A taxonomy and survey on Green Data Center Networks

Bilal, Kashif; Malik, Saif Ur Rehman; Khalid, Osman; Hameed, Abdul; Alvarez, Enríque; Wijaysekara, Vidura; Irfan, Rizwana; Shrestha, Sarjan; Dwivedy, Debjyoti; Ali, Mazhar; Khan, Usman Ali; Abbas, Abdulla; Jalil, Nauman; Khan, Samee U.

doi:10.1016/j.future.2013.07.006

Cited by 170 publications

(74 citation statements)

References 89 publications

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“…ITS use results in significant environmental footprints (Faucheux and Nicolaï, 2011;Uddin and Rahman, 2012). Green ITS can help mitigate these environmental footprints (Bhadauria et al, 2014) and optimize overall energy consumption of mines (Bilal et al, 2014). The use of eco-friendly hardware and data center, reducing waste of unused hardware, collaborative group software to minimize cost, and introducing telepresence to increase flexibility and buying eco-labeling of IT products are considered as green practices.…”

Section: Green Information Technology and Systems (Gits)mentioning

confidence: 99%

A review on green supply chain aspects and practices

Islam

Karia

Fauzi

et al. 2017

Management &Amp; Marketing

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Section: Green Information Technology and Systems (Gits)mentioning

confidence: 99%

A review on green supply chain aspects and practices

Islam

Karia

Fauzi

et al. 2017

Management &Amp; Marketing

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“…Simulation results showed up to 24% of energy savings [4]. The detailed discussion of energy efficiency in data centers can be seen in [16].…”

Section: Green Networking In Data Centersmentioning

confidence: 96%

Green Data Center Networks: Challenges and Opportunities

Bilal

Khan

Zomaya

2013

2013 11th International Conference on Frontiers of Information Technology

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Abstract-The concerns about environmental impacts, electricity cost, and energy needs of data centers are mounting. These concerns have given significant impetus to consider energy efficiency as one of the major data center design parameters for researchers and IT executives. Tremendous growth in the size and computing demands of data centers mandate scalable, fault tolerant, and energy efficient network infrastructure within data centers. In the recent years, new Data Center Network (DCN) architectures have aptly proposed a viable solution to the issues posed by the legacy DCNs. New DCN architectures are required to be energy efficient and energy proportional, besides dealing with other identified design drawbacks, such as scalability, fault tolerance, end-to-end bandwidth, and non-agility. In this article, we will deliberate on the various DCN architectures, their inherent problems, and the potentials to achieve energy efficiency. We will also shed light on myriad techniques that may be suitable to make the DCN architectures more energy efficient and energy proportional. Finally, we will outline future directions.

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“…Thus, data centers are faced with inherent problems in the traditional data center network (DCN) architecture. This leads to real problematic issues such as bandwidth bottlenecks, oversubscription in the higher layers and the under-utilization of the lower layers of the data center network are becoming [1]. To resolve this, several new approaches to designing data center network topologies have been proposed in the recent years, one of which is the "tree topology" discussed below.…”

Section: Data Center Network Architecturesmentioning

confidence: 99%

On achieving intelligent traffic-aware consolidation of virtual machines in a data center using Learning Automata

Jobava

Yazidi

Oommen

et al. 2018

Journal of Computational Science

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Abstract-Cloud Computing (CC) is becoming increasingly pertinent and popular. A natural consequence of this is that many modern-day data centers experience very high internal traffic within the data centers themselves. The VMs with high mutual traffic often end up being far apart in the data center network, forcing them to communicate over unnecessarily long distances. The consequent traffic bottlenecks negatively affect both the performance of the application and the network in its entirety, posing nontrivial challenges for the administrators of these cloudbased data centers. The problem can, quite naturally, be compartmentalized into two phases which follow each other. First of all, the VMs are consolidated with a VM clustering algorithm, and this is achieved by utilizing the toolbox involving Learning Automata (LA). By mapping the clustering problem onto the Graph Partitioning (GP) problem, our LAbased solution successfully reduces the total communication cost by amounts that range between 34% to 85%. Thereafter, the resulting clusters are assigned to the server racks using a cluster placement algorithm that involves a completely different intelligent strategy, i.e., one that invokes Simulated Annealing (SA). This phase further reduces the total cost of communication by amounts that range between 89% to 99%. The analysis and results for different models and topologies demonstrate that the optimization is done in a fast and computationally-efficient way. Indeed, as far as we know, this paper pioneers the application of LA in the traffic-aware consolidation of virtual machines in data centers, and also pioneers a strategy which serializes the tools in LA and SA to optimize CC.

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A taxonomy and survey on Green Data Center Networks

Cited by 170 publications

References 89 publications

A review on green supply chain aspects and practices

A review on green supply chain aspects and practices

Green Data Center Networks: Challenges and Opportunities

On achieving intelligent traffic-aware consolidation of virtual machines in a data center using Learning Automata

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