Victor Suciu

Big Data, Internet of Things and Cloud Convergence --- An Architecture for Secure E-Health Applications

Big data storage and processing are considered as one of the main applications for cloud computing systems. Furthermore, the development of the Internet of Things (IoT) paradigm has advanced the research on Machine to Machine (M2M) communications and enabled novel tele-monitoring architectures for E-Health applications. However, there is a need for converging current decentralized cloud systems, general software for processing big data and IoT systems. The purpose of this paper is to analyze existing components and methods of securely integrating big data processing with cloud M2M systems based on Remote Telemetry Units (RTUs) and to propose a converged E-Health architecture built on Exalead CloudView, a search based application. Finally, we discuss the main findings of the proposed implementation and future directions.

Cloud consulting: ERP and communication application integration in open source cloud systems

Cloud Consulting combines open source grid computing for distributed cloud computing and Enterprise Resource Modeling (ERP) to provide Infrastructure as a Service (IaaS), Platform as a Service (PaaS) and Software as a Service (SaaS) through a simple, unified API. Cloud communications refers to using internet-based or cloud-based voice and data communications services, where telecommunications applications, switching, and storage are managed generally by third parties. These services can include capabilities that range from Voice over IP (VoIP) communications to hosted PBX and unified communications delivering voice, fax, video, and data requirements. Provisioning for these services is known as Communication as a Service (CaaS).

Big data search for environmental telemetry

This paper presents how Exalead CloudView is used to search for environmental parameters in big data. In particular, given an environmental application, we propose to leverage trivial and non-trivial connections between different sensor signals, in order to find patterns that are likely to provide innovative solutions to existing problems and to establish different data models. The aggregation of such data models will provide evidence of connections between different events and environmental responses to those triggers, faster and better than trivial mining sensors data. As a consequence, the software has a significant potential for matching environmental applications and challenges that are related in non-obvious ways.

M2M remote telemetry and cloud IoT big data processing in viticulture

Current M2M communication platforms are being integrated in cloud IoT applications for providing remote sensing and actuating. Nevertheless, requirements for energy efficiency and resilience in severe operating environments are driving the development of new algorithms and infrastructures. This paper presents a survey of the measurement results for the winegrowing season 2014, as it was seen by an M2M remote telemetry station in cooperation with a big data processing platform and several sensors. We demonstrate the use of recent technologies such as Cloud IoT systems and Big Data processing in order to implement disease prediction and alerting application for viticulture. Finally, the extension of the proposed system for other agriculture applications is discussed.

Big Data, Internet of Things and Cloud Convergence for E-Health Applications

Big data storage and processing are considered as one of the main applications for cloud computing systems. Furthermore, the development of the Internet of Things (IoT) paradigm has advanced the research on Machine to Machine (M2M) communications and enabled novel tele-monitoring architectures for E-Health applications. However, there is a need for converging current decentralized cloud systems, general software for processing big data and IoT systems. The purpose of this paper is to analyze existing components and methods of integrating big data processing with cloud M2M systems based on Remote Telemetry Units (RTUs) and to propose a converged E-Health architecture built on Exalead CloudView, a search based application. Finally, we discuss the main findings of the proposed implementation and future directions.

Big data fusion for eHealth and Ambient Assisted Living Cloud Applications

Senior citizens may suffer from a number of diseases, including but not limited to mild dementia and cognitive disabilities, which require either the institutionalization or the constant support from care-givers. To successfully help the senior citizens, usually a monitoring system is needed that can gather and process data from various sensors about the healthcare condition of the user and environment parameters (noise level, light, humidity and temperature). In this paper we describe a cloud-based approach for monitoring the healthcare condition of senior citizens and the fusion of big data from heterogeneous information flows coming from the sensors. Furthermore, because context understanding is not easily done with a single source of metadata, we analyze metadata available from various online sources, aiming to understand the context of its users, in order to offer them personalized eHealth services.

Dynamically reconfigurable workflows for time-critical applications

Cloud-based applications that depend on time-critical data processing or network throughput require the capability of reconfiguring their infrastructure on demand as and when conditions change. Although the ability to apply quality of service constraints on the current Cloud offering is limited, there are ongoing efforts to change this. One such effort is the European funded SWITCH project that aims to provide a programming model and toolkit to help programmers specify quality of service and quality of experience metrics of their distributed application and to provide the means to specify the reconfiguration actions which can be taken to maintain these requirements. In this paper, we present an approach to application reconfiguration by applying a workflow methodology to implement a prototype involving multiple reconfiguration scenarios of a distributed real-time social media analysis application, called Sentinel. We show that by using a lightweight RPC-based workflow approach, we can monitor a live application in real time and spawn dependency-based workflows to reconfigure the underlying Docker containers that implement the distributed components of the application. We propose to use this prototype as the basis for part of the SWITCH workbench, which will support more advanced programmable infrastructures.

Network management and monitoring for cloud systems

Monitoring represents an important factor in improving the quality of the services provided in cloud computing, given the fact that it allows scaling resource utilization in an adaptive manner. It is widely used for detecting critical events and abnormalities of distributed systems and also it helps identifying the faults within the system, discovering application patterns for the users. As cloud systems increase their architecture, the degree of workload also grows in datacenters, causing node failures and performance issues. This paper aims to provide a solution for the monitoring of cloud computing systems and services, allowing users and also providers to optimize the usage of the computational resources according to the constantly changing business requirements inside an organization. The main contribution of the paper consists of the integration of the monitoring system, which is based on Nagios and NConf with a test cloud architecture. Finally, the paper discusses the main findings for a reference implementation using the OpenStack cloud platform.

Rapid detection of pesticide residues based on telemetry platform

Pesticides are very important in improving agricultural production, but can harm beneficial insect species, soil, air, water, plants and their fruits. The purpose of this paper is to describe a telemetry solution together with a novel generation of immunobiosensors that will be used in order to gather data to a platform for remote monitoring of pesticide residues. The paper is an overview of the techniques and interfaces proposed for rapid detection of pesticide residues. LabVIEW is the preferred solution to create the graphical interface in order to insure the visualization of data. Furthermore, the platform can be applied for tele-monitoring pesticides in an agricultural production by performing a qualitative and quantitative assessment of pesticides. The paper fills a gap in the biosensors domain literature by proposing a solution based on data from immunobiosensors for rapid detection of pesticide through a telemetry platform.

Cloud Computing for Extracting Price Knowledge from Big Data

Customer price knowledge has been the object of considerable research in the past decades since the advent of online shopping. Furthermore, customers express online their personal opinions regarding the products or services they purchase, this activity becoming a habit for many people nowadays. However, due to the difficulty of analyzing such large datasets, extracting price knowledge from big data presents unique systems engineering and architectural challenges. The purpose of this paper is to analyze several existing solutions used for search and analysis of large volumes of data, with applicability in the retail field, and to present the results for price knowledge extraction from Big Data using Exalead Cloud View technology. The main contribution of this paper consists in the development of several connectors and a data model based on properties and patterns specific for price calculations.