A Survey of Green Networking Research

Reduction of unnecessary energy consumption is becoming a major concern in wired networking, because of the potential economical benefits and of its expected environmental impact. These issues, usually referred to as "green networking", relate to embedding energy-awareness in the design, in the devices and in the protocols of networks. In this work, we first formulate a more precise definition of the "green" attribute. We furthermore identify a few paradigms that are the key enablers of energy-aware networking research. We then overview the current state of the art and provide a taxonomy of the relevant work, with a special focus on wired networking. At a high level, we identify four branches of green networking research that stem from different observations on the root causes of energy waste, namely (i) Adaptive Link Rate, (ii) Interface proxying, (iii) Energy-aware infrastructures and (iv) Energy-aware applications. In this work, we do not only explore specific proposals pertaining to each of the above branches, but also offer a perspective for research.Comment: Index Terms: Green Networking; Wired Networks; Adaptive Link Rate; Interface Proxying; Energy-aware Infrastructures; Energy-aware Applications. 18 pages, 6 figures, 2 table

Energy-efficient bandwidth reservation for bulk data transfers in dedicated wired networks

International audienceThe ever increasing number of Internet connected end-hosts call for high performance end-to-end networks leading to an increase in the energy consumed by the networks. Our work deals with the energy consumption issue in dedicated network with bandwidth provisionning and in-advance reservations of network equipments and bandwidth for Bulk Data transfers. First, we propose an end-to-end energy cost model of such networks which described the energy consumed by a transfer for all the crossed equipments. This model is then used to develop a new energy-aware framework adapted to Bulk Data Transfers over dedicated networks. This framework enables switching off unused network portions during certain periods of time to save energy. This framework is also endowed with prediction algorithms to avoid useless switching off and with adaptive scheduling management to optimize the energy used by the transfers. 1 Introductio

Research challenges on energy-efficient networking design

The networking research community has started looking into key questions on energy efficiency of communication networks. The European Commission activated under the FP7 the TREND Network of Excellence with the goal of establishing the integration of the EU research community in green networking with a long perspective to consolidate the European leadership in the field. TREND integrates the activities of major European players in networking, including manufacturers, operators, research centers, to quantitatively assess the energy demand of current and future telecom infrastructures, and to design energy-efficient, scalable and sustainable future networks. This paper describes the main results of the TREND research community and concludes with a roadmap describing the next steps for standardization, regulation agencies and research in both academia and industry.The research leading to these results has received funding from the EU 7th Framework Programme (FP7/2007–2013) under Grant Agreement No. 257740 (NoE TREND)

Cross-Layer Platform for Dynamic, Energy-Efficient Optical Networks

The design of the next-generation Internet infrastructure is driven by the need to sustain the massive growth in bandwidth demands. Novel, energy-efficient, optical networking technologies and architectures are required to effectively meet the stringent performance requirements with low cost and ultrahigh energy efficiencies. In this thesis, a cross-layer communications platform is proposed to enable greater intelligence and functionality on the physical layer. Providing the optical layer with advanced networking capabilities will facilitate the dynamic management and optimization of optical switching based on performance monitoring measurements and higher-layer attributes. The cross-layer platform aims to create a new framework for networks to incorporate packet-scale measurement subsystems and techniques for monitoring the health of the optical channel. This will allow for quality-of-service- and energy-aware routing schemes, as well as an enhanced awareness of the optical data signals. This thesis first presents the design and development of an optical packet switching fabric. Leveraging a networking test-bed environment to validate networking hypotheses, advanced switching functionalities are demonstrated, including the support for quality-of-service based routing and packet multicasting. The investigated cross-layering is based on emerging optical technologies, enabling packet protection techniques and packet-rate switching fabric reconfiguration. Coupled with fast performance monitoring, the platform will achieve significant performance gains within the endeavor of all-optical switching. Allowing for a more intelligent, programmable optical layer aims to support greater flexibility with respect to bandwidth allocation and potentially a significant reduction in the network's energy consumption. The ultimate deliverable of this work is a high-performance, cross-layer enabled optical network node. The experimental demonstration of an initial prototype creates a dynamic network element with distributed control plane management, featuring fast packet-rate optical switching capabilities and embedded physical-layer performance monitoring modules. The cross-layer box enables an intelligent traffic delivery system that can dynamically manipulate optical switching on a packet-granular scale. With the goal of achieving advanced multi-layer routing and control algorithms, the network node requires an intelligent co-optimization across all the layers. The proposed cross-layer design should drive optical technologies and architectures in an innovative way, in order to fulfill the void between the design of basic photonic devices and the networking protocols that use them. The performance of the entire network -- from the optical components, to the routing algorithms and user applications -- should be optimized in concert. This contribution to the area of cross-layer network design creates an adaptable optical pipe that is extremely flexible and intelligent aware of both the physical optical signals and higher-layer requirements. The impact of this work will be seen in the realization of dynamic, energy-efficient optical communication links in future networking infrastructures

Energy Efficient Big Data Networks

The continuous increase of big data applications in number and types creates new challenges that should be tackled by the green ICT community. Data scientists classify big data into four main categories (4Vs): Volume (with direct implications on power needs), Velocity (with impact on delay requirements), Variety (with varying CPU requirements and reduction ratios after processing) and Veracity (with cleansing and backup constraints). Each V poses many challenges that confront the energy efficiency of the underlying networks carrying big data traffic. In this work, we investigated the impact of the big data 4Vs on energy efficient bypass IP over WDM networks. The investigation is carried out by developing Mixed Integer Linear Programming (MILP) models that encapsulate the distinctive features of each V. In our analyses, the big data network is greened by progressively processing big data raw traffic at strategic locations, dubbed as processing nodes (PNs), built in the network along the path from big data sources to the data centres. At each PN, raw data is processed and lower rate useful information is extracted progressively, eventually reducing the network power consumption. For each V, we conducted an in-depth analysis and evaluated the network power saving that can be achieved by the energy efficient big data network compared to the classical approach. Along the volume dimension of big data, the work dealt with optimally handling and processing an enormous amount of big data Chunks and extracting the corresponding knowledge carried by those Chunks, transmitting knowledge instead of data, thus reducing the data volume and saving power. Variety means that there are different types of big data such as CPU intensive, memory intensive, Input/output (IO) intensive, CPU-Memory intensive, CPU/IO intensive, and memory-IO intensive applications. Each type requires a different amount of processing, memory, storage, and networking resources. The processing of different varieties of big data was optimised with the goal of minimising power consumption. In the velocity dimension, we classified the processing velocity of big data into two modes: expedited-data processing mode and relaxed-data processing mode. Expedited-data demanded higher amount of computational resources to reduce the execution time compared to the relaxed-data. The big data processing and transmission were optimised given the velocity dimension to reduce power consumption. Veracity specifies trustworthiness, data protection, data backup, and data cleansing constraints. We considered the implementation of data cleansing and backup operations prior to big data processing so that big data is cleansed and readied for entering big data analytics stage. The analysis was carried out through dedicated scenarios considering the influence of each V’s characteristic parameters. For the set of network parameters we considered, our results for network energy efficiency under the impact of volume, variety, velocity and veracity scenarios revealed that up to 52%, 47%, 60%, 58%, network power savings can be achieved by the energy efficient big data networks approach compared to the classical approach, respectively

Framework For Performance Analysis of Optical Circuit Switched Network Planning Algorithms

Projecte final de carrera realitzat en col.laboració amb Ecole Polytechnique Fédérale de Lausann

Energy Efficient Distributed Processing for IoT

The number of connected objects in the Internet of Things (IoT) is growing exponentially. IoT devices are expected to number between 26 billion to 50 billion devices by 2020 and this figure can grow even further due to the production of miniaturised portable devices that are lightweight, energy and cost efficient together with the widespread use of the Internet and the added value organisations and individuals can gain from IoT devices, if their data is processed. These connected objects are expected to be used in multitudes of applications, of which, some are, highly resource intensive such as visual processing services for surveillance based object recognition applications. The sensed data requires processing by the cloud in order to extract knowledge and make decisions accordingly. Given the pervasiveness of future IoT-based visual processing applications, massive amounts of data will be collected due to the nature of multimedia files. Transporting all that collected data to the cloud at the core of the network, is prohibitively costly, in terms of energy consumption. Hence, to tackle the aforementioned challenges, distributed processing is proposed by academia and industry to make use of a large number of devices located in the edge of the network to process some or all of the data before it gets to the cloud. Due to the heterogeneity of the devices in the edge of the network, it is crucial to develop energy efficient models that take care of resource provisioning optimally. The focus in today’s network design and development has shifted towards energy efficiency, due to the rising cost of electricity, resource scarcity and increasing emission of carbon dioxide (CO2). This thesis addresses some of the challenges associated with service placement in a distributed architecture such as the fog. First, a Passive Optical Network (PON) is used to connect IoT devices and to support the fog infrastructure. A metro network is also used to connect to the fog and aggregate traffic from the PON towards the core network. An IP/WDM backbone network is considered to model the core layer and to interconnect the cloud data centres. The entire network was modelled and optimised through Mixed Integer Linear Programming (MILP) and the total end to end power consumption was jointly minimised for processing and networking. Two aspects of service placements were examined: 1) non-splitable services, and 2) splitable services. The results obtained showed that, in the capacitated problem, service splitting introduced power consumption savings of up to 86% compared to 46% with non-splitable services. Moreover, an energy efficient special purposed data centre (SP-DC) was deployed in addition to its general purpose counterpart (GP-DC). The results showed that, for very high demands, power savings of up to 50% could be achieved compared to 30% without SP-DC. The performance of the proposed architecture was further examined by considering additional dimensions to the problem of service placements such as resiliency dimension in terms of 1+1 server protection, in the long term network design problem (un-capacitated) and the impact of inter-service synchronisation overhead on the total number service splits per task

Telecommunications Networks

This book guides readers through the basics of rapidly emerging networks to more advanced concepts and future expectations of Telecommunications Networks. It identifies and examines the most pressing research issues in Telecommunications and it contains chapters written by leading researchers, academics and industry professionals. Telecommunications Networks - Current Status and Future Trends covers surveys of recent publications that investigate key areas of interest such as: IMS, eTOM, 3G/4G, optimization problems, modeling, simulation, quality of service, etc. This book, that is suitable for both PhD and master students, is organized into six sections: New Generation Networks, Quality of Services, Sensor Networks, Telecommunications, Traffic Engineering and Routing

Energy Efficient Network Function Virtualisation in 5G Networks

Once the dust settled around 4G, 5G mobile networks become the buzz word in the world of communication systems. The recent surge of bandwidth-greedy applications and the proliferation of smart phones and other wireless connected devices has led to an enormous increase in mobile traffic. Therefore, 5G networks have to deal with a huge number of connected devices of different types and applications, including devices running life-critical applications, and facilitate access to mobile resources easily. Therefore given the increase in traffic and number of connected devices, intelligent and energy efficient architectures are needed to adequately and sustainably meet these requirements. In this thesis network function virtualisation is investigated as a promising paradigm that can contribute to energy consumption reduction in 5G networks. The work carried out in this thesis considers the energy efficiency mainly in terms of processing power consumption and network power consumption. Furthermore, it considers the energy consumption reduction that can be achieved by optimising the locations of virtual machines running the mobile 5G network functions. It also evaluates the consolidation and pooling of the mobile resources. A framework was introduced to virtualise the mobile core network functions and baseband processing functions. Mixed integer linear programming optimisation models and heuristics were developed minimise the total power consumption. The impact of virtualisation in the 5G front haul and back haul passive optical network was investigated by developing MILP models to optimise the location of virtual machines. A further consideration is caching the contents close to the user and its impact on the total power consumption. The impact of a number of factor on the power consumption were investigated such as the total number of active users, the backhaul to the fronthaul traffic ratio, reduction/expansion in the traffic due to baseband processing, and the communication between virtual machines. Finally, the integration of network function virtualisation and content caching were introduced and their impact on improving the energy efficiency was investigated