Practical Implementation of New Algorithm for Restricting Data Fusion in Cloud Computing with Use of Information Kalman Filtering

These days’ lots of technologies migrate from traditional systems into cloud and similar technologies; also we should note that cloud can be used for military and civilian purposes [3]. On the other hand, in such a large scale networks we should consider the reliability and powerfulness of such networks in facing with events such as high amount of users that may login to their profiles simultaneously, or for example if we have the ability to predict about what times that we would have the most crowd in network, or even users prefer to use which part of the Cloud Computing more than other parts – which software or hardware configuration. With knowing such information, we can avoid accidental crashing or hanging of the network that may be cause by logging of too much users. In this paper we propose Kalman Filter that can be used for estimating the amounts of users and software’s that run on cloud computing or other similar platforms at a certain time. After introducing this filter, at the end of paper, we talk about some potentials of this filter in cloud computing platform. In this paper we demonstrate about how we can use Kalman filter in estimating and predicting of our target, by the means of several examples on Kalman filter. Also at the end of paper we propose information filter for estimation and prediction about cloud computing resources.

Modeling of Groundwater Potential Using Cloud Computing Platform: A Case Study from Nineveh Plain, Northern Iraq

Knowledge of the groundwater potential, especially in an arid region, can play a major role in planning the sustainable management of groundwater resources. In this study, nine machine learning (ML) algorithms—namely, Artificial Neural Network (ANN), Decision Jungle (DJ), Averaged Perceptron (AP), Bayes Point Machine (BPM), Decision Forest (DF), Locally-Deep Support Vector Machine (LD-SVM), Boosted Decision Tree (BDT), Logistic Regression (LG), and Support Vector Machine (SVM)—were run on the Microsoft Azure cloud computing platform to model the groundwater potential. We investigated the relationship between 512 operating boreholes with a specified specific capacity and 14 groundwater-influencing occurrence factors. The unconfined aquifer in the Nineveh plain, Mosul Governorate, northern Iraq, was used as a case study. The groundwater-influencing factors used included elevation, slope, curvature, topographic wetness index, stream power index, soil, land use/land cover (LULC), geology, drainage density, aquifer saturated thickness, aquifer hydraulic conductivity, aquifer specific yield, depth to groundwater, distance to faults, and fault density. Analysis of the contribution of these factors in groundwater potential using information gain ratio indicated that aquifer saturated thickness, rainfall, hydraulic conductivity, depth to groundwater, specific yield, and elevation were the most important factors (average merit > 0.1), followed by geology, fault density, drainage density, soil, LULC, and distance to faults (average merit < 0.1). The average merits for the remaining factors were zero, and thus, these factors were removed from the analysis. When the selected ML classifiers were used to estimate groundwater potential in the Azure cloud computing environment, the DJ and BDT models performed the best in terms of all statistical error measures used (accuracy, precision, recall, F-score, and area under the receiver operating characteristics curve), followed by DF and LD-SVM. The probability of groundwater potential from these algorithms was mapped and visualized into five groundwater potential zones: very low, low, moderate, high, and very high, which correspond to the northern (very low to low), southern (moderate), and middle (high to very high) portions of the study area. Using a cloud computing service provides an improved platform for quickly and cheaply running and testing different algorithms for predicting groundwater potential.

The Swiss data cube, analysis ready data archive using earth observations of Switzerland

Since the opening of Earth Observation (EO) archives (USGS/NASA Landsat and EC/ESA Sentinels), large collections of EO data are freely available, offering scientists new possibilities to better understand and quantify environmental changes. Fully exploiting these satellite EO data will require new approaches for their acquisition, management, distribution, and analysis. Given rapid environmental changes and the emergence of big data, innovative solutions are needed to support policy frameworks and related actions toward sustainable development. Here we present the Swiss Data Cube (SDC), unleashing the information power of Big Earth Data for monitoring the environment, providing Analysis Ready Data over the geographic extent of Switzerland since 1984, which is updated on a daily basis. Based on a cloud-computing platform allowing to access, visualize and analyse optical (Sentinel-2; Landsat 5, 7, 8) and radar (Sentinel-1) imagery, the SDC minimizes the time and knowledge required for environmental analyses, by offering consistent calibrated and spatially co-registered satellite observations. SDC derived analysis ready data supports generation of environmental information, allowing to inform a variety of environmental policies with unprecedented timeliness and quality.

Research on CarMaker and Cloud Computing Platform Co-Simulation Based on APO

An cloud computing platform based on B/S architecture and docker container technology for autonomous driving simulation has been established in this paper. The map editor module of the cloud platform lets users design 3D scenes for simulating and testing automated driving systems. When the customized roadway scene for simulation created, it would be saved as OpenDrive format both for the server of cloud platform and CarMaker’s TestRun which all parameters of the virtual environment (vehicle, road, tires, etc.) are sufficiently defined. Then, based on the application online (APO) communication protocol of CarMaker, the local APO agent service was created. When the 27 parameters of vehicle dynamics received from CarMaker server, they were sent to the cloud platform in real time through UPD protocol. The process of data communication is completed by APO agent. Through the work above, a co-simulation between cloud platform and CarMaker could be successfully established for autonomous driving with seventeen-degree-of-freedom. Through the co-simulation experiment, it is found that the real-time data sampling frequency of the co-simulation is 70Hz, which completes the synchronous simulation of carmaker and cloud platform.

Application of IoT technology to collect data for integrated power system

In this paper, the authors present the design of a data collection system of an integrated power system including small-scale wind and solar power on Haiwell's IoT application platform. The proposed research system includes 02 digital power meters connecting with PLC via Modbus RTU standard and PLC communicating with Wifi integrated HMI screen via ZigBee wireless communication technology. This system can be easily installed into electrical cabinets to collect electrical information such as power consumption, voltage, current, power, and frequency of the system to be monitored. The application of Haiwell's free cloud computing platform will be very convenient in designing the interface for the on-site monitoring system via HMI or accessible via computer or smartphone. With the actual results of the project, it has been shown that this is one of the solutions that can be deployed at factories and is very convenient in construction and installation because it uses the high-quality ZigBee wireless communication standard.

An educational toolkit for teaching cloud computing

In an educational context, experimenting with a real cloud computing platform is very important to let students understand the core concepts, methodologies and technologies of cloud computing. However, API heterogeneity of cloud providers complicates the experimentation by forcing students to focus on the use of different APIs, and by hindering the jointly use of different platforms. In this paper, we present EasyCloud, a toolkit enabling the easy and effective use of different cloud platforms. In particular, we describe its features, architecture, scalability, and use in our cloud computing courses, as well as the pedagogical insights we learnt over the years.