The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
높은 에너지 비용은 현재 데이터 센터가 직면한 큰 과제이며, 컴퓨팅 에너지와 냉각 에너지가 이러한 비용의 주요 원인입니다. 워크로드를 더 적은 수의 서버에 통합하면 컴퓨팅 에너지가 줄어듭니다. 그러나 일반적으로 더 많은 냉각 에너지를 소비하는 열 핫스팟이 발생할 수 있습니다. 따라서 에너지 절약을 위해서는 컴퓨팅 에너지 감소와 냉각 에너지 감소 간의 균형이 필요합니다. 본 논문에서는 C라는 최소화된 총 에너지 가상 머신(줄여서 VM) 마이그레이션 모델을 제안한다.2vmMap은 두 가지 관계(리소스 활용도와 컴퓨팅 성능 간의 관계, 다른 하나는 리소스 활용도, 서버 입구 및 출구 온도, 냉각 전력 간의 관계)와 관련하여 컴퓨팅 에너지와 냉각 에너지 간의 효율적인 균형을 기반으로 합니다. 더 나은 확장성을 위한 위 모델의 온라인 해결과 관련하여 우리는 C라는 VM 마이그레이션 알고리즘을 제안합니다.2vmMap_heur는 런타임 시 데이터 센터의 총 에너지를 줄입니다. C를 평가한다2다양한 워크로드 시나리오에서 vmMap_heur. 실제 서버 실험 결과는 C2vmMap_heur는 비 마이그레이션 로드 밸런싱 알고리즘에 비해 최대 40.43%의 에너지를 절감합니다. 이 알고리즘은 기존 VM 마이그레이션 알고리즘에 비해 최대 3배의 에너지를 절약합니다.
Ying SONG
Beijing Key Laboratory of Internet Culture and Digital Dissemination
Xia ZHAO
Beijing Technology and Business University
Bo WANG
Zhengzhou University of Light Industry
Yuzhong SUN
CAS
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Ying SONG, Xia ZHAO, Bo WANG, Yuzhong SUN, "Trading-Off Computing and Cooling Energies by VM Migration in Data Centers" in IEICE TRANSACTIONS on Information,
vol. E101-D, no. 9, pp. 2224-2234, September 2018, doi: 10.1587/transinf.2017EDP7329.
Abstract: High energy cost is a big challenge faced by the current data centers, wherein computing energy and cooling energy are main contributors to such cost. Consolidating workload onto fewer servers decreases the computing energy. However, it may result in thermal hotspots which typically consume greater cooling energy. Thus the tradeoff between computing energy decreasing and cooling energy decreasing is necessary for energy saving. In this paper, we propose a minimized-total-energy virtual machine (VM for short) migration model called C2vmMap based on efficient tradeoff between computing and cooling energies, with respect to two relationships: one for between the resource utilization and computing power and the other for among the resource utilization, the inlet and outlet temperatures of servers, and the cooling power. Regarding online resolution of the above model for better scalability, we propose a VM migration algorithm called C2vmMap_heur to decrease the total energy of a data center at run-time. We evaluate C2vmMap_heur under various workload scenarios. The real server experimental results show that C2vmMap_heur reduces up to 40.43% energy compared with the non-migration load balance algorithm. This algorithm saves up to 3x energy compared with the existing VM migration algorithm.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2017EDP7329/_p
부
@ARTICLE{e101-d_9_2224,
author={Ying SONG, Xia ZHAO, Bo WANG, Yuzhong SUN, },
journal={IEICE TRANSACTIONS on Information},
title={Trading-Off Computing and Cooling Energies by VM Migration in Data Centers},
year={2018},
volume={E101-D},
number={9},
pages={2224-2234},
abstract={High energy cost is a big challenge faced by the current data centers, wherein computing energy and cooling energy are main contributors to such cost. Consolidating workload onto fewer servers decreases the computing energy. However, it may result in thermal hotspots which typically consume greater cooling energy. Thus the tradeoff between computing energy decreasing and cooling energy decreasing is necessary for energy saving. In this paper, we propose a minimized-total-energy virtual machine (VM for short) migration model called C2vmMap based on efficient tradeoff between computing and cooling energies, with respect to two relationships: one for between the resource utilization and computing power and the other for among the resource utilization, the inlet and outlet temperatures of servers, and the cooling power. Regarding online resolution of the above model for better scalability, we propose a VM migration algorithm called C2vmMap_heur to decrease the total energy of a data center at run-time. We evaluate C2vmMap_heur under various workload scenarios. The real server experimental results show that C2vmMap_heur reduces up to 40.43% energy compared with the non-migration load balance algorithm. This algorithm saves up to 3x energy compared with the existing VM migration algorithm.},
keywords={},
doi={10.1587/transinf.2017EDP7329},
ISSN={1745-1361},
month={September},}
부
TY - JOUR
TI - Trading-Off Computing and Cooling Energies by VM Migration in Data Centers
T2 - IEICE TRANSACTIONS on Information
SP - 2224
EP - 2234
AU - Ying SONG
AU - Xia ZHAO
AU - Bo WANG
AU - Yuzhong SUN
PY - 2018
DO - 10.1587/transinf.2017EDP7329
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E101-D
IS - 9
JA - IEICE TRANSACTIONS on Information
Y1 - September 2018
AB - High energy cost is a big challenge faced by the current data centers, wherein computing energy and cooling energy are main contributors to such cost. Consolidating workload onto fewer servers decreases the computing energy. However, it may result in thermal hotspots which typically consume greater cooling energy. Thus the tradeoff between computing energy decreasing and cooling energy decreasing is necessary for energy saving. In this paper, we propose a minimized-total-energy virtual machine (VM for short) migration model called C2vmMap based on efficient tradeoff between computing and cooling energies, with respect to two relationships: one for between the resource utilization and computing power and the other for among the resource utilization, the inlet and outlet temperatures of servers, and the cooling power. Regarding online resolution of the above model for better scalability, we propose a VM migration algorithm called C2vmMap_heur to decrease the total energy of a data center at run-time. We evaluate C2vmMap_heur under various workload scenarios. The real server experimental results show that C2vmMap_heur reduces up to 40.43% energy compared with the non-migration load balance algorithm. This algorithm saves up to 3x energy compared with the existing VM migration algorithm.
ER -