Energy-efficient traffic engineering

The energy consumption in telecommunication networks is expected to grow considerably, especially in core networks. In this chapter, optimization of energy consumption is approached from two directions. In a first study, multilayer traffic engineering (MLTE) is used to assign energy-efficient paths and logical topology to IP traffic. The relation with traditional capacity optimization is explained, and the MLTE strategy is applied for daily traffic variations. A second study considers the core network below the IP layer, giving a detailed power consumption model. Optical bypass is evaluated as a technique to achieve considerable power savings over per-hop opticalelectronicoptical regeneration. Document type: Part of book or chapter of boo

Energy Efficient Core Networks Using Network Coding

In this paper we investigate the use of network coding to improve energy efficiency of core networks. A mixed integer linear programming model is developed to optimize routing in network coding enabled non-bypass IP/WDM networks considering unicast traffic flows. We quantify the power savings obtained by implementing network coding. The results show that network coding can improve the energy efficiency of non-bypass IP/WDM networks by up to 33% compared to conventional architectures

E-PUR: An Energy-Efficient Processing Unit for Recurrent Neural Networks

Recurrent Neural Networks (RNNs) are a key technology for emerging applications such as automatic speech recognition, machine translation or image description. Long Short Term Memory (LSTM) networks are the most successful RNN implementation, as they can learn long term dependencies to achieve high accuracy. Unfortunately, the recurrent nature of LSTM networks significantly constrains the amount of parallelism and, hence, multicore CPUs and many-core GPUs exhibit poor efficiency for RNN inference. In this paper, we present E-PUR, an energy-efficient processing unit tailored to the requirements of LSTM computation. The main goal of E-PUR is to support large recurrent neural networks for low-power mobile devices. E-PUR provides an efficient hardware implementation of LSTM networks that is flexible to support diverse applications. One of its main novelties is a technique that we call Maximizing Weight Locality (MWL), which improves the temporal locality of the memory accesses for fetching the synaptic weights, reducing the memory requirements by a large extent. Our experimental results show that E-PUR achieves real-time performance for different LSTM networks, while reducing energy consumption by orders of magnitude with respect to general-purpose processors and GPUs, and it requires a very small chip area. Compared to a modern mobile SoC, an NVIDIA Tegra X1, E-PUR provides an average energy reduction of 92x

Energy Efficient Tapered Data Networks for Big Data Processing in IP/WDM Networks

Classically the data produced by Big Data applications is transferred through the access and core networks to be processed in data centers where the resulting data is stored. In this work we investigate improving the energy efficiency of transporting Big Data by processing the data in processing nodes of limited processing and storage capacity along its journey through the core network to the data center. The amount of data transported over the core network will be significantly reduced each time the data is processed therefore we refer to such a network as an Energy Efficient Tapered Data Network. The results of a Mixed Integer linear Programming (MILP), developed to optimize the processing of Big Data in the Energy Efficient Tapered Data Networks, show significant reduction in network power consumption up to 76%

CORNETO: A Software System for Simulating and Optimizing Optical Networks

In this paper we present a software system that is being developed at the University of Leeds for simulating and optimizing energy efficient optical core networks. The system is called CORNETO, an acronym for CORe NETwork Optimization. The software implements many of the energy saving concepts, methods and computational heuristics that have been produced by the ongoing INTERNET, INTelligent Energy awaRe NETworks, project. The main objective of the software is to help network operators and planners green their networks while maintaining quality of service. In this paper we briefly describe the software and demonstrate its capabilities with two case studies

Automatic application object migration in sensor networks

Object migration in wireless sensor networks has the potential to reduce energy consumption for a wireless sensor network mesh. Automated migration reduces the need for the programmer to perform manual static analysis to find an efficient layout solution. Instead, the system can self-optimise and adjust to changing conditions. This paper describes an automated, transparent object migration system for wireless sensor networks, implemented on a micro Java virtual machine. The migration system moves objects at runtime around the sensor mesh to reduce communication overheads. The movement of objects is transparent to the application developer. Automated transparent object migration is a core component of Hydra, a distributed operating system for wireless sensor networks that is currently under development. Performance of the system under a complex performance test scenario using a real-world dataset of seismic events is described. The results show that under both simple and complex conditions the migration technique can result in lower data traffic and consequently lower overall energy cost

Energy Efficient Core Networks with Clouds

The popularity of cloud based applications stemming from the high volume of connected mobile devices has led to a huge increase in Internet traffic. In order to enable easy access to cloud applications, infrastructure providers have invested in geographically distributed databases and servers. However, intelligent and energy efficient high capacity transport networks with near ubiquitous connectivity are needed to adequately and sustainably serve these requirements. In this thesis, network virtualisation has been identified as a potential networking paradigm that can contribute to network agility and energy efficiency improvements in core networks with clouds. The work first introduces a new virtual network embedding core network architecture with clouds and a compute and bandwidth resource provisioning mechanism aimed at reducing power consumption in core networks and data centres. Further, quality of service measures in compute and bandwidth resource provisioning such as delay and customer location have been investigated and their impact on energy efficiency established. Data centre location optimisation for energy efficiency in virtual network embedding infrastructure has been investigated by developing a MILP model that selects optimal data centre locations in the core network. The work also introduces an optical OFDM based physical layer in virtual network embedding to optimise power consumption and optical spectrum utilization. In addition, virtual network embedding schemes aimed at profit maximization for cloud infrastructure providers as well greenhouse gas emission reduction in cloud infrastructure networks have been investigated. GreenTouch, a consortium of industrial and academic experts on energy efficiency in ICTs, has adopted the work in this thesis as one of the measures of improving energy efficiency in core networks