Elena Apostol

A solution for the management of multimedia sessions in hybrid clouds

Cloud-based systems expanded considerably in recent years, as the demand for cheaper and easily scalable resources provisioning solutions increased. In this context, the deployment of multimedia services in the cloud, as a way to increase their usability and overcome their processing overhead, gained additional interest. Throughout this paper we present the design of a management system for multimedia services built on top of a hybrid cloud. This describes a model of service unit deployment automation as well as a services composition scheme adapted to the IaaS cloud architecture and resources pool. We propose a modular and flexible architecture to handle service composition, automated service deployment with execution flow management and scalable storage capabilities. Finally we analyse the benefits of using the service management system with a hybrid cloud back-end.

Efficient manager for virtualized resource provisioning in Cloud Systems

Cloud Systems provide the computing infrastructure and on-demand capacity required to host services.

Policy Based Resource Allocation in Cloud Systems

Cloud Systems provide computing resources in a flexible manner. There are several key requirements that need to be addressed regarding the resource allocation in Clouds and the most important of them is providing on demand elasticity. This paper focuses on adding new features to the Cloud resource allocation mechanism that enhance on demand elasticity. Most of the resource managers that are now on the market use static allocation. We propose a novel solution that uses dynamic allocation, based on well defined policies. Moreover, the proposed solution offers authentication and accountability for the actions of the users which is very important for the commercial aspect of public clouds.

The Art of Advanced Healthcare Applications in Big Data and IoT Systems

The goal of this chapter is to analyze existing solutions for self-aware Internet of Things. It will highlight, from a research perspective, the performance and limitations of existing architectures, services and applications specialized on healthcare. The chapter will offer to scientists from academia and designers from industry an overview of the current status of the evolution of applications based on Internet of Things and Big Data. It will also highlight the existing problems and benefits of the IoT for disabled people or people suffering from diseases and the research challenges found in this area.

Integrated Cloud Framework for Farm Management

Smart or intelligent farming is a relatively new concept, but its becoming an important factor for the agricultural sectors, as a way to raise productivity through technology. This article presents a global management and decision system for smart farming. Our system is addressed to farmers and other actors involved in farming operations, such as rural service companies and banks. It can manage up to several groups of smart or technology-based farms. The system is functioning on two levels, local and Cloud. First of all, it must be deployed on the farms local wireless network. And secondly, it uses the Clouds resources and flexibility to offer even more complex services.

Towards a Hybrid Local-Cloud Framework for Smart Farms

Smart or intelligent farming is a relatively new concept, but its becoming an important factor for the agricultural sectors, as a way to raise productivity through technology. This article presents a global management and decision system for smart farming. Our system is addressed to farmers and other actors involved in farming operations, such as rural service companies and banks. It can manage up to several groups of smart or technology-based farms. The system is functioning on two levels, local and Cloud. First of all, it must be deployed on the farms local wireless network. And secondly, it uses the Clouds resources and flexibility to offer even more complex services.

Advanced Service Management and Visualization for Multiple-Cloud Environments

This paper presents a flexible and reliable platform to access and manage services from a multiple-Cloud environment. The proposed platform facilitates user interaction with the resources provided by the Clouds for both service providers and service customers. It performs authentication, virtual resource and service level access management, and it organizes all user interfaces in a single general interface. The proposed solution can be interconnected with any Cloud front-end.

Efficient storage and replication solutions for healthcare applications

EHRs(Electronic Health Records) are widely used by hospitals and clinics to maintain relevant medical information about their patients. Medical institutions moved towards this solution in order to improve the quality of care, by providing easier access to information, at lower costs. However, the downside of EHR is represented by the storage requirements which could include physicians orders, prescriptions, lab results, X-rays, MRIs. In this paper we propose an efficient storage solution by analyzing and taking into consideration the requirements of e-Health applications (such as computational time, storage, processing time, costs) and important aspects of data replication strategies (such as data priority, price, data size).

Real-Time Processing of Heterogeneous Data in Sensor-Based Systems

Nowadays the applications for real time processing of large amounts of data are encountered increasingly more frequently, as there are lots of systems types that can generate large comprehensive information in a relatively short time. In this paper we focus on sensor-based systems. Such systems may be found in several important domains, such as smart farming, medical field, water management, or smart cities. The proposed solution in this paper has the capacity to analyze data streams from different sensors but also considers historical data, in order to provide alerts or invoke different services. This is a new approach, as, to our knowledge, none of the existing stream-processing solutions support combining streaming with batch processing data. We tested our solution with data from sensors and actuators, using a smart farm test scenario.

Forecasting Techniques for Time Series from Sensor Data

Forecasting has always been of interest. Whether ones field is finance, health or seismology, being able to predict future values based on previously gathered data proves to be invaluable when taking decisions concerning the future. In this paper, we research machine learning techniques for predictions on time series and choose the best models that fit our use case, Smart Farms, in which we distributedly analyze time series received from farm-monitoring sensors. On time series with short term dependencies, like temperature or pressure, we make predictions with Hidden Markov Models, whilst for those with long range dependencies, like ground wind speeds orprecipitations, we use Recurrent Neural Networks with Long Short-Term Memory architecture.