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There is high demand for storage related services supporting scientists in their research activities. Those services are expected to provide not only capacity but also features allowing for more flexible and cost efficient usage. Such features include easy multiplatform data access, long term data retention, support for performance and cost differentiating of SLA restricted data access. The paper presents a policy-based SLA storage management model for distributed data storage services. The model allows for automated management of distributed data aimed at QoS provisioning with no strict resource reservation. The problem of providing users with the required QoS requirements is complex, and therefore the model implements heuristic approach for solving it. The corresponding system architecture, metrics and methods for SLA focused storage management are developed and tested in a real, nationwide environment.

In this paper we present the results of a two-year study aimed at developing a full-fledged computer environment supporting post-stroke rehabilitation. The system was designed by a team of computer scientists, psychologists and physiotherapists. It adopts a holistic approach to rehabilitation. In order to extend the rehabilitation process, the applied methods include a remote rehabilitation stage which can be carried out of at the patient’s home. The paper presents a distributed system architecture as well as results achieved by patients prior to and following a three-month therapy based on the presented system.

Traditional data storage systems provide access to user’s data on the “best effort” basis. While this paradigm is sufficient in many use cases it becomes an obstacle for applications with Quality of Service (QoS) constraints. Service Level Agreement (SLA) is a part of the contract agreed between the service provider and the client and contains a set of well defined QoS requirements regarding the provided service and the penalties applied in case of violations. In the paper we propose a set of SLA parameters and QoS metrics relevant to data storage processes and the management methods necessary for avoiding SLA violations. A key assumption in the proposed approach is that the underlying distributed storage system does not provide functionality for resource or bandwidth reservation for a given client request.

This paper describes a programming toolkit developed in the PL-Grid project, named QStorMan, which supports storage QoS provisioning for data-intensive applications in distributed environments. QStorMan exploits knowledge-oriented methods for matching storage resources to non-functional requirements, which are defined for a data-intensive application. In order to support various usage scenarios, QStorMan provides two interfaces, such as programming libraries or a web portal. The interfaces allow to define the requirements either directly in an application source code or by using an intuitive graphical interface. The first way provides finer granularity, e.g., each portion of data processed by an application can define a different set of requirements. The second method is aimed at legacy applications support, which source code can not be modified. The toolkit has been evaluated using synthetic benchmarks and the production infrastructure of PL-Grid, in particular its storage infrastructure, which utilizes the Lustre file system.

Applying the principles of open research data is an important factor accelerating the production, analysis of scientific results and worldwide collaboration. However, still very little data is being shared. The aim of this article is analysis of existing data access solutions in order to identify reasons for such situation. After analysis of existing solutions and data access stakeholders needs, the authors propose own vision of data access model evolution.