E-Infrastructures in Armenia: Virtual research environments

Shoukourian, Yuri; Sahakyan, Vladimir; Astsatryan, Hrachya

doi:10.1109/csitechnol.2013.6710360

Cited by 15 publications

(7 citation statements)

References 8 publications

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“…Various workloads as input data have been evaluated based on diverse data node configurations and compression methods using CPU, memory, I/O, network, and job execution time metrics. Computational and storage resources of the IaaS (Infrastructure as a Service) cloud service of the Armenian hybrid research computing platform have been used for the experiments [23,24]. The hardware and software configurations of the experimental environment is described in Table 4.1.…”

Section: Discussionmentioning

confidence: 99%

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

Astsatryan

Lalayan

Kocharyan³

et al. 2021

SCPE

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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.

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Section: Discussionmentioning

confidence: 99%

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

Astsatryan

Lalayan

Kocharyan³

et al. 2021

SCPE

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“…The HPC over cloud layer depends on the IaaS (Infrastructure as a Service) cloud service of the Armenian hybrid research computing platform [9]. The PGAS programming model constructs a global memory address space combining the local memories of VMs of IaaS cloud infrastructure without any memory allocation or pinning.…”

Section: Methodsmentioning

confidence: 99%

Vm Based Evaluation of the Scalable Parallel Minimum Spanning Tree Algorithm for Pgas Model

Bejanyan¹,

Astsatryan²

2021

9th International Conference "Distributed Computing and Grid Technologies in Science and Education"

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The minimum spanning tree problem has influential importance in computer science, networkanalysis, and engineering. However, the sequential algorithms become unable to process the givenproblem as the volume of the data representing graph instances overgrowing. Instead, the highperformance computational resources pursue to simulate large-scale graph instances in a distributedmanner. Generally, the standard shared or distributed memory models like OpenMP and MessagePassing Interface are applied to address the parallelization. Nevertheless, as an emerging alternative,the Partitioned Global Address Space model communicates in the form of asynchronous remoteprocedure calls to access distributed-shared memory, positively affecting the performance usingoverlapping communications and locality-aware structures. The paper presents a modification of theKruskal algorithm for MST problems based on performance and energy-efficiency evaluation relyingon emerging technologies. The algorithm evaluation shows great scalability within the server up to 16vCPU and between the physical servers coupled with a connected weighted graph using differentvertices, edges, and densities.

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“…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

Astsatryan

Narsisian

Kocharyan

et al. 2017

Concurrency and Computation

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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.

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E-Infrastructures in Armenia: Virtual research environments

Cited by 15 publications

References 8 publications

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

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

Vm Based Evaluation of the Scalable Parallel Minimum Spanning Tree Algorithm for Pgas Model

Energy optimization methodology for e‐infrastructure providers

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