Research on the Architecture and its Implementation for Instrumentation and Measurement Cloud

Cloud computing has brought a new method of resource utilization and management. Nowadays some researchers are working on cloud-based instrumentation and measurement systems designated as Instrumentation and Measurement Clouds (IMCs). However, until now, no standard definition or detailed architecture with an implemented system for IMC has been presented. This paper adopts the philosophy of cloud computing and brings forward a relatively standard definition and a novel architecture for IMC. The architecture inherits many key features of cloud computing, such as service provision on demand, scalability and so on, for remote Instrumentation and Measurement (IM) resource utilization and management. In the architecture, instruments and sensors are virtualized into abstracted resources, and commonly used IM functions are wrapped into services. Users can use these resources and services on demand remotely. Platforms implemented under such architecture can reduce the investment for building IM systems greatly, enable remote sharing of IM resources, increase utilization efficiency of various resources, and facilitate processing and analyzing of Big Data from instruments and sensors. Practical systems with a typical application are implemented upon the architecture. Results demonstrate that the novel IMC architecture can provide a new effective and efficient framework for establishing IM systems

On the Virtualization of CUDA Based GPU Remoting on ARM and X86 Machines in the GVirtuS Framework

The astonishing development of diverse and different hardware platforms is twofold: on one side, the challenge for the exascale performance for big data processing and management; on the other side, the mobile and embedded devices for data collection and human machine interaction. This drove to a highly hierarchical evolution of programming models. GVirtuS is the general virtualization system developed in 2009 and firstly introduced in 2010 enabling a completely transparent layer among GPUs and VMs. This paper shows the latest achievements and developments of GVirtuS, now supporting CUDA 6.5, memory management and scheduling. Thanks to the new and improved remoting capabilities, GVirtus now enables GPU sharing among physical and virtual machines based on x86 and ARM CPUs on local workstations, computing clusters and distributed cloud appliances

A Survey on Mobile Crowdsensing Systems: Challenges, Solutions, and Opportunities

Mobile crowdsensing (MCS) has gained significant attention in recent years and has become an appealing paradigm for urban sensing. For data collection, MCS systems rely on contribution from mobile devices of a large number of participants or a crowd. Smartphones, tablets, and wearable devices are deployed widely and already equipped with a rich set of sensors, making them an excellent source of information. Mobility and intelligence of humans guarantee higher coverage and better context awareness if compared to traditional sensor networks. At the same time, individuals may be reluctant to share data for privacy concerns. For this reason, MCS frameworks are specifically designed to include incentive mechanisms and address privacy concerns. Despite the growing interest in the research community, MCS solutions need a deeper investigation and categorization on many aspects that span from sensing and communication to system management and data storage. In this paper, we take the research on MCS a step further by presenting a survey on existing works in the domain and propose a detailed taxonomy to shed light on the current landscape and classify applications, methodologies, and architectures. Our objective is not only to analyze and consolidate past research but also to outline potential future research directions and synergies with other research areas

SIaaS-Sensing Instrument as a Service Using Cloud Computing to Turn Physical Instrument into Ubiquitous Service

Sensing Instrument as a Service (SIaaS) is a new cloud service that allows users to use data acquisition instruments shared through cloud infrastructure. It offers a common interface to managing physical sensing instrument and it permits to take all advantages of using cloud computing technology in storing and processing acquired data. With SIaaS different research groups could share physical instrument, in a controlled way, event if they are geographically distributed and user can access to them using an internet connected device without the need to install any sort of program. With the interaction of different own framework we have created a new cloud service for transforming physical resources in a ubiquitous service