Energy Efficiency Measures for Future Core Networks

Elmirghani, Jaafar M. H.; Nonde, Leonard; Lawey, Ahmed Q.; El-Gorashi, Taisir E. H.; Musa, Mohamed O. I.; Dong, Xiaowen; Hinton, Kerry; Klein, Thierry

doi:10.1364/ofc.2017.th1i.4

Cited by 15 publications

(7 citation statements)

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“…The authors of [31], [32] discussed greening these big data networks. The authors of [33], [34] evaluated the methodologies that can be used to provide energy efficient clouds to process the IoT data, while the authors in [34], [35] discussed energy efficient content sharing. The energy efficiency of the networks supporting different services was optimized in [36][37][38][39][40][41][42][43][44].…”

Section: Literature Reviewmentioning

confidence: 99%

Resilient Service Embedding in IoT Networks

et al. 2020

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The Internet of Things (IoT) can support a significant number of services including those in smart homes and the automation of industries and public utilities. However, the growth of these deployments has posed a significant challenge especially in terms of how to build such deployments in a highly resilient manner. The IoT devices are prone to unpredicted failures and cyber-attacks, i.e. various types of damage, unreliable wireless connections, limited transmission power, computing ability, and storage space. Thus resilience is essential in IoT networks and in the services they support. In this paper, we introduce a new approach to resilience in IoT service embedding, based on traffic splitting. Our study assesses the power consumption associated with the services embedded and the data delivery time. The results are compared to recent approaches in resilience including redundancy and replication approaches. We constructed an optimization model whose goal is to determine the optimum physical resources to be used to embed the IoT virtual topology, where the latter is derived from a business process (BP). The embedding process makes use of the service-oriented architecture (SOA) paradigm. The physical resources of interest include IoT links and devices. The model made use of mixed integer linear programming (MILP) with an objective function that aimed to minimize both the total power consumption and the traffic latency. The optimization results show that the power consumption is reduced and the data delivery time is reduced in the service embedding approach where the proposed traffic splitting approach is employed resulting in the selection of energy efficient nodes and routes in the IoT network. Our methods resulted in up to 35% power saving compared to current methods and reduced the average traffic latency by up to 37% by selecting energy-efficient nodes and routes in IoT networks and by optimizing traffic flow to minimize latency.

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Section: Literature Reviewmentioning

confidence: 99%

Resilient Service Embedding in IoT Networks

et al. 2020

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“…Processing the sensor data and the use of data analytics based on big data streams was surveyed in [27], with [28] using these analytics for effective actuation in the network. Greening these big data networks was introduced and discussed in [29], [30] whereas improving the energy efficiency of the clouds and their interconnecting networks that process the IoT data was evaluated in [31], [32] with the energy efficiency of content sharing optimized in [32] and [33]. The energy efficiency of the networks supporting different services was optimized in [34]- [42].…”

Section: Introductionmentioning

confidence: 99%

Service Embedding in IoT Networks

et al. 2020

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The Internet of Things (IoT) is the cornerstone of smart applications such as smart buildings, smart factories, home automation, and healthcare automation. These smart applications express their demands in terms of high-level requests. Application requests in service-oriented IoT architectures are translated into a business process (BP) workflow. In this paper, we model such a BP as a virtual network containing a set of virtual nodes and links connected in a specific topology. These virtual nodes represent the requested processing and locations where sensing and/or actuation are needed. The virtual links capture the requested communication requirements between nodes. We introduce a framework, optimized using mixed integer linear programming (MILP), that embeds the BPs from the virtual layer into a lower-level implementation at the IoT physical layer. We formulate the problem of finding the optimal set of IoT nodes and links to embed BPs into the IoT layer considering three objective functions: i) minimizing network and processing power consumption only, ii) minimizing mean traffic latency only, iii) minimizing a weighted combination of power consumption and traffic latency to study the trade-off between minimizing the power consumption and minimizing the traffic latency. We have established, as reference, a scenario where service embedding is performed to meet all the demands with no consideration to power consumption or latency. Compared to this reference scenario, our results indicate that the power savings achieved by our energy efficient embedding scenario is 42% compared with the energy-latency unaware service embedding (ELUSE) reference scenario, while our low latency embedding reduced the traffic latency by an average of 47% compared to the ELUSE scenario. Our combined energy efficient low latency service embedding approach achieved high optimality by jointly realizing 91% of the power and latency reductions obtained under the single objective of minimizing power consumption or latency.

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“…In [4], we gave the technical background, assumptions, models and detailed results of implementing energy efficient techniques in core networks including bypass [5], sleep, mixed line rates (MLR) [6], [7] and physical topology optimization [8]. In [9] [10] we summarized the outcomes of a framework for designing energy efficient cloud services developed by the GreenTouch consortium. In this paper we look into the details of this framework and incorporate it with the aforementioned techniques studied in [4].…”

Section: Introductionmentioning

confidence: 99%