Middleware architecture for data consistency in multi-cloud systems

Multi-cloud middleware must perform many different resource management, control, and monitoring functions that must interoperate but may differ in implementation in each cloud service provider. A mechanism for monotonic recording model implementation for multi-cloud systems with a geo-distributed middleware architecture is proposed in the article. It is shown, the middleware modules location defines the algorithm of synchronization of start moments of adjusting intervals required to generating the global sequence numbers for customer's data recording into the databases of multi-cloud systems. Figs.: 2. Tabl.: 1. Refs.: 10 titles. Keywords: middleware architecture, geo-distributed middleware architecture, multicloud systems.

In-Network Convolution in Grid Shaped Sensor Networks

Gathering information is the primary purpose of a Sensor Network. The task is performed by spatially distributed nodes equipped with sensing, processing, and communication capabilities. However, data gathered from a sensor network must be processed, and often the collective computation capability of nodes forming the sensor network is neglected in favor of data processing on cloud systems. Nowadays, Edge Computing has emerged as a new paradigm aiming to migrate data processing close to data sources. In this contribution, we focus on the development of a sensor network designed to detect a person’s fall. We named this sensor network the smart floor. Fall detection is tackled with a Convolutional Neural Network, and we propose an approach for in-network processing of convolution layers on grid-shaped sensor networks. The proposed approach could lead to the development of a sensor network that detects falls by performing CNN inference processing on the edge. We complement our work with a simulation using the simulator ns-3. The simulation is designed to emulate the communication overhead of the proposed approach applied to a wired sensor network that resembles the smart floor. Simulation results provide evidence on the feasibility of the proposed concept applied to wired grid shaped sensor networks.

NEVONEX—The Importance of Middleware and Interfaces for the Digital Transformation of Agriculture

For a “system of systems” approach in agriculture, agricultural machinery is facing the challenge of bringing tractor-implement combinations and harvesting machinery to the IoT (Internet of Things). While standards such as the ISO 11783 (ISOBUS) have enabled seamless tractor-implement communications, the manufacturer-independent communication of tractors, implements and harvesting machinery with external sensors and different Cloud systems/services are still lacking. IoT ecosystems for agricultural machinery are gaining ground in the market to solve these problems. In this regard, an overview is given about the role of interfaces and middleware and how they can help to improve data flow, improve connectivity, compatibility and interoperability of digital products that farmers use.

A Two Stage Task Scheduler for Effective Load Optimization in Cloud – FoG Architectures

In recent times, computing technologies have moved over to a new dimension with the advent of cloud platforms which provide seamless rendering of required services to consumers either in static or dynamic state. In addition, the nature of data being handled in today’s scenario has also become sophisticated as mostly real time data acquisition systems equipped with High-Definition capture (HD) have become common. Lately, cloud systems have also become prone to computing overheads owing to huge volume of data being imparted on them especially in real time applications. To assist and simplify the computational complexity of cloud systems, FoG platforms are being integrated into cloud interfaces to streamline and provide computing at the edge nodes rather at the cloud core processors, thus accounting for reduction of load overhead on cloud core processors. This research paper proposes a Two Stage Load Optimizer (TSLO) implemented as a double stage optimizer with one being deployed at FoG level and the other at the Cloud level. The computational complexity analysis is extensively done and compared with existing benchmark methods and superior performance of the suggested method is observed and reported.

Synergistic radar and sub-millimeter radiometer retrievals of ice hydrometeors in mid-latitude frontal cloud systems

Accurate measurements of ice hydrometeors are required to improve the representation of clouds and precipitation in weather and climate models. In this study, a newly developed, synergistic retrieval algorithm that combines radar with passive millimeter and sub-millimeter observations is applied to observations of three frontally-generated, mid-latitude cloud systems in order to validate the retrieval and asses its capabilities to constrain the properties of ice hydrometeors. To account for uncertainty in the assumed shapes of ice particles, the retrieval is run multiple times while the assumed shape is varied. Good agreement with in situ measurements of ice water content and particle concentrations for particle maximum diameters larger than 200 μm is found for one of the flights for the Large Plate Aggregate and the 6-Bullet Rosette shapes. The variational retrieval fits the observations well although small systematic deviations are observed for some of the sub-millimeter pointing towards issues with the sensor calibration or the modeling of gas absorption. We find that the quality of the fit to the observations is independent of the assumed ice particle shape, indicating that the employed combination of observations is insufficient to constrain the shape of ice particles in the observed clouds. Compared to a radar-only retrieval, the results show an improved sensitivity of the synergistic retrieval to the microphysical properties of ice hydrometeors at the base of the cloud. Our findings indicate that the synergy between active and passive microwave observations improve remote-sensing measurements of ice hydrometeors and may thus help to reduce uncertainties that affect currently available data products. Due to the increased sensitivity to their microphysical properties, the retrieval may also be a valuable tool to study ice hydrometeors in field campaigns. The good fits obtained to the observations increases confidence in the modeling of clouds in the Atmospheric Radiative Transfer Simulator and the corresponding single scattering database, which were used to implement the retrieval forward model. Our results demonstrate the suitability of these tools to produce realistic simulations for upcoming sub-millimeter sensors such as the Ice Cloud Image or the Arctic Weather Satellite.