Energy Efficiency in SDDC: Considering Server and Network Utilities

Assefa, Beakal Gizachew; Özkasap, Öznur; Kızıl, İpek; Aloqaily, Moayad; Bouachir, Ouns

doi:10.1109/iscc50000.2020.9219605

Cited by 5 publications

(8 citation statements)

References 20 publications

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“…Paper, 13 however, only considers a single data center, not networked data centers, and does not associate any monetary metric with the negative impact of the Server Migration traffic. Consequently, cloud service providers may not use the contributions of paper 13 in order to determine to which (physical) computers servers migrate in optimizing their monetary benefits. This article addresses both monetary metrics and negative financial impact of the server migration traffic; many existing studies 14‐28 in cloud systems, edge computing systems and fog computing systems address migrating servers considering neither the monetary metrics nor the negative financial impact of the server migration traffic. (Papers 14‐21 in “cloud systems” and Papers 22‐28 in “edge and fog computing systems” in Table 1.)…”

Section: Contributions Of This Articlementioning

confidence: 99%

“…It proposes an adaptive algorithm to reduce the number of VMs needed by an application, while satisfying the application's SLA and develops an optimal off‐line algorithm. Paper 17 also focuses on VM placement in a single data center in a cloud system. It proposes an integer programming model that jointly minimizes the energy consumptions of physical servers and switches.…”

Section: Contributions Of This Articlementioning

confidence: 99%

“…Unlike this article, papers 14‐17 do not associate any monetary metric with the total energy consumption and application's QoS, and consequently, cloud service providers may not use the contributions of papers 14‐17 in order to determine to which (physical) computers servers migrate in optimizing the monetary benefits. In addition, papers 14‐17 neither address the negative financial impact of the server migration traffic nor consider servers migrating between multiple data centers. Paper 18 focuses on VM placement between multiple data centers in a cloud system. It develops a mixed‐integer programming model that determines to which data centers servers are placed in order to minimize the combined energy consumption of the data centers and of the network.…”

Section: Contributions Of This Articlementioning

confidence: 99%

“…Consequently, cloud service providers may not use the contributions of paper 13 in order to determine to which (physical) computers servers migrate in optimizing their monetary benefits. • This article addresses both monetary metrics and negative financial impact of the server migration traffic; many existing studies [14][15][16][17][18][19][20][21][22][23][24][25][26][27][28] in cloud systems, edge computing systems and fog computing systems address migrating servers considering neither the monetary metrics nor the negative financial impact of the server migration traffic. (Papers [14][15][16][17][18][19][20][21] in "cloud systems" and Papers [22][23][24][25][26][27][28] in "edge and fog computing systems" in Table 1.)…”

Section: -21mentioning

confidence: 99%

“…• This article addresses both monetary metrics and negative financial impact of the server migration traffic; many existing studies [14][15][16][17][18][19][20][21][22][23][24][25][26][27][28] in cloud systems, edge computing systems and fog computing systems address migrating servers considering neither the monetary metrics nor the negative financial impact of the server migration traffic. (Papers [14][15][16][17][18][19][20][21] in "cloud systems" and Papers [22][23][24][25][26][27][28] in "edge and fog computing systems" in Table 1.) -Paper 14 focuses on VM placement in a single data center in a cloud system.…”

Section: -21mentioning

confidence: 99%

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Minimizing the monetary penalty and energy cost of server migration service

Fukushima

Suda²,

Murase

et al. 2022

Trans Emerging Tel Tech

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Current IaaS (infrastructure as a service) cloud service may not satisfy communication QoS (quality of service) requirements of delay-sensitive network applications, if there is a significant physical distance between a server of the network application (NetApp server) at a data center and its network application clients (NetApp clients). In order to improve communication QoS of NetApp clients, we propose server migration service (SMS) in this article. SMS allows NetApp servers to migrate among different locations in the network (1) to optimally locate themselves in relation to NetApp clients and mitigate the QoS degradation caused by location-related factors (ie, propagation delays on network links) and (2) to optimally distribute traffic load over routers and processing load over (physical) computers and decrease the energy consumption.We develop a mixed-integer programming model that determines when and to which locations NetApp servers migrate to minimize the total operating cost of SMS, that is, the sum of the monetary penalty incurred due to QoS violation and energy cost incurred due to energy consumption, while preventing NetApp servers from excessively migrating and adversely impacting QoS of the non-SMS service that share the resource of the substrate network with SMS. Simulation results show that the model developed in this article achieves up to 42% lower total operating cost of SMS compared to the model that only minimizes the monetary penalty of SMS without considering the energy cost of SMS.

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Section: Contributions Of This Articlementioning

confidence: 99%

Section: Contributions Of This Articlementioning

confidence: 99%

Section: Contributions Of This Articlementioning

confidence: 99%

Section: -21mentioning

confidence: 99%

Section: -21mentioning

confidence: 99%

See 3 more Smart Citations

Minimizing the monetary penalty and energy cost of server migration service

Fukushima

Suda²,

Murase

et al. 2022

Trans Emerging Tel Tech

View full text Add to dashboard Cite

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Software‐defined networking in vehicular networks: A survey

Mekki¹,

Jabri

Rachedi

et al. 2021

Trans Emerging Tel Tech

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Software‐defined networking (SDN) is a new paradigm that consists in decoupling the control plane from the data plane, which offers the flexibility of configuring and managing networks. Recently, an extension of SDN to vehicular ad hoc networks (VANETs), named software‐defined vehicular networks (SDVN), has been emerged to expand the capabilities of VANET in terms of the provided services and to overcome the VANET issues. Also, SDN has gained a lot of interest to be involved in the extensions of VANET, which are vehicular cloud and vehicular fog. In this paper, we overview the use of SDN in VANET. We survey its deployment in the classic vehicular networks, 5G‐VANET, vehicular cloud, and vehicular fog. Thereafter, we discuss some case studies of SDN in the vehicular context. Then, we present a taxonomy of the existing architectures followed by the major challenges in the implementation of SDN in the vehicular context. Next, we review the tools used to simulate SDN‐based VANET scenarios. Finally, we give some future research directions.

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