Building the IDECi-UIB: the scientific spatial data infrastructure node for the Balearic Islands University

Technical and methodological enhancements in Information Technologies (IT) and Geographical Information Systems (GIS) has permitted the growth in Spatial Data Infrastructures (SDI) performance. In this way, their uses and applications have grown very rapidly. In the scientific and educational working fields, different institutions and organisations have bet for its use enforcing information exchange that allows researchers to improve their studies as well as give a better dissemination within the scientific community. Therefore, the GIS and Remote Sensing Service (SSIGT) at the Balearic Islands University (UIB) has decided to build and launch its own SDI to serve scientific Geo-Information (GI) throughout the Balearic Islands society focussing on the university community. By these means it intends to boost the development of research and education focusing on the field of spatial information. This article tries to explain the background ideas that form the basic concept of the scientific SDI related to the concepts of e-Science and e-Research. Finally, it explains how these ideas are taken into practice into the new University Scientific SDI

Next Generation Cloud Computing: New Trends and Research Directions

The landscape of cloud computing has significantly changed over the last decade. Not only have more providers and service offerings crowded the space, but also cloud infrastructure that was traditionally limited to single provider data centers is now evolving. In this paper, we firstly discuss the changing cloud infrastructure and consider the use of infrastructure from multiple providers and the benefit of decentralising computing away from data centers. These trends have resulted in the need for a variety of new computing architectures that will be offered by future cloud infrastructure. These architectures are anticipated to impact areas, such as connecting people and devices, data-intensive computing, the service space and self-learning systems. Finally, we lay out a roadmap of challenges that will need to be addressed for realising the potential of next generation cloud systems.Comment: Accepted to Future Generation Computer Systems, 07 September 201

Private Data System Enabling Self-Sovereign Storage Managed by Executable Choreographies

With the increased use of Internet, governments and large companies store and share massive amounts of personal data in such a way that leaves no space for transparency. When a user needs to achieve a simple task like applying for college or a driving license, he needs to visit a lot of institutions and organizations, thus leaving a lot of private data in many places. The same happens when using the Internet. These privacy issues raised by the centralized architectures along with the recent developments in the area of serverless applications demand a decentralized private data layer under user control. We introduce the Private Data System (PDS), a distributed approach which enables self-sovereign storage and sharing of private data. The system is composed of nodes spread across the entire Internet managing local key-value databases. The communication between nodes is achieved through executable choreographies, which are capable of preventing information leakage when executing across different organizations with different regulations in place. The user has full control over his private data and is able to share and revoke access to organizations at any time. Even more, the updates are propagated instantly to all the parties which have access to the data thanks to the system design. Specifically, the processing organizations may retrieve and process the shared information, but are not allowed under any circumstances to store it on long term. PDS offers an alternative to systems that aim to ensure self-sovereignty of specific types of data through blockchain inspired techniques but face various problems, such as low performance. Both approaches propose a distributed database, but with different characteristics. While the blockchain-based systems are built to solve consensus problems, PDS's purpose is to solve the self-sovereignty aspects raised by the privacy laws, rules and principles.Comment: DAIS 201

Exploring heterogeneity of unreliable machines for p2p backup

P2P architecture is a viable option for enterprise backup. In contrast to dedicated backup servers, nowadays a standard solution, making backups directly on organization's workstations should be cheaper (as existing hardware is used), more efficient (as there is no single bottleneck server) and more reliable (as the machines are geographically dispersed). We present the architecture of a p2p backup system that uses pairwise replication contracts between a data owner and a replicator. In contrast to standard p2p storage systems using directly a DHT, the contracts allow our system to optimize replicas' placement depending on a specific optimization strategy, and so to take advantage of the heterogeneity of the machines and the network. Such optimization is particularly appealing in the context of backup: replicas can be geographically dispersed, the load sent over the network can be minimized, or the optimization goal can be to minimize the backup/restore time. However, managing the contracts, keeping them consistent and adjusting them in response to dynamically changing environment is challenging. We built a scientific prototype and ran the experiments on 150 workstations in the university's computer laboratories and, separately, on 50 PlanetLab nodes. We found out that the main factor affecting the quality of the system is the availability of the machines. Yet, our main conclusion is that it is possible to build an efficient and reliable backup system on highly unreliable machines (our computers had just 13% average availability)

Sigmoid(x): secure distributed network storage

Secure data storage is a serious problem for computer users today, particularly in enterprise environments. As data requirements grow, traditional approaches of secured silos are showing their limitations. They represent a single – or at least, limited – point of failure, and require significant, and increasing, maintenance and overhead. Such solutions are totally unsuitable for consumers, who want a ‘plug and play’ secure solution for their increasing datasets – something with the ubiquity of access of Facebook or webmail. Network providers can provide centralised solutions, but that returns us to the first problem. Sigmoid(x) takes a completely different approach – a scalable, distributed, secure storage mechanism which shares data storage between the users themselves