Cloud Service for Numerical Calculations and Visualizations of Photonic Dissipative Systems

Grigoryan, Hayk; Astsatryan, Hrachya; Gevorgyan, T. V.; Manukyan, V. A.

doi:10.1515/cait-2017-0058

Cited by 2 publications

(4 citation statements)

References 22 publications

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“…Different nowcasting methodologies based on artificial intelligence and the utilization of satellite imagery will be implemented for the development of a hazardous hydro-meteorological phenomena alarm system. The extended platform will be integrated with the available cloud services [15,16,17] by providing access to the required specialized climatic data.…”

Section: Discussionmentioning

confidence: 99%

Weather Data Visualization and Analytical Platform

Astsatryan

Grogoryan

Gyulgyulyan

et al. 2018

SCPE

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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.

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Section: Discussionmentioning

confidence: 99%

Weather Data Visualization and Analytical Platform

Astsatryan

Grogoryan

Gyulgyulyan

et al. 2018

SCPE

Self Cite

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“…The provided solutions which include 2 decision algorithms for achieving optimal or near-optimal solutions for multi-site offloading with regard to the size of the application. The heterogeneity of a system has an important role in the determination of the dynamics of strategy-proof mechanisms [5] initiates with a PSO, and if the PSO failed to find the global optima, then, the ACO will strive to address the drawbacks of PSO.…”

Section: Hybrid Swarm Algorithms Used In a Cloud Environmentmentioning

confidence: 99%

“…The ACO metaheuristic algorithm is described in three basic steps. Reviewing the characteristics for ACO, it becomes obvious that artificial ants build solutions out of a finite set of possible partial solutions [5,22]. The starting point is an empty partial solution, which is extended to a feasible solution for each iteration step.…”

Section: The Metaheuristic Search (Aco and Pso) Algorithmsmentioning

confidence: 99%

“…The optimizer, with the aid of the developed cost model and runtime constraints, will generate the minimum energy configuration of cloud computing nodes while ensuring that the performance is guaranteed. The three key areas of energy, performance, and reliability will be covered by the cost functions [3][4][5]. A decrease in the number of active servers was evidenced through simulation to have reduced power consumption by 42 %.…”

Section: Introductionmentioning

confidence: 99%

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HSO: A Hybrid Swarm Optimization Algorithm for Reducing Energy Consumption in the Cloudlets

et al. 2018

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Mobile Cloud Computing (MCC) is an emerging technology for the improvement of mobile service quality. MCC resources are dynamically allocated to the users who pay for the resources based on their needs. The drawback of this process is that it is prone to failure and demands a high energy input. Resource providers mainly focus on resource performance and utilization with more consideration on the constraints of service level agreement (SLA). Resource performance can be achieved through virtualization techniques which facilitates the sharing of resource providers' information between different virtual machines. To address these issues, this study sets forth a novel algorithm (HSO) that optimized energy efficiency resource management in the cloud; the process of the proposed method involves the use of the developed cost and runtime-effective model to create a minimum energy configuration of the cloud compute nodes while guaranteeing the maintenance of all minimum performances. The cost functions will cover energy, performance and reliability concerns. With the proposed model, the performance of the Hybrid swarm algorithm was significantly increased, as observed by optimizing the number of tasks through simulation, (power consumption was reduced by 42%). The simulation studies also showed a reduction in the number of required calculations by about 20% by the inclusion of the presented algorithms compared to the traditional static approach. There was also a decrease in the node loss which allowed the optimization algorithm to achieve a minimal overhead on cloud compute resources while still saving energy significantly. Conclusively, an energy-aware optimization model which describes the required system constraints was presented in this study, and a further proposal for techniques to determine the best overall solution was also made.

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Cloud Service for Numerical Calculations and Visualizations of Photonic Dissipative Systems

Cited by 2 publications

References 22 publications

Weather Data Visualization and Analytical Platform

Weather Data Visualization and Analytical Platform

HSO: A Hybrid Swarm Optimization Algorithm for Reducing Energy Consumption in the Cloudlets

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