Distributed, Parallel and Collaborative Systems

Proposta de tesi


Grup de recerca

Parallel and distributed scientific applications: performance and efficiency

There are currently various bottlenecks in the growth in parallel and distributed programming paradigms and environments, which are affecting the ability to provide efficient applications for performing concurrent computations.

We need to know the platforms, their performance, the underlying hardware and networking technologies, and we must be able to produce optimized software that statically or dynamically may take advantage of the computational resources available.

In this line of research we study different approaches to producing better scientific applications, and to making tools (via automatic performance analysis), which can understand the application model and the underlying programming paradigm. We try to tune the performance of these to a dynamically changing computational environment, in which the resources (and their characteristics) can be homogeneous or heterogeneous depending on the hardware platform. In particular we focus our research on shared memory and message-passing paradigms, and in many-core/multi-core environments including multi-core CPUs, GPUs (graphic cards computing) and cluster/grid/cloud/super computing platforms.

Dr. Josep Jorba

WINE Research Group

Reliability and availability issues in distributed computer systems

Increasingly, services are being deployed over large-scale computational and storage infrastructures. To meet ever-increasing computational demands and reduce both hardware and system administration costs, these infrastructures have begun to include Internet resources distributed over enterprise and residential broadband networks. As these infrastructures increase in scale to hundreds of thousands or millions of resources, issues of resource availability and service reliability inevitably emerge.

Contributory distributed systems based on non-dedicated resources provide attractive benefits but face at least one significant challenge: nodes can enter and leave the collective on a whim, because each machine may be separately owned and managed. At the same time, resource availability is critical for the reliability and responsiveness (low response latency) of services. Groups of available resources are often required to execute tightly coupled distributed and parallel algorithms of services. Moreover, load spikes observed with Internet services require guarantees that a collection of resources is available.

The goal of this line of research is to determine and evaluate predictive methods that ensure the availability of a collection of resources. We strive to achieve collective availability from non-dedicated resources distributed around the Internet, arguably the most unreliable type of resource worldwide. Our predictive methods could work in coordination with a virtualization layer for masking resource unavailability.

Dr. Joan Manuel Marquès ICSO Research Group

Community cloud systems based on non-dedicated resources

There are currently many online communities in existence: social networks, open source development, Wikipedia, Wikileaks, etc. These communities have thousands or even millions of participants and are commonly hosted in resources belonging to organizations not directly related with participants in the community. Considering that most of the participants' computers are underutilized – ie they have more resources than are actually used in daily activity – we are studying how to take advantage of these surplus resources to be able to host communities using only the resources provided by their members. We call these kinds of communities contributory communities.

More precisely, we are working on a) availability prediction of non-dedicated resources; b) privacy guarantees, especially anonymity; and c) efficient deployment and storage.

Therefore, we are looking for researchers interested in large-scale distributed systems applied to contributory communities in fields such as availability guarantees, privacy, efficient deployment of services and reliable storage.

Dr. Joan Manuel Marquès

ICSO Research Group

Simulation environments for networking and distributed systems

Large-scale testbeds like PlanetLab open up new opportunities for testing and simulating network protocols, distributed systems and applications. An unresolved issue in these kinds of systems is how to create, deploy, execute and monitor experiments in a coordinated way, in order to take advantage of the real deployment of systems and protocols. A great deal of research needs to be undertaken to understand the kinds of systems that can benefit from these environments and the systems that are better simulated in classical simulation environments. Adapting existing simulation environments to these new systems is not a trivial process and a great deal of work has to be done in order to characterize the distributed environment and its coordination needs. We are especially interested in building simulation environments for distributed systems or networking in large-scale testbeds, both for research and teaching.

Dr. Joan Manuel Marquès ICSO Research Group

Big data, BI and NoSQL systems

Storing, processing and extracting valuable knowledge from data has been the most general use case in the field of business applications over the last 40 years. We have seen an alternating trend in data storage, manipulation and access that continues to repeat itself. Technologies have been moving from general approaches to specialized techniques and back to generalized, with revisited techniques. Five years ago, key value stores implementing the BigTable structure such as HBase, column oriented stores such as Cassandra and document stores such as MongoDB emerged, quickly demonstrating an increase in performance by several orders of magnitude for certain types of applications. However, these models required lots of specialization, meaning that applications tailored to one technology would not be easily ported to another. An interesting fact is that nowadays most of the large scale Internet applications are storing their data in a NoSQL data store, having realized that specialization imposes a big restriction on the flexibility to query data. Impala, Hive, Kiji, Pig, etc., are shifting the trend again, our old friend SQL is returning, this time in NoSQL data-stores. In this line of research we aim to investigate new techniques to retrieve meaningful data efficiently from big data repositories, ie business intelligence, new tools to leverage the complexity of MapReduce implementations and to contribute to the standardization and generalization of querying NoSQL systems.

Dr. Xavier Vilajosana

Dra. Àngels Rius

WINE Research Group

Development of e-collaboration and e-training tools which promote horizontal cooperation among enterprises

In a competitive global market, horizontal cooperation among companies constitutes a key issue, especially for small and medium-sized enterprises that have to compete with world-class firms. The research work conducted in this topic should first perform a comparative analysis of technologies and methodologies that encourage horizontal collaboration among companies in educational environments, business, transport and logistics (eg indoor/outdoor location), information systems (eg integration of information systems), telecommunications, etc. This should highlight the computational aspects that have a significant influence on the development of activities and strategies for horizontal collaboration. The relevance of this praxis is a way of benefitting from economies of scale in order to reduce costs, improve quality of service, and become more environmental friendly, thus increasing firms’ competitiveness and social responsiveness. After identifying the common opportunities and challenges associated with real-life computer-based horizontal cooperation, the research work will propose an integrated solution based on the development of innovative e-collaboration and e-training tools which allow companies to promote online collaboration (including technical training) among employees of different departments or even different companies.

Dr. Atanasi Daradoumis

Dr. Angel A Juan 

ICSO Research Group