Enabling IoT stream management in multi-cloud environment by orchestration

(c) 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.Every-Day lives are becoming increasingly instrumented by electronic devices and any kind of computer-based (distributed) service. As a result, organizations need to analyse an enormous amounts of data in order to increase their incomings or to improve their services. Anyway, setting-up a private infrastructure to execute analytics over Big Data is still expensive. The exploitation of Cloud infrastructure in IoT Stream management is appealing because of costs reductions and potentiality of storage, network and computing resources. The Cloud can consistently reduce the cost of analysis of data from different sources, opening analytics to big storages in a multi-cloud environment. Anyway, creating and executing this kind of service is very complex since different resources have to be provisioned and coordinated depending on users' needs. Orchestration is a solution to this problem, but it requires proper languages and methodologies for automatic composition and execution. In this work we propose a methodology for composition of services used for analyses of different IoT Stream and, in general, Big Data sources: in particular an Orchestration language is reported able to describe composite services and resources in a multi-cloud environment.Peer ReviewedPostprint (author's final draft

A Model Driven Approach to Water Resource Analysis based on Formal Methods and Model Transformation

AbstractSeveral frameworks have been proposed in literature in order to cope with critical infrastructure modelling issues, and almost all rely on simulation techniques. Anyway simulation is not enough for critical systems, where any problem may lead to consistent loss in money and even human lives. Formal methods are widely used in order to enact exhaustive analyses of these systems, but their complexity grows with system dimension and heterogeneity. In addition, experts in application domains could not be familiar with formal modelling techniques. A way to manage complexity of analysis is the use of Model Based Transformation techniques: analysts can express their models in the way they use to do and automatic algorithms translate original models into analysable ones, reducing analysis complexity in a completely transparent way.In this work we describe an automatic transformation algorithm generating hybrid automata for the analysis of a natural water supply system. We use real system located in the South of Italy as case study

Generation of game contents by social media analysis and MAS planning

In the age of pervasive computing and social networks, it has become commonplace to retrieve opinions about digital contents in games. In the case of multi-player, open world gaming, in fact even in “old-school” single players games, it is evident the need for adding new features in a game depending on users comments and needs. However this is a challenging task that usually requires considerable design and programming efforts, and more and more patches to games, with the inevitable consequence of loosing interest in the game by players over years. This is particularly a hard problem for all games that do not intend to be designed as interactive novels. Process Content Generation (PCG) of new contents could be a solution to this problem, but usually such techniques are used to design new maps or graphical contents. Here we propose a novel PCG technique able to introduce new contents in games by means of new story-lines and quests. We introduce new intelligent agents and events in the world: their attitudes and behaviors will promote new actions in the game, leading to the involvement of players in new gaming content. The whole methodology is driven by Social Media Analysis contents about the game, and by the use of formal planning techniques based on Multi-Agents modelsPeer ReviewedPostprint (author's final draft

A component-based framework for certification of components in a cloud of HPC services

HPC Shelfis a proposal of a cloud computing platform to provide component-oriented services for High Performance Computing (HPC) applications. This paper presents a Verification-as-a-Service (VaaS) framework for component certification onHPC Shelf. Certification is aimed at providing higher confidence that components of parallel computing systems ofHPC Shelfbehave as expected according to one or more requirements expressed in their contracts. To this end, new abstractions are introduced, starting with certifier components. They are designed to inspect other components and verify them for different types of functional, non-functional and behavioral requirements. The certification framework is naturally based on parallel computing techniques to speed up verification tasks.NORTE-01-0145- FEDER-000037

IMPROVING MULTIBANK MEMORY ACCESS PARALLELISM ON SIMT ARCHITECTURES

Memory mapping has traditionally been an important optimization problem for high-performance parallel systems. Today, these issues are increasingly affecting a much wider range of platforms. Several techniques have been presented to solve bank conflicts and reduce memory access latency but none of them turns out to be generally applicable to different application contexts. One of the ambitious goals of this Thesis is to contribute to modelling the problem of the memory mapping in order to find an approach that generalizes on existing conflict-avoiding techniques, supporting a systematic exploration of feasible mapping schemes

Model-driven engineering of cloud components in MetaMORP(h)OSY

Cloud architecture is established as a standard for services provisioning. Discovering of services is a hard task since several services have to be analysed. The mOSAIC project aims at providing a framework for searching and interfacing services from multiple vendors. Its main component, the cloud agency, provides searching and wrapping features and the management of Services Level Agreements (SLAs). In this scenario, validating services requirements is appealing. Usually users are forced to use SLAs pre-offered by providers that need to reconfigure cloud components in order to meet requests. Model-Driven Engineering (MDE) methodologies allow for requirements verification during all life cycle of systems. They can be exploited in definition of models for cloud components that have to be used and configured in order to fulfil requested service levers. Here, the MetaMORP(h)OSY methodology and framework are introduced and applied to the verification of SLAs in the mOSAIC platform

Model-driven engineering of cloud components in MetaMORP(h)OSY

Cloud architecture is established as a standard for services provisioning. Discovering of services is a hard task since several services have to be analysed. The mOSAIC project aims at providing a framework for searching and interfacing services from multiple vendors. Its main component, the cloud agency, provides searching and wrapping features and the management of Services Level Agreements (SLAs). In this scenario, validating services requirements is appealing. Usually users are forced to use SLAs pre-offered by providers that need to reconfigure cloud components in order to meet requests. Model-Driven Engineering (MDE) methodologies allow for requirements verification during all life cycle of systems. They can be exploited in definition of models for cloud components that have to be used and configured in order to fulfil requested service levers. Here, the MetaMORP(h)OSY methodology and framework are introduced and applied to the verification of SLAs in the mOSAIC platform