Greening big data networks: velocity impact

Al-Salim, Ali M.; El-Gorashi, Taisir E. H.; Lawey, Ahmed Q.; Elmirghani, Jaafar M. H.

doi:10.1049/iet-opt.2016.0165

Cited by 42 publications

(28 citation statements)

References 22 publications

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“…However, the workload capacity is large enough if a DC is built at core node p. Note that, the model implements a consolidation process by processing as many Chunks as possible within the same server to minimize the NPC and number of active servers. 6) PNs and DCs storage constraints (19) …”

Section: Power Consumption Of Optical Switch Installed At Node I N (Wmentioning

confidence: 99%

“…Constraint (19) represents the size of Chunks in Gb stored in PN p. Constraint (20) ensures that the total data stored in PN p does not exceed the storage capacity of that PN. H is a large enough unitless number to guarantee that there is no storage capacity limitation at the DCs.…”

Section: Power Consumption Of Optical Switch Installed At Node I N (Wmentioning

confidence: 99%

“…We referred to such a network as an Energy Efficient Tapered Data Network since there is a significant reduction in the data transported over the network each time the data is processed. Our work in [19] and [20] considered big data velocity and veracity respectively, which are two dimensions of big data that are not related to the current work. In the velocity dimension, we presented several scenarios to process expedited-data and relaxed-data made up of big data Chunks progressively starting at the source nodes of the network, moving through the intermediate nodes of the network and finally processing at the centralized data centers.…”

Section: Introductionmentioning

confidence: 99%

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Energy Efficient Big Data Networks: Impact of Volume and Variety

Al-Salim

Lawey

El-Gorashi

et al. 2018

IEEE Trans. Netw. Serv. Manage.

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Abstract-In this article, we study the impact of big data's volume and variety dimensions on Energy Efficient Big Data Networks (EEBDN) by developing a Mixed Integer Linear Programming (MILP) model to encapsulate the distinctive features of these two dimensions. Firstly, a progressive energy efficient edge, intermediate, and central processing technique is proposed to process big data's raw traffic by building processing nodes (PNs) in the network along the way from the sources to datacenters. Secondly, we validate the MILP operation by developing a heuristic that mimics, in real time, the behaviour of the MILP for the volume dimension. Thirdly, we test the energy efficiency limits of our green approach under several conditions where PNs are less energy efficient in terms of processing and communication compared to data centers. Fourthly, we test the performance limits in our energy efficient approach by studying a "software matching" problem where different software packages are required to process big data. The results are then compared to the Classical Big Data Networks (CBDN) approach where big data is only processed inside centralized data centers. Our results revealed that up to 52% and 47% power saving can be achieved by the EEBDN approach compared to the CBDN approach, under the impact of volume and variety scenarios, respectively. Moreover, our results identify the limits of the progressive processing approach and in particular the conditions under which the CBDN centralized approach is more appropriate given certain PNs energy efficiency and software availability levels. Index Terms -Big data volume, big data variety, energy efficient networks, IP over WDM core networks, MILP, processing location optimization, software matching.

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Section: Power Consumption Of Optical Switch Installed At Node I N (Wmentioning

confidence: 99%

Section: Power Consumption Of Optical Switch Installed At Node I N (Wmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Energy Efficient Big Data Networks: Impact of Volume and Variety

Al-Salim

Lawey

El-Gorashi

et al. 2018

IEEE Trans. Netw. Serv. Manage.

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“…An IP over DWDM core network is used to demonstrate communication between multiple data centres located in multiple cities with over 100 km of optical fibre links as shown in Figure 4 [20]- [29]. Each core node is 100 Gbps MRV/ADVA DWDM node.…”

Section: Architecture Implementationmentioning

confidence: 99%

Experimental Evaluation of Server Centric Passive Optical Network Based Data Centre Architecture

Eltraify

Musa

Al-Quzweeni

et al. 2019

2019 21st International Conference on Transparent Optical Networks (ICTON)

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Passive optical networks (PON) technology has recently been proposed as a solution for scalability, energy efficiency, high capacity, low cost, flexibility and oversubscription issues in data centres. This paper experimentally demonstrates and discusses the implementation of a server centric PON based data centre architecture with high speed and reliability. The architecture is set up using a set of servers grouped into racks directly connected together and to the Optical Line Terminal (OLT) through gateway servers. The switching and routing functionalities have been embedded into servers using 4x10GE Xilinx NetFPGA. Flow continuity has been observed through live video streaming using IP cameras transmitting over up to 110 km optical connections through WDM nodes and the PON network.

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“…The energy consumption of clouds and data centers is contributing much to the total cost and power consumption in the Information and Communication Technology (ICT) field. That is why a lot of effort is being put forward now to explore alternatives that are more energy efficient and still as powerful [2][3][4][5][6][7][8][9][10][11]. One approach that is being actively evaluated is distributed service providers or the installation of mini data centers close to end users' level.…”

mentioning

confidence: 99%

Energy Efficient Distributed Processing in Vehicular Cloud Architecture

Behbehani

Musa

El-Gorashi

et al. 2019

2019 21st International Conference on Transparent Optical Networks (ICTON)

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Facilitating the revolution for smarter cities, vehicles are getting smarter and equipped with more resources to go beyond transportation functionality. On-Board Units (OBU) are efficient computers inside vehicles that serve safety and non-safety based applications. However, much of these resources are underutilised. On the other hand, more users are relying now on cloud computing which is becoming costly and energy consuming. In this paper, we develop a Mixed Integer linear Programming (MILP) model that optimizes the allocation of processing demands in an architecture that encompasses the vehicles, edge and cloud computing with the objective of minimizing power consumption. The results show power savings of 70%-90% compared to conventional clouds for small demands. For medium and large demand sizes, the results show 20%-30% power saving as the cloud was used partially due to capacity limitations on the vehicular and edge nodes. INTROUCTIONEnd users are growing more dependent on cloud services and data centers [1]. As the demand on cloud services grows higher, the data centers, as expected, tend to grow even bigger and more expensive in term of both monetary cost and energy consumptions. The energy consumption of clouds and data centers is contributing much to the total cost and power consumption in the Information and Communication Technology (ICT) field. That is why a lot of effort is being put forward now to explore alternatives that are more energy efficient and still as powerful [2][3][4][5][6][7][8][9][10][11]. One approach that is being actively evaluated is distributed service providers or the installation of mini data centers close to end users' level. In [12] data processing is done at different layers of the network and not only in the core cloud through optimized placement of Virtual Machines (VM) in IoT devices. Comparison between centralized data centers and nano data centers, to show the validity of the small data centers and its impacting factors, was carried out in [13]. The work in [14] analysed the energy consumption and latency of computation offloading in mobile clouds.Modern vehicles are increasingly being viewed as smart machines with plenty of computing resources. Research in the area of vehicular networks is very promising and it varies from Internet of Vehicles (IoV) [15] to Vehicular Clouds to VaaR (vehicle as a Resource) [16]. Our work presents an end-to-end architecture that uses vehicular and edge computing as the first level of processing resources. It compares this architecture with conventional clouds from an energy consumption point of view. For the remainder or the paper, Section 2 presents the proposed architecture. Section 3 discusses the optimization model and its results, and in Section 4 the paper is concluded.

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Greening big data networks: velocity impact

Cited by 42 publications

References 22 publications

Energy Efficient Big Data Networks: Impact of Volume and Variety

Energy Efficient Big Data Networks: Impact of Volume and Variety

Experimental Evaluation of Server Centric Passive Optical Network Based Data Centre Architecture

Energy Efficient Distributed Processing in Vehicular Cloud Architecture

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