Strengthening compute and data intensive capacities of Armenia

Lalayan

Kocharyan³

et al. 2021

SCPE

Self Cite

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 data-intensive applications and micro-benchmarks have been evaluated to improve the performance, including Log Analyzer, WordCount, and K-Means.

Section: Discussionmentioning

confidence: 99%

Performance-efficient Recommendation and Prediction Service for Big Data frameworks focusing on Data Compression and In-memory Data Storage Indicators

Lalayan

Kocharyan³

et al. 2021

SCPE

Self Cite

“…The core computational infrastructure is the Armenian National Grid Initiative, which is a national effort to establish a nationwide grid environment for computational science and research . Now, the computational resources (about 500 cores) of Armenian Grid infrastructure distributed among our leading research (National Academy of Sciences, Yerevan Physics Institute) and academic (Yerevan State University, State Engineering University of Armenia) organizations are located in the cities of Yerevan and Ashtarak (see Figure ).…”

Section: Methodsmentioning

confidence: 99%

Energy optimization methodology for e‐infrastructure providers

Concurrency and Computation

Narsisian

Kocharyan

et al. 2017

Self Cite

Summary 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.

“…All this help to produce more accurate and reliable results and outcomes, which can confirm real experiments or provide a proper and deep understanding of several processes. With the availability of HPC resources and different cloud services [24,25], the biologists in Armenia can run complex workflows that integrate programs, methods, and data from various resources and run different simulations in a single consolidated platform. Using different scientific computing methods with the support of scientific discovery development, a new area of scientific methods arise with new data analysis strategies enabled which are called e-science paradigm.…”

Section: Data Reference and Data Sharingmentioning

confidence: 99%

An Integrated Web-based Interactive Data Platform for Molecular Dynamics Simulations

Narsisian

Gyulgyulyan

et al. 2018

SCPE

Self Cite

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.