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Nowadays, service providers offer a lot of IT services in the public or private cloud. The client can buy various kinds of services like SaaS, PaaS, etc. Recently there was introduced Backup as a Service (BaaS) as a variety of SaaS. At the moment there are available several different BaaSes for archiving the data in the cloud, but they provide only a basic level of service quality. In the paper we propose a model which ensures QoS for BaaS and some methods for management of storage resources aimed at achieving the required SLA. This model introduces a set of parameters responsible for SLA level which can be offered on the basic or higher level of quality. The storage systems (typically HSM), which are distributed between several Data Centres, are built based on disk arrays, VTLs, and tape libraries. The RSMM model does not assume bandwidth reservation or control, but is rather focused on the management of storage resources.

Traditional data storage systems provide access to user’s data on the “besteffort” basis. While this paradigm is sufficient in many use cases it becomesan obstacle for applications with Quality of Service (QoS) constraints. ServiceLevel Agreement (SLA) is a part of the contract agreed between the serviceprovider and the client and contains a set of well defined QoS requirementsregarding the provided service and the penalties applied in case of violations.In the paper we propose a set of SLA parameters and QoS metrics relevantto data storage processes and the management methods necessary for avoidingSLA violations. A key assumption in the proposed approach is that the underlyingdistributed storage system does not provide functionality for resource orbandwidth reservation for a given client request.

Grid computing has recently gained in popularity. Grid applications can be very demanding of the data storage facilities in the Grid. The existing data grid services are often insufficient and additional optimization of the data access is necessary. This paper presents research concerning the optimization aspects of data management for Hierarchical Storage Management (HSM) systems in the Grid.

The EU Pellucid project is developing an experience management system for public organizations with staff mobility. The paper presents an activity whitin the project focused on searching for information in repositories of documents. The project’s background and the process of information searching are described. Ontological methods such as semantic annotation and similarity searching, as well as ontology- and full-text-based searching are presented. Monitoring of organizational repositories is discussed.

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.

In this paper we describe a semi-automatic programming framework for supporting userswith managing the deployment of distributed applications along with storing large amountsof data in order to maintain Quality of Service in highly dynamic and distributed environments,e.g., Grid. The Polish national PL-GRID project aims to provide Polish science withboth hardware and software infrastructures which will allow scientists to perform complexsimulations and in-silico experiments on a scale greater than ever before. We highlight theissues and challenges related to data storage strategies that arise at the analysis stage ofuser requirements coming from different areas of science. Next we present a solution to thediscussed issues along with a description of sample usage scenarios. At the end we provideremarks on the current status of the implementation work and some results from the testsperformed.