A Review on Dynamic Virtual Machine Consolidation Approaches for Energy-Efficient Cloud Data Centers

Prabha, B.; Ramesh, K.; Renjith, P. N.

doi:10.1007/978-981-15-8530-2_60

Cited by 12 publications

(3 citation statements)

References 42 publications

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“…Aspek pengelolaan berarti pengguna bisa melakukan apa saja pada virtual server tersebut tanpa adanya campur tangan dari pihak lain (Almurayh, 2010). Virtual server sendiri akan bekerja secara optimal jika didukung oleh webserver yang tepat, beberapa webserver yang sering digunakan adalah N-nginx, N-ginx merupakan sebuah perangkat lunak yang memiliki fungsi utama sebagai sebuah web server, selain digunakan sebagai web server juga memiliki fitur untuk digunakan sebagai reverse proxy, HTTP cache, dan load balancer (Chandra, 2019) Alasan penggunaan virtualisasi diantaranya adalah menghemat pengeluaran uang, optimalisasi sumber daya, penurunan konsumsi daya dan konsolidasi pusat data, mengurangi pekerjaan administrasi sistem (Prabha et al, 2021). VPS merupakan virtual server yang dipandang sebagai dedicated/private server bagi pengguna meskipun server tersebut di install pada sebuah server fisik (Gröber et al, 2023;Westfall & Westfall, 2021) yang menjalankan lebih dari satu sistem operasi pada saat yang sama (Balen et al, 2020).…”

Section: Studi Pustakaunclassified

Development of Smart Servers for Informatics Education Program Using NDLC Method

Jamaluddin,

Muhammad Zamroni Uska,

Wirasasmita

et al. 2024

JITE

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The development of processor technology, especially Multicore Processors, has had a significant impact in various fields, including the field of higher education, one of which is the need for Virtual Private Servers for information management and their potential as a storage space for web development products. This research aims to create a Virtual Private Server in the Informatics Education program at the Faculty of Mathematics and Natural Sciences of Hamzanwadi University. It uses the Research & Development (R&D) method with the Network Development Life Cycle (NDLC) approach, involving six stages: Analysis, Design, Prototype Simulation, Implementation, Monitoring, and Management. Data were obtained through interviews and questionnaires, then analyzed descriptively qualitatively. The research resulted in a Virtual Private Server using Proxmox VE 7.4, Ubuntu 18.04.5 operating system, remote access via OpenSSH-Server, Control Panel AAPanel 6.8.30, Nginx 1.21.4 web server, MySQL 5.7.34 database management system, PHP 7.4, PhpMyAdmin 5.0 for web access to the database, and PureFTPd 1.1.49 as the FTP Server. Expert evaluations in the field of IT yielded a rating of 83% with the category "Highly Suitable," indicating that the Virtual Private Server product is suitable for use as a storage facility for web-based development products in the Informatics Education program at the Faculty of Mathematics and Natural Sciences, Hamzanwadi University

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Section: Studi Pustakaunclassified

Development of Smart Servers for Informatics Education Program Using NDLC Method

Jamaluddin,

Muhammad Zamroni Uska,

Wirasasmita

et al. 2024

JITE

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“…Another critical issue in cloud environments is related to the dynamic consolidation of virtual machines. Moving virtual machines dynamically to a better placement and turning down inactive nodes helps to reduce power consumption, as seen in [29].…”

Section: B Cloud Computingmentioning

confidence: 99%

Sustainable Artificial Intelligence Systems: An Energy Efficiency Approach

Hidalgo,

Fenández-de_Vega,

Ceberio

et al. 2023

Preprint

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<p>The energy consumption of Artificial Intelligence (AI) systems has increased 300,000-fold from 2012 to now, and data centers running massive AI software produce up to 5-9% of global electricity demand and 2% of all CO2 emissions. Such an increase in energy consumption has been partially motivated by the strong development of new AI-specific architectures to improve the performance of AI models. Nevertheless, the AI community has recently become aware of the importance of considering energy efficiency as a metric when developing AI techniques. To date, a great effort has been made to find optimal AI model configurations that provide the best solution in the shortest possible time. However, only a few works have sought a compromise between energy cost and system performance. This paper analyses recent efforts in these directions and proposes the path toward energy-efficient AI. We describe a set of energy efficiency strategies for applying and deploying AI models on different computing infrastructures in search of democratizing an environmentally sustainable AI. To that end, we propose a full-stack approach of energy-efficient AI and analyze the role that different types of users should play, tackling the energy-focused optimization in all steps of the AI model design flow, from the high levels of models and algorithm design to the low levels ones, more related to the hardware and architecture.<br> </p>

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“…The improvement projects that bring a cost reduction of air conditioning consumption in the data center are critical success factors to better energy efficiency in environments like this, significantly if these improvements also extend equipment life without direct electrical power from the data center. It is a consensus in the literature and practice of data centers that the most impact factor in energy consumption is maintaining the temperature at adequate levels [30][31][32]. The strong dependence on temperature and energy consumption in semiconductors was demonstrated by Vanish [33] in 1967.…”

Section: Introductionmentioning

confidence: 99%

Towards Energy Efficiency in Data Centers: An Industrial Experience Based on Reuse and Layout Changes

Machado¹,

Pires²,

Caldeira³

et al. 2021

Applied Sciences

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Data centers are widely recognized for demanding many energy resources. The greater the computational demand, the greater the use of resources operating together. Consequently, the greater the heat, the greater the need for cooling power, and the greater the energy consumption. In this context, this article aims to report an industrial experience of achieving energy efficiency in a data center through a new layout proposal, reuse of previously existing resources, and air conditioning. We used the primary resource to adopt a cold corridor confinement, the increase of the raised floor’s height, and a better direction of the cold airflow for the aspiration at the servers’ entrance. We reused the three legacy refrigeration machines from the old data center, and no new ones were purchased. In addition to 346 existing devices, 80 new pieces of equipment were added (between servers and network assets) as a load to be cooled. Even with the increase in the amount of equipment, the implementations contributed to energy efficiency compared to the old data center, still reducing approximately 41% of the temperature and, consequently, energy-saving.

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A Review on Dynamic Virtual Machine Consolidation Approaches for Energy-Efficient Cloud Data Centers

Cited by 12 publications

References 42 publications

Development of Smart Servers for Informatics Education Program Using NDLC Method

Development of Smart Servers for Informatics Education Program Using NDLC Method

Sustainable Artificial Intelligence Systems: An Energy Efficiency Approach

Towards Energy Efficiency in Data Centers: An Industrial Experience Based on Reuse and Layout Changes

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