EnLoB: Energy and Load Balancing-Driven Container Placement Strategy for Data Centers; IEEE Global Communications Conference (GLOBECOM 20192019)
| Parent link: | IEEE Global Communications Conference (GLOBECOM 20192019).— 2019.— [19435039, 6 p.] |
|---|---|
| Autor Corporativo: | |
| Otros Autores: | , , , , |
| Sumario: | Title screen Cloud data centers (DCs) can be aptly regarded as the epicenter of today's business and economy; which support seamless data processing, analysis, and storage. However, various studies advocate that the existing DCs are often underutilized. To be precise, almost 30% of the installed DCs in the United States are comatose. In addition to this, the existing DC architecture leads to extensive energy utilization, which severely hampers the environment and places a severe risk on the power sector. Thus, it is highly essential to reduce DC's energy utilization through efficient resource consolidation approaches. In this work, we investigate the joint impact of resource consolidation and load balancing on cutting down the energy utilization indices of the cloud DCs. In this vein, we formulate a multi-objective optimization problem (MOOP) for container placement across heterogeneous infrastructure, primarily with the intent to minimize the overall energy consumption and balance the load amongst the operating hosts. However, due to the hardness of the underlying problem and its infeasibility to furnish optimal solutions in polynomial time, we designed an online solution based on the incremental exploration of the solution space to map containers on the available array of hosts such that the objectives mentioned above can be attained. Finally, we evaluated the performance of the proposed algorithm in contrast to an existing algorithm on real-time workload traces obtained from PlanetLab. The obtained results confirm the superior performance of the proposed algorithm relative state-of-the-art. Режим доступа: по договору с организацией-держателем ресурса |
| Lenguaje: | inglés |
| Publicado: |
2019
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| Materias: | |
| Acceso en línea: | https://doi.org/10.1109/GCWkshps45667.2019.9024592 |
| Formato: | MixedMaterials Electrónico Capítulo de libro |
| KOHA link: | https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=662275 |
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| 200 | 1 | |a EnLoB: Energy and Load Balancing-Driven Container Placement Strategy for Data Centers |f K. Kaur, S. Garg, G. Kaddoum [et al.] | |
| 203 | |a Text |c electronic | ||
| 300 | |a Title screen | ||
| 320 | |a [References: 29 tit.] | ||
| 330 | |a Cloud data centers (DCs) can be aptly regarded as the epicenter of today's business and economy; which support seamless data processing, analysis, and storage. However, various studies advocate that the existing DCs are often underutilized. To be precise, almost 30% of the installed DCs in the United States are comatose. In addition to this, the existing DC architecture leads to extensive energy utilization, which severely hampers the environment and places a severe risk on the power sector. Thus, it is highly essential to reduce DC's energy utilization through efficient resource consolidation approaches. In this work, we investigate the joint impact of resource consolidation and load balancing on cutting down the energy utilization indices of the cloud DCs. In this vein, we formulate a multi-objective optimization problem (MOOP) for container placement across heterogeneous infrastructure, primarily with the intent to minimize the overall energy consumption and balance the load amongst the operating hosts. However, due to the hardness of the underlying problem and its infeasibility to furnish optimal solutions in polynomial time, we designed an online solution based on the incremental exploration of the solution space to map containers on the available array of hosts such that the objectives mentioned above can be attained. Finally, we evaluated the performance of the proposed algorithm in contrast to an existing algorithm on real-time workload traces obtained from PlanetLab. The obtained results confirm the superior performance of the proposed algorithm relative state-of-the-art. | ||
| 333 | |a Режим доступа: по договору с организацией-держателем ресурса | ||
| 463 | |t IEEE Global Communications Conference (GLOBECOM 20192019) |o proceedings, Waikoloa, HI, USA, December 9-13, 2019 |v [19435039, 6 p.] |d 2019 | ||
| 610 | 1 | |a труды учёных ТПУ | |
| 610 | 1 | |a электронный ресурс | |
| 610 | 1 | |a containers | |
| 610 | 1 | |a energy consumption | |
| 610 | 1 | |a mathematical model | |
| 610 | 1 | |a load management | |
| 610 | 1 | |a bandwidth | |
| 610 | 1 | |a linear programming | |
| 610 | 1 | |a optimization | |
| 610 | 1 | |a контейнеры | |
| 610 | 1 | |a математические модели | |
| 610 | 1 | |a пропускная способность | |
| 610 | 1 | |a линейное программирование | |
| 610 | 1 | |a оптимизация | |
| 701 | 1 | |a Kaur |b K. | |
| 701 | 1 | |a Garg |b S. |g Sahil | |
| 701 | 1 | |a Kaddoum |b G. |g Georges | |
| 701 | 1 | |a Gagnon |b F. |g Francois | |
| 701 | 1 | |a Dzhayakodi (Jayakody) Arachshiladzh |b D. N. K. |c specialist in the field of electronics |c Professor of Tomsk Polytechnic University |f 1983- |g Dushanta Nalin Kumara |3 (RuTPU)RU\TPU\pers\37962 |9 20606 | |
| 712 | 0 | 2 | |a Национальный исследовательский Томский политехнический университет |b Инженерная школа информационных технологий и робототехники |b Научно-образовательный центр "Автоматизация и информационные технологии" |3 (RuTPU)RU\TPU\col\27515 |
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