Energy-Aware Multilevel Control System for a Network of Linux Software Routers: Design and Implementation

Karpowicz, Michał P.; Arabas, Piotr; Niewiadomska-Szynkiewicz, Ewa

doi:10.1109/jsyst.2015.2489244

Cited by 17 publications

(6 citation statements)

References 40 publications

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“…The goal is to reduce the gap between the capacity provided by distributed computing environments and application requirements, especially during low workload periods. Various efforts are undertaken to develop energy efficient task scheduling and balancing of loads [8][9][10][11] and frequency scaling techniques [12][13][14].…”

Section: Enabling Infrastructures and Middleware For Big-data Modellimentioning

confidence: 99%

Why High-Performance Modelling and Simulation for Big Data Applications Matters

Grelck

Niewiadomska-Szynkiewicz

Aldinucci

et al. 2019

Lecture Notes in Computer Science

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Modelling and Simulation (M&S) offer adequate abstractions to manage the complexity of analysing big data in scientific and engineering domains. Unfortunately, big data problems are often not easily amenable to efficient and effective use of High Performance Computing (HPC) facilities and technologies. Furthermore, M&S communities typically lack the detailed expertise required to exploit the full potential of HPC solutions while HPC specialists may not be fully aware of specific modelling and simulation requirements and applications. The COST Action IC1406 High-Performance Modelling and Simulation for Big Data Applications has created a strategic framework to foster interaction between M&S experts from various application domains on the one hand and HPC experts on the other hand to develop effective solutions for big data applications. One of the tangible outcomes of the COST Action is a collection of case studies from various computing domains. Each case study brought together both HPC and M&S experts, giving witness of the effective cross-pollination facilitated by the COST Action. In this introductory article we argue why joining forces between M&S and HPC communities is both timely in the big data era and crucial for success in many application domains. Moreover, we provide an overview on the state of the art in the various research areas concerned.

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Section: Enabling Infrastructures and Middleware For Big-data Modellimentioning

confidence: 99%

Why High-Performance Modelling and Simulation for Big Data Applications Matters

Grelck

Niewiadomska-Szynkiewicz

Aldinucci

et al. 2019

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

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“…The rapid increase in energy demand, generated by data centers and network infrastructures, may be mitigated on software and hardware levels. According to [56], the following interrelated approaches and solutions can be distinguished:…”

Section: Simulation Frameworkmentioning

confidence: 99%

“…For an overview of recent design trends and a detailed discussion on related technical issues concerned with the energy conservation approaches and solutions, see e.g. [56], [60]- [63]. A commonly used direction is to apply novel measurement technologies and utilize assessment of energy consumption characteristics exposing power management functionality through APIs.…”

Section: Energy-saving Technologiesmentioning

confidence: 99%

“…• cpufreq governor [61], [56], [72]. In [73], a design of a feedback controller for solving the problem of low utilization of servers in a data-center running I/O-intensive applications is proposed.…”

Section: Energy-saving Technologiesmentioning

confidence: 99%

“…An optimized control strategy was developed to meet the workload processing deadlines. Papers [61], [79], and [56] address issues concerned with the structure of optimized energy-aware CPU frequency scaling rules. A class of CPU frequency switching rules, exploiting DVFS, is discussed.…”

Section: Energy-saving Technologiesmentioning

confidence: 99%

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Infrastructure and Energy Conservation in Big Data Computing: A Survey

Niewiadomska-Szynkiewicz

Karpowicz

2019

JTIT

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Progress in life, physical sciences and technology depends on eﬃcient data-mining and modern computing technologies. The rapid growth of data-intensive domains requires a continuous development of new solutions for network infrastructure, servers and storage in order to address Big Datarelated problems. Development of software frameworks, include smart calculation, communication management, data decomposition and allocation algorithms is clearly one of the major technological challenges we are faced with. Reduction in energy consumption is another challenge arising in connection with the development of eﬃcient HPC infrastructures. This paper addresses the vital problem of energy-eﬃcient high performance distributed and parallel computing. An overview of recent technologies for Big Data processing is presented. The attention is focused on the most popular middleware and software platforms. Various energy-saving approaches are presented and discussed as well.

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