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Earth observation (EO) satellite data is essential to environmental monitoring. At a national and regional level, the open data cubes harness the power of satellite data by providing application programming interfaces and services to the end-users. The volume and the complexity of satellite observations are increasing, demanding novel approaches for data storing, managing, and processing. High-performance computing (HPC) and cloud platforms may improve Big EO data processing performance. However, it is necessary to consider several vital aspects for efficient and flexible EO data processing, such as the interoperability from cloud-HPC and EO data repositories, automatic provisioning and scaling of cloud-HPC resources, cost-effectiveness, support of new EO data formats and open-source packages, or linkage with data cube platforms. The article proposes a scalable EO data processing platform interoperable from cloud-HPC and EO data repositories. The platform enables linking any data repository supporting web coverage service or SpatioTemporal Asset Catalog Application Programming Interfaces (STAC-API), and any cloud or HPC resource supporting scheduling system API for providing access to the cluster backends. [ABSTRACT FROM AUTHOR]

The MapReduce framework manages Big Data sets by splitting the large datasets into a set of distributed blocks and processes them in parallel. Data compression and in-memory file systems are widely used methods in Big Data processing to reduce resource-intensive I/O operations and improve I/O rate correspondingly. The article presents a performance-efficient modular and configurable decision-making robust service relying on data compression and in-memory data storage indicators. The service consists of Recommendation and Prediction modules, predicts the execution time of a given job based on metrics, and recommends the best configuration parameters to improve Hadoop and Spark frameworks' performance. Several CPU and dataintensive applications and micro-benchmarks have been evaluated to improve the performance, including Log Analyzer, WordCount, and K-Means. [ABSTRACT FROM AUTHOR]

The article aims to introduce an integrated web-based interactive data platform for molecular dynamic simulations using the datasets generated by different life science communities from Armenia. The suggested platform, consisting of data repository and workflow management services, is vital for current and future scientific discoveries in the life science domain. We focus on interactive data visualization workflow service as a key to perform more in-depth analyzes of research data outputs, helping to understand the problems efficiently and to consolidate the data into one collective illustration platform. The functionalities of the integrated data platform is presented as an advanced integrated environment to capture, analyze, process and visualize the scientific data. [ABSTRACT FROM AUTHOR]

This article aims to present a web-based interactive visualization and analytical platform for weather data in Armenia by integrating the three existing infrastructures for observational data, numerical weather prediction, and satellite image processing. The weather data used in the platform consists of near-surface atmospheric elements including air temperature, pressure, relative humidity, wind and precipitation. The visualization and analytical platform has been implemented for 2-m surface temperature. The platform gives Armenian State Hydrometeorological and Monitoring Service analytical capabilities to analyze the in-situ observations, model and satellite image data per station and region for a given period. [ABSTRACT FROM AUTHOR]

The most important factor of High performance computing (HPC) systems nowadays is to limit or decrease the power consumption while preserving a high utilization. And with the availability of alternative energy, which powers such systems, there is a need to maximize the usage of alternative energy over brown power. For now, the usage of alternative energy is varying in time due to different factors such as sunny days, the wind, etc. and it is crucial to have an energy-aware algorithm to maximize the usage of this energy. In this paper a SaaS service is presented to optimize a usage of alternative energy, to reduce the power consumption and to preserve a best possible percentage of resource utilization. [ABSTRACT FROM AUTHOR]