GreenTouch GreenMeter core network power consumption models and results

IEEE Trans. Netw. Serv. Manage.

et al. 2018

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.

Section: B2 Different Cpu Workloads and Prr Per Chunkmentioning

confidence: 99%

Energy Efficient Big Data Networks: Impact of Volume and Variety

Al-Salim

Lawey

IEEE Trans. Netw. Serv. Manage.

et al. 2018

“…A total of 50 enterprise clients send VNRs to the InP over a 24 hour period at two hour time granularity of service. The traffic generated by the VNRs over a 24 hour period is modelled according to the 2020 average business Internet traffic between nodes in the NSFNET network as projected by the GreenTouch Consortium [12]. The requests once accepted stay in the network for 2 hours after which they will be torn down and adjusted according to the new arriving demands.…”

Section: Link Embedding Constraintsmentioning

confidence: 99%

“…The current and future criterion for designing cloud infrastructure is to distribute the content among a number of data centers to minimize the delay experienced by the users and to avoid the scenario of having a single hot node in the network. The NSFNET contains five data centers located at nodes (2, 3, 6, 8 and 10) [12].…”

Section: Link Embedding Constraintsmentioning

confidence: 99%

Virtual Network Embedding Employing Renewable Energy Sources

Nonde

2016 IEEE Global Communications Conference (GLOBECOM)

Elmirgahni

2016

Abstract-Environmental sustainability in high capacitynetworks and cloud data centers has become one of the hottest research subjects. In this paper, we investigate the effective use of renewable energy and hence resource allocation in core networks with clouds as a means of reducing the carbon footprint. We develop a Green Virtual Network Embedding (GVNE) framework for minimizing the use of non-renewable energy through intelligent provisioning of bandwidth and cloud data center resources. The problem is modeled as a mixed integer linear program (MILP). The results show that it is better to instantiate virtual machines in cloud data centers that have access to abundant renewable energy even at the expense of traversing several links across the network. The GVNE model reduces the overall CO2 emissions by up to 32% for the network considering solar power availability and data center locations.

“…Constraints (19) and (20) represent the physical link capacity constraints. Constraint (19) ensures that the number of wavelengths in a physical link does not exceed the capacity of fibers in the physical links.…”

Section: Physical Link Capacity Constraintsmentioning

confidence: 99%

“…Constraint (19) ensures that the number of wavelengths in a physical link does not exceed the capacity of fibers in the physical links. Constraint (20) gives the total number of wavelength channels used in a physical link.…”

Section: Physical Link Capacity Constraintsmentioning

confidence: 99%

Cloud Virtual Network Embedding: Profit, Power and Acceptance

Nonde

2015 IEEE Global Communications Conference (GLOBECOM)

Elmirghani

2015

Abstract-In this paper, we investigate maximizing the profit achieved by infrastructure providers (InPs) from embedding virtual network requests (VNRs) in IP/WDM core networks with clouds. We develop a mixed integer linear programming (MILP) model to study the impact of maximizing the profit on the power consumption and acceptance of VNRs. The results show that higher acceptance rates do not necessarily lead to higher profit due to the high cost associated with accepting some of the requests. The results also show that minimum power consumption can be achieved while maintaining the maximum profit.