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International audience; The environmental protection is a dominant concern for all types of industries, organizations, and governments. In this regard, the reduction of the energy consumption is substantial in bringing down the CO2 gas emission, which is considered as an important factor causing global warming. The e-infrastructure service providers, such as National Research and Education Networks or National Grid Initiatives have crucial role in the context of energy awareness because the energy consumption of the networking, data, and computational infrastructures keeps increasing exponentially over the time. In addition to this, scientific gateways and cloud services are becoming more significant to tackle scientific and societal challenges. Therefore, there is a need to provide robust and reliable services taking into account energy consumption aspect of e-infrastructures. The aim of the article is to introduce an energy optimization methodology for the beneficiaries of the e-infrastructures to explore, optimize, and report the energy consumption and CO2 emission of data, computing, and networking facilities. The suggested methodology has been implemented within the Armenian e-infrastructure aiming at the reduction of the energy consumption and thereby the CO2 emission.

Proceedings of: Third International Workshop on Sustainable Ultrascale Computing Systems (NESUS 2016). Sofia (Bulgaria), October, 6-7, 2016. The power consumption of data centers is becoming a crucial challenge in the context of the steadily increasing demand for computation. In this regard finding a way to improve energy efficiency of running applications in data centers is becoming a crucial trend. One method to improve the processor utilization is the consolidation of applications on physical servers. It is possible to run multiple jobs in parallel on the same machine, especially when their requirements regarding computation are smaller than the maximum processor performance. It reduces the number of servers in the data center required to handle multiple requests and therefore leads to energy usage reductions. In this paper, we introduce a realistic model of applications with deadlines executed in parallel on a server and competing for the shared resources and present an energy-aware algorithm which may be used to minimize the overall energy consumption of the servers. European Community's Seventh Framework Program This work is partially supported by EU under the COST Program Action 1305: Network for Sustainable Ultrascale Computing (NESUS). The research presented in this paper is partially funded by a grant from Polish National Science Center under award number 2013/08/A/ST6/00296. This research was supported by the EU Seventh Framework Programme FP7/2007–2013 under grant agreement no. FP7-ICT-2013-10 (609757).

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.

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.

The environmental protection is a dominant concern for all types of industries, organizations, and governments. In this regard, the reduction of the energy consumption is substantial in bringing down the CO2 gas emission, which is considered as an important factor causing global warming. The e‐infrastructure service providers, such as National Research and Education Networks or National Grid Initiatives have crucial role in the context of energy awareness because the energy consumption of the networking, data, and computational infrastructures keeps increasing exponentially over the time. In addition to this, scientific gateways and cloud services are becoming more significant to tackle scientific and societal challenges. Therefore, there is a need to provide robust and reliable services taking into account energy consumption aspect of e‐infrastructures. The aim of the article is to introduce an energy optimization methodology for the beneficiaries of the e‐infrastructures to explore, optimize, and report the energy consumption and CO2 emission of data, computing, and networking facilities. The suggested methodology has been implemented within the Armenian e‐infrastructure aiming at the reduction of the energy consumption and thereby the CO2 emission.

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.

Earth Science community depends on the exploration, analysis and reprocessing of high volumes of data as well as the modeling and simulation of complex coupled systems on multiple scales. The main aim of this article is to introduce a new hydrological modeling service based on the Soil and Water Assessment Tool (SWAT) (Arnold et al. J American Water Resour Assoc 34(1), 73–89, 1998 ; Arnold and Fohrer Hydrol Process 19(3), 563–572, 2005) model using high efficiency, resource sharing and low cost cloud computing resources (Astsatryan et al. International Journal of Scientific & Engineering Research 1(1), 1130–1133, 2014). Such a Desktop as a Service (DaaS) approach allowing users to work from anywhere, and gives centralized desktop management and great performance. Within the Spatial Data Infrastructure (SDI) and cloud platform, the DaaS service gives secure access to the model and a centralized data storage to get a SWAT model input. The article illustrates the analyses of the implementation of the SWAT model for the Sotk watershed of Lake Sevan in Armenia (Sargsyan 2007).

The main objective of this paper is to introduce a portal of geoprocessing services that can be used to compute either a single vegetation index or a combination of vegetation indices, as a workflow. High Performance Computing (HPC) resources are used for the calculations, and the Web Processing Service (WPS) standard is used to handle the requests from and the responses to the portal. In case of a workflow, a single node of the cluster is dedicated to each index, and the number of used cores depends on the complexity of the task. In addition, based on a series of experiments made to accelerate remote sensing image processing, a parallelization method within the computational node is automatically chosen depending on the complexity of the operations and the amount of data. The suggested algorithm optimizes the processing by selecting the best methodology (serial or parallel) and the number of cores to efficiently manipulate and distribute the data. The interoperable web portal, Spatial Data Infrastructure (SDI) and the heterogeneous resources of HPC cluster are located in the same local area network, and the cluster nodes have access to the data via network file system sharing. The use of standardized web services makes it possible to use remote data as inputs.

Processing of high-resolution time series satellite images typically requires a large amount of computational resources and time. We introduce here a scientific gateway for computing the Normalized Difference Vegetation Index (NDVI) time series data. Based on a distributed workflow using the Web Processing Service (WPS) standard, the gateway aims to be completely interoperable with other standardized tools. The availability of this gateway may help researchers to acquire knowledge of land cover changes more efficiently over very large spatial and temporal extents, which is especially important in the context of Armenia for which timely decisionmaking is needed.