Second Screen User Profiling and Multi-level Smart Recommendations in the context of Social TVs

In the context of Social TV, the increasing popularity of first and second screen users, interacting and posting content online, illustrates new business opportunities and related technical challenges, in order to enrich user experience on such environments. SAM (Socializing Around Media) project uses Social Media-connected infrastructure to deal with the aforementioned challenges, providing intelligent user context management models and mechanisms capturing social patterns, to apply collaborative filtering techniques and personalized recommendations towards this direction. This paper presents the Context Management mechanism of SAM, running in a Social TV environment to provide smart recommendations for first and second screen content. Work presented is evaluated using real movie rating dataset found online, to validate the SAM's approach in terms of effectiveness as well as efficiency.Comment: In: Wu TT., Gennari R., Huang YM., Xie H., Cao Y. (eds) Emerging Technologies for Education. SETE 201

Containerised Application Profiling and Classification Using Benchmarks

Along with the rise of cloud and edge computing has come a plethora of solutions that regard the deployment and operation of different types of applications in such environments. Infrastructure as a service (IaaS) providers offer a number of different hardware solutions to facilitate the needs of the growing number of distributed applications. It is critical in this landscape to be able to navigate and discover the best-suited infrastructure solution for the applications, taking into account not only the cost of operation but also the quality of service (QoS) required for any given application. The proposed solution has two main research developments: (a) the creation and optimisation of multidimensional vectors that represent the hardware usage profiles of an application, and (b) the assimilation of a machine learning classification algorithm, in order to create a system that can create hardware-agnostic profiles of a vast variety of containerised applications in terms of nature and computational needs and classify them to known benchmarks. Given that benchmarks are widely used to evaluate a system’s hardware capabilities, having a system that can help select which benchmarks best correlate to a given application can help an IaaS provider make a more informed decision or recommendation on the hardware solution, not in a broad sense, but based on the needs of a specific application

Evaluation and optimization of quality of service and experience in Cloud Computing systems

Cloud has a number of features that are essential for application owners to ensure quality of service, stability and scalability. Because of these features it comes across as the de facto infrastructure solution for the installation and operation of a variety of applications. Developing an application in a Cloud environment, however, poses a number of serious challenges, mainly in terms of the provider and resource selection process, based on the expected quality of service, and the management of virtual resources at the provider's premises. This dissertation attempts to approach and solve these fundamental problems related to the adoption of Cloud solutions. In particular, it seeks to address these issues by providing a sophisticated architecture and set of software tools that assist both the Cloud user and the provider, in terms of application profiling and categorization, analysis and prediction of interference effects in parallel running applications, resource analysis and management, benchmarking and analysis and clarification of the quality of experience in Cloud infrastructure. In terms of interference and resource management, models based on artificial neural networks have been developed, which they analyze log data to help providers better manage virtual resources. Service quality and experience as well as the general evaluation of Cloud infrastructure focused on the development of software for the evaluation of the service level agreement quality as well as the standardization of the benchmarking processes and metrics.Το Cloud έχει μια πληθώρα από χαρακτηριστικά που είναι απαραίτητα για τους κατόχους εφαρμογών ώστε να διασφαλίσουν την ποιότητα υπηρεσίας, την σταθερότητα καθώς και την κλιμάκωσή τους. Λόγω αυτών των χαρακτηριστικών αποτελεί την ντε φάκτο λύση υποδομής για την εγκατάσταση και λειτουργεία μιας πληθώρας από εφαρμογές. Η ανάπτυξη μιας εφαρμογής σε ένα περιβάλλον Cloud ωστόσο, θέτει μια σειρά από σοβαρές προκλήσεις, κυρίως όσον αφορά τη διαδικασία επιλογής παρόχου και πόρων, με βάση την αναμενόμενη ποιότητα υπηρεσίας, καθώς και τη διαχείριση των εικονικών πόρων στις εγκαταστάσεις του παρόχου. Η διατριβή αυτή επιχειρεί να προσεγγίσει και να επιλύσει αυτά τα θεμελιώδη προβλήματα που αφορούν την υιοθέτηση λύσεων Cloud. Συγκεκριμένα, επιχειρεί να αντιμετωπίσει αυτά τα ζητήματα παρέχοντας μια εξελιγμένη αρχιτεκτονική και ένα σύνολο εργαλείων λογισμικού που βοηθά τόσο τον χρήστη του Cloud όσο και τον πάροχο, όσον αφορά το προφίλ και την κατηγοριοποίηση εφαρμογών, την ανάλυση και την πρόβλεψη των επιπτώσεων παρεμβολών σε παραλληλοποιημένες εφαρμογές, την ανάλυση και διαχείριση των πόρων, τη συγκριτική αξιολόγηση και την ανάλυση και αποσαφήνιση της ποιότητας εμπειρίας σε υποδομές Cloud. Αναφορικά με τις παρεμβολές και διαχείριση πόρων αναπτύχθηκαν μοντέλα βασισμένα σε τεχνητά νευρωνικά δίκτυα, που αναλύουν δεδομένα καταγραφής για να βοηθήσουν τους παρόχους να διαχειριστούν καλύτερα τους εικονικούς πόρους. Η ποιότητα υπηρεσίας και εμπειρίας καθώς και η αξιολόγηση των Cloud υποδομών επικεντρώθηκε στην ανάπτυξη λογισμικού για την αξιολόγηση του επιπέδου ποιότητας υπηρεσίας, καθώς και στην τυποποίηση των μετρικών και διαδικασιών της συγκριτικής αξιολόγησης των Cloud υπηρεσιών

Reinforcing SLA Consensus on Blockchain

Cloud Infrastructure as a Service (IaaS) Service Level Agreements (SLAs) assessment constitutes the de facto area of interest and applications in the public cloud infrastructure. However, the domination of colossal corporations tends to monopolize the way metrics and Key Performance Indicators (KPIs) are measured and determined, leading to governed environments where the clientele is unable to obtain accurate and unbiased assessment of SLAs. Leaning toward SLA self-assessment, this paper provides a fair SLA consensus approach with innate transparency and privacy by leveraging permissioned blockchains that are equipped with Trusted Execution Environments (TEEs). The SLA assessment intelligence is performed inside enclaved smart contracts isolated from the on-chain entities views. The result constitutes a permissioned blockchain ecosystem where the IaaS and their clientele commonly agree on all the respective SLA monitoring and computation rules beforehand, as defined in any SLA assessment process, while the SLA consensus scheme constantly audits the SLA metrics based on these pre-approved regulations

Containerised Application Profiling and Classification Using Benchmarks

Along with the rise of cloud and edge computing has come a plethora of solutions that regard the deployment and operation of different types of applications in such environments. Infrastructure as a service (IaaS) providers offer a number of different hardware solutions to facilitate the needs of the growing number of distributed applications. It is critical in this landscape to be able to navigate and discover the best-suited infrastructure solution for the applications, taking into account not only the cost of operation but also the quality of service (QoS) required for any given application. The proposed solution has two main research developments: (a) the creation and optimisation of multidimensional vectors that represent the hardware usage profiles of an application, and (b) the assimilation of a machine learning classification algorithm, in order to create a system that can create hardware-agnostic profiles of a vast variety of containerised applications in terms of nature and computational needs and classify them to known benchmarks. Given that benchmarks are widely used to evaluate a system’s hardware capabilities, having a system that can help select which benchmarks best correlate to a given application can help an IaaS provider make a more informed decision or recommendation on the hardware solution, not in a broad sense, but based on the needs of a specific application

A Scalable and Semantic Data as a Service Marketplace for Enhancing Cloud-Based Applications

Data handling and provisioning play a dominant role in the structure of modern cloud–fog-based architectures. Without a strict, fast, and deterministic method of exchanging data we cannot be sure about the performance and efficiency of transactions and applications. In the present work we propose an architecture for a Data as a Service (DaaS) Marketplace, hosted exclusively in a cloud environment. The architecture includes a storage management engine that ensures the Quality of Service (QoS) requirements, a monitoring component that enables real time decisions about the resources used, and a resolution engine that provides semantic data discovery and ranking based on user queries. We show that the proposed system outperforms the classic ElasticSearch queries in data discovery use cases, providing more accurate results. Furthermore, the semantic enhancement of the process adds extra results which extend the user query with a more abstract definition to each notion. Finally, we show that the real-time scaling, provided by the data storage manager component, limits QoS requirements by decreasing the latency of the read and write data requests

Dynamic Social and Media Content Syndication for Second Screen

Social networking apps, sites and technologies offer a wide range of opportunities for businesses and developers to exploit the vast amount of information and user-generated content produced through social networking. In addition, the notion of second screen TV usage appears more influential than ever, with viewers continuously seeking further information and deeper engagement while watching their favourite movies or TV shows. In this work, the authors present SAM, an innovative platform that combines social media, content syndication and targets second screen usage to enhance media content provisioning, renovate the interaction with end-users and enrich their experience. SAM incorporates modern technologies and novel features in the areas of content management, dynamic social media, social mining, semantic annotation and multi-device representation to facilitate an advanced business environment for broadcasters, content and metadata providers, and editors to better exploit their assets and increase their revenues.This work has been partially funded by the European Commission under the Seventh (FP7 - 2007- 2013) Framework Programme for Research and Technological Development through the SAM (FP7-611312) project