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.
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
- ALOMAR, GUILLEM - JUNIOR DEVELOPER
- BECERRA, YOLANDA - ASSOCIATE RESEARCHER
- BOSCH JIMENEZ, RAIMON - JUNIOR DEVELOPER
- CANUTO, MAURO - RESEARCH SUPPORT ENGINEER
- CAPDEVILA, JOAN - ASSOCIATE STUDENT
- CUGNASCO, CESARE - RESEARCH SUPPORT ENGINEER
- GUITART FERNANDEZ, JORDI - ASSOCIATE RESEARCHER
- IMTIAZ, SANA - JUNIOR DEVELOPER
- MACIAS LLORET, MARIO - POSTDOCTORAL RESEARCHER
- TORRES VINALS, JORDI - AUTONOMIC SYSTEMS AND E-BUSINESS PLATFORMS GROUP MANAGER