Energy models driven green routing for data centers

Saha, Shivashis; Deogun, Jitender S.; Xu, Lisong

doi:10.1109/glocom.2012.6503497

Cited by 11 publications

(8 citation statements)

References 18 publications

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“…Using more than one evaluation method could help derived conclusions be more concrete. For instance, Jin [ Shang et al 2012;Si et al 2012;Tarutani et al 2012;Peoples et al 2012;Aksanli et al 2012;Peoples et al 2011;Shang et al 2010;Kitayama et al 2013;Cordeschi et al 2013;Dong et al 2013;Mahadevan et al 2009;Leivadeas et al 2013;Saha et al 2012;Sun et al 2012;He et al 2012;Shirayanagi et al 2013;Gyarmati and Trinh 2010;Szymanski 2013;Fang et al 2012;Mahadevan et al 2011;McGeer et al 2010;Huang et al 2011;Kachris and Tomkos 2011;…”

Section: Results Regarding Energy Efficiency Evaluationmentioning

confidence: 98%

“…Considering that different areas of administration and ownership exist in multidomain transfer networks, these solution types cannot be deployed in a centralized consistent way. We have identified two primary studies Decision framework Tarutani et al 2012;Heller et al 2010;Thanh et al 2012;Cordeschi et al 2013;Dong et al 2013;Mahadevan et al 2009;Leivadeas et al 2013;Saha et al 2012;Sun et al 2012;Shirayanagi et al 2013;Nguyen Huu et al 2013;Fang et al 2012;Mahadevan et al 2011;McGeer et al 2010;Huang et al 2011;Kuribayashi 2012;Mann et al 2011;Fang et al 2013] Decision framework [Peoples et al 2012;Kantarci et al 2013] 3 9…”

Section: Results Regarding Scalementioning

confidence: 99%

“…Shang et al 2012;Tarutani et al 2012;Cerutti et al 2013;Heller et al 2010; Thanh et al 2012;Shang et al 2010;Kitayama et al 2013;Dong et al 2013;Ji et al 2012;Shirayanagi et al 2013;Gyarmati and Trinh 2010;McGeer et al 2010;Huang et al 2011; Tomkos 2013, 2011;Carrega et al 2012; Mann et al Shang et al 2012;Si et al 2012; Thanh et al 2012;Shang et al 2010;Kitayama et al 2013;Cordeschi et al 2013;Dong et al 2013;Leivadeas et al 2013;Glesk et al 2013;Sun et al 2012;He et al 2012;Fang et al 2012;Mann et al 2011] Device[Si et al 2012;Tarutani et al 2012;Cerutti et al 2013;Kitayama et al 2013;Ji et al 2012;Saha et al 2012;Glesk et al 2013; Tomkos 2013, 2011;Carrega et al 2012] 10 Scale: Inter-Data Center (11.3%) 5Decision framework[Aksanli et al 2012;Buysse et al 2011;Peoples et al 2011; Mouftah 2012Aksanli et al 2012;Buysse et al 2011;Peoples et al 2011;Szymanski 2013; Scale: Mixed-(Inter-& Intra-) Data Center (6.8%) 8…”

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confidence: 97%

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Energy-Efficient Networking Solutions in Cloud-Based Environments

Moghaddam

Lago²,

Grosso

2015

ACM Comput. Surv.

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The energy consumed by data centers hosting cloud services is increasing enormously. This brings the need to reduce energy consumption of different components in data centers. In this work, we focus on energy efficiency of the networking component. However, how different networking solutions impact energy consumption is still an open question. We investigate the state of the art in energy-efficient networking solutions in cloud-based environments. We follow a systematic literature review method to select primary studies. We create a metamodel based on the codes extracted from our primary studies using the Coding analytical method. Our findings show three abstraction levels of the proposed networking solutions to achieve energy efficiency in cloud-based environments: Strategy, Solution, and Technology. We study the historical trends in the investigated solutions and conclude that the emerging and most widely adopted one is the Decision framework. ACM Reference Format:Fahimeh Alizadeh Moghaddam, Patricia Lago, and Paola Grosso. 2015. Energy-efficient networking solutions in cloud-based environments: A systematic literature review. ACM Comput.

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Section: Results Regarding Energy Efficiency Evaluationmentioning

confidence: 98%

Section: Results Regarding Scalementioning

confidence: 99%

mentioning

confidence: 97%

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Energy-Efficient Networking Solutions in Cloud-Based Environments

Moghaddam

Lago²,

Grosso

2015

ACM Comput. Surv.

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“…The Dynamic Voltage Scaling (DVS) is a type of DPM technique that allows greater energy efficiency than the SPM [18]. The DVS regulates the value of the supply voltage V DD to control the energy consumption [3], as shown in Fig. 2.…”

Section: Introductionmentioning

confidence: 99%

“…The Dynamic Power Management (DPM) techniques consider resource utilization based on the current workload for energy savings [2]. Conventionally, DPM techniques are used to manage the power supply voltage within CMOS processors at the operating system levels [2], [3]. The Dynamic Voltage Scaling (DVS) is a type of DPM technique that allows greater energy efficiency than the SPM [18].…”

Section: Introductionmentioning

confidence: 99%

JEM: Just in Time/Just Enough Energy Management Methodology for Computing Systems

Jawad

Ali

Jorgenson

et al. 2015

IEEE Trans. Comput.

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This paper presents the Just in Time/Just Enough Energy Management (JEM) methodology that is applicable to a broad range of computing systems. The conventional concept of a fixed voltage supply (V DD ) scheme for both performance and power saving modes of computing systems is revisited and is improved with JEM. The JEM consists of an efficient DC/DC converter and a Power Management Integrated Circuit (PMIC) with a feedback to monitor the activities within a given computing system, providing a new means for dynamic voltage scaling at the system level. The JEM is tested and validated on a blade server that results in 15.11 percent power savings at the motherboard level. A significant thermal improvement of 9.0 C is measured in a 16 GB memory module of the blade server, as well. Moreover, a JEM enabled CMOS circuit depicts a remarkable reduction in the supply current. Furthermore, the JEM is compared to a conventional power supply design, with significant improvement in the processor performance and considerable power savings in the blade server.

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MVMM: Data Center Scheduler Algorithm for Virtual Machine Migration

Kortas

Youssef

2019

Advanced Information Networking and Applications

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Energy models driven green routing for data centers

Cited by 11 publications

References 18 publications

Energy-Efficient Networking Solutions in Cloud-Based Environments

Energy-Efficient Networking Solutions in Cloud-Based Environments

JEM: Just in Time/Just Enough Energy Management Methodology for Computing Systems

MVMM: Data Center Scheduler Algorithm for Virtual Machine Migration

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