grouped in the following main lines:

  • Models for the assessment and forecasting of energy and ecological efficiency in a virtualised Data Centre at different levels
  • Policies for the optimization of the scheduling and placement of Virtual Machines (VMs) in physical nodes considering the energy and ecological efficiency factors
  • Policies for the selection of Data Centre for remote placement of Virtual Machines (VMs) in a Data Centre ecosystem considering the energy and ecological efficiency factors
  • Integration of the cooling and power supply subsystems in the energy management strategy of Data Centres
  • Integration of renewable energy sources in the energy management strategy of Data Centres       

We are also doing research in Data-driven Scientific Computing. The goal of this area is to design resource management strategies for Big Data applications, defining policies that enable distributed data stores to meet high-level performance goals. We focus on scientific applications, like those from life science domain, which data generation and accesses bound both precision and performance. During next years the main work of this research activity will be developed as part of these four main threads:

  • Propose novelty resource management strategies as query-driven data model, which focus on adapting the data model to the particular type of accesses implemented by the applications. We also aim to consider the intrinsic of continuous data streaming with real time requirements. This kind of environment also raises the challenge of defining an execution framework that is able to digest this kind of input data streams.
  • Create a set of plugin modules based on our research results in order to be added to state-of-the-art open source NoSQL platforms. After this integration the comprehensive software package will be integrated in the BSC Big Data tools that the BSC department Computer Science will develop with the conjoint work of our research group and the groups of Storage and Grid. 
  • Hecuba: A project that aims to design and develop strategies to facilitate programmers the efficient usage of data stores for big data applications. For example, we will provide programmers with a software layer that will decouple data models from data layouts.
  •  EuroServer (FP7-ICT-2013-10 European Project, Grant Agreement no: 610456): Green Computing Node for European Micro-servers. Goal: Design and build a drastically improved energy- and cost-efficient solution suitable across both cloud data-centres and embedded application workloads. Our contribution: Optimise the local placement of Virtual Machines (VMs) within a physical node in a single Data Centre aiming for energy efficiency, by exploiting ARM low-power architectures
  • RenewIT (FP7-SMARTCITIES-2013 European Project, Grant Agreement no: 608679): Advanced concepts and tools for renewable energy supply of IT Data Centres. Goal: Develop a simulation tool to evaluate energy performance of different technical solutions that integrate renewable energy supply in IT Data Centres. Our contribution: Optimise both the local placement of Virtual Machines (VMs) in physical nodes and the selection of Data Centre for remote placement of VMs aiming for ecological efficiency, by exploiting the usage of green energy and the interaction with energy supply and cooling systems
  • ASCETiC  (FP7-ICT-2013-10 European Project, Grant Agreement no: 610874): Adapting Service lifeCycle towards EfficienT Clouds. Goal: Definition and integration of explicit measures of energy and ecological requirements into the design and development process for software. Our contribution: Optimise both the local placement of Virtual Machines (VMs) in physical nodes and the selection of Data Centre for remote placement of VMs aiming for energy efficiency, by focusing on the interaction and information exchange among Cloud layers during the whole service lifecycle for better optimization




Cugnasco C, Becerra Y, Torres J, Ayguadé E. D8-tree: a de-normalized approach for multidimensionaldata analysis on key-value databases. ICDCN '16 Proceedings of the 17th International Conference on Distributed Computing and Networking [Internet]. 2016 . Available from:


Tous R, Torres J, Ayguadé E. Multimedia Big Data Computing for In-depth Event Analysis. IEEE International Conference on Multimedia Big Data (BigMM 2015). Beijing, China, April 2015. [Internet]. 2015 :144-147. Available from:
Hernandez R, Cugnasco C, Becerra Y, Torres J, Ayguadé E. Experiences of Using Cassandra for Molecular Dynamics Simulation. 2015 23rd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing [Internet]. 2015 :288-295. Available from:
Basmadjian R, Bouvry P, Da Costa G, Gyarmati L, Kliazovich D, Lafond S, Lefèvre L, De Meer H, Pierson JM, Pries R, et al. Green Data Centers. In: Large-Scale Distributed Systems and Energy Efficiency: A holistic view. Large-Scale Distributed Systems and Energy Efficiency: A holistic view. ; 2015. pp. 159-196. Available from:
Capdevila J, Cerquides J, Nin J, Torres J. Tweet-SCAN: An event discovery technique for geo-located tweets. Artificial Intelligence Research and Development. Proceedings of the 18th International Conference of the Catalan Association for Artificial Intelligence [Internet]. 2015 ;277( Frontiers in Artificial Intelligence and Applications):110-119. Available from:


Poggi N, Carrera D, Ayguadé E, Torres J. Profit-Aware Cloud Resource Provisioner for Ecommerce. 2014 IEEE International Conference on Cluster Computing. 2014 :274–275.
Cea D, Nin J, Tous R, Torres J, Ayguadé E. Towards the Cloudification of the Social Networks Analytics. 11th International Conference on Modeling Decisions for Artificial Intelligence. 2014 :192–203.
Ferrer-Roca O, Tous R, Milito R. Big and Small data. The FOG. 1st International Conference on Identification, Information and Knowledge in the Internet of the Things. 2014 .
Polo J, Becerra Y, Carrera D, Torres J, Ayguadé E, Steinder M. Adaptive MapReduce Scheduling in Shared Environments. 14th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing. 2014 :61–70.