Energy optimization methodology for e‐infrastructure providers

Abstract

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.

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

“…It is planned to study and develop algorithms for distributed large graphs in the PGAS model considering chunk-sizes, communication costs and network optimizations [10], as well as to develop graph algorithms for centrality, shortest paths, and link analysis using emerging distributed programming languages and HPC technologies like Chapel, InfiniBand or Remote Direct Memory Access [11].…”

Performance Optimization System for Hadoop and Spark Frameworks

“…Meanwhile, Spark achieves the best performance with Iz4 only for 4 GB input data, and zstandard codec for 8 GB and 16 GB cases. It is planned to study the energy-efficient data transfers of Apache Hadoop and Spark using RDMA-capable networks like InfiniBand based on the developed methodology [27] and techniques [28].…”

SaaS for Energy Efficient Utilization of HPC Resources of Linear Algebra Calculations

“…For instance, the paper [7] analyzes the impact on power consumption of two DVFS-control strategies when applied to the execution of dense linear algebra operations on multi-core processors. Another example is to adopt DVFS method to decrease a power consumption of an application without affecting the performance of HPC resources [8,9]. Reduces clock frequencies for MPI ranks that lack computational work, this technique showed an average measured energy savings of 10.6% and a most of 21.0% over regular application runs [10].…”