This research is done in the context of the SliceNet project [4] that aims to extend 5G infrastructure with cognitive management of cross-domain, cross-layer network slices [1], with emphasis on Quality of Experience (QoE) for vertical industries. The provisioning of network slices with proper QoE guarantees is seen as one of the key enablers of future 5G-enabled networks. The challenge is to assess the QoE experienced by the vertical application and its users without requiring the applications or the users to measure and report QoE related metrics back to the provider. To address this challenge, we propose a method for deriving application-level QoE from network-level Quality of Service (QoS) measurements, easily accessible by the provider. In particular, we describe a PoC where QoE, perceived by application users, is estimated from low level network monitoring data, by applying cognitive methods. Our main goal is enabling the cloud provider to support the desired E2E QoE-based Service Level Agreements (SLAs), e.g. by monitoring QoS metrics within the provider's domain to optimize resource allocation through provider's actuators. Additional benefit can be achieved by applying the same technique to troubleshoot issues in the provider's infrastructure. In this work, we employed classical statistical methods to assess the relationship between the application-level QoE and the network-level QoS.

The emergence of the cloud and advanced object-based storage services provides opportunities to support novel models for long term preservation of digital assets. Among the benefits of this approach is leveraging the cloud's inherent scalability and redundancy to dynamically adapt to evolving needs of digital preservation. PDS Cloud is an OAIS-based preservation-aware storage service employing multiple heterogeneous cloud providers. It materializes the logical concept of a preservation information-object into physical cloud storage objects. Preserved information can be interpreted by deploying virtual appliances in the compute cloud provisioned with cloud storage data objects together with their designated rendering software. PDS Cloud has a hierarchical data model supporting independent tenants whose assets are organized in multiple aggregations based on content and value. Continuous changes to data objects, life-cycle activities, virtual appliances and cloud providers are applied in a manner transparent to the client. PDS Cloud is being developed as an infrastructure component of the European Union ENSURE project, where it is used for preservation of medical and financial data.

Cloud computing is increasingly gaining inroads among a variety of organizational users. As clouds are introduced for use by enterprises, service providers, and governmental and educational entities, new challenges related to the interconnection between such clouds emerge. Cloud administrators seek to maintain acceptable levels of autonomy and control over their cloud infrastructure, while ensuring the integrity of the cloud services. At the same time, they are expected to enable cross-cloud services, including mobility of workloads between clouds. We present the design and implementation of a technology that enables live mobility of virtual machines between clouds, while enforcing the cloud insularity requirements of autonomy, privacy, and security. We also provide an empirical evaluation of our solution, demonstrating its viability and compliance with requirements.

We describe the tools and interfaces created by the AGEDIS project, a European Commission sponsored project for the creation of a methodology and tools for automated model driven test generation and execution for distributed systems. The project includes an integrated environment for modeling, test generation, test execution, and other test related activities. The tools support a model based testing methodology that features a large degree of automation and also includes a feedback loop integrating coverage and defect analysis tools with the test generator and execution framework. Prototypes of the tools have been tried in industrial settings providing important feedback for the creation of the next generation of tools in this area.