Distributed and Load-Adaptive Self Configuration in Sensor Networks

Proactive self-configuration is crucial for MANETs such as sensor networks, as these are often deployed in hostile environments and are ad hoc in nature. The dynamic architecture of the network is monitored by exchanging so-called Network State Beacons (NSBs) between key network nodes. The Beacon Exchange rate and the network state define both the time and nature of a proactive action to combat network performance degradation at a time of crisis. It is thus essential to optimize these parameters for the dynamic load profile of the network. This paper presents a novel distributed adaptive optimization Beacon Exchange selection model which considers distributed network load for energy efficient monitoring and proactive reconfiguration of the network. The results show an improvement of 70% in throughput, while maintaining a guaranteed quality-of- service for a small control-traffic overhead

Energy Efficiency of P2P and Distributed Clouds Networks

Since its inception, the Internet witnessed two major approaches to communicate digital content to end users: peer to peer (P2P) and client/server (C/S) networks. Both approaches require high bandwidth and low latency physical underlying networks to meet the users’ escalating demands. Network operators typically have to overprovision their systems to guarantee acceptable quality of service (QoS) and availability while delivering content. However, more physical devices led to more ICT power consumption over the years. An effective approach to confront these challenges is to jointly optimise the energy consumption of content providers and transportation networks. This thesis proposes a number of energy efficient mechanisms to optimise BitTorrent based P2P networks and clouds based C/S content distribution over IP/WDM based core optical networks. For P2P systems, a mixed integer linear programming (MILP) optimisation, two heuristics and an experimental testbed are developed to minimise the power consumption of IP/WDM networks that deliver traffic generated by an overlay layer of homogeneous BitTorrent users. The approach optimises peers’ selection where the goal is to minimise IP/WDM network power consumption while maximising peers download rate. The results are compared to typical C/S systems. We also considered Heterogeneous BitTorrent peers and developed models that optimise P2P systems to compensate for different peers behaviour after finishing downloading. We investigated the impact of core network physical topology on the energy efficiency of BitTorrent systems. We also investigated the power consumption of Video on Demand (VoD) services using CDN, P2P and hybrid CDN-P2P architectures over IP/WDM networks and addressed content providers efforts to balance the load among their data centres. For cloud systems, a MILP and a heuristic were developed to minimise content delivery induced power consumption of both clouds and IP/WDM networks. This was done by optimally determining the number, location and internal capability in terms of servers, LAN and storage of each cloud, subject to daily traffic variation. Different replication schemes were studied revealing that replicating content into multiple clouds based on content popularity is the optimum approach with respect to energy. The model was extended to study Storage as a Service (StaaS). We also studied the problem of virtual machine placement in IP/WDM networks and showed that VM Slicing is the best approach compared to migration and replication schemes to minimise energy. Finally, we have investigated the utilisation of renewable energy sources represented by solar cells and wind farms in BitTorrent networks and content delivery clouds, respectively. Comprehensive modelling and simulation as well as experimental demonstration were developed, leading to key contributions in the field of energy efficient telecommunications

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

Energy Efficient Nano Servers Provisioning for Information Piece Delivery in a Vehicular Environment

In this paper, we propose energy efficient Information Piece Delivery (IPD) through Nano Servers (NSs) in a vehicular network. Information pieces may contain any data that needs to be communicated to a vehicle. The available power (renewable or non-renewable) for a NS is variable. As a result, the service rate of a NS varies linearly with the available energy within a given range. Our proposed system therefore exhibits energy aware rate adaptation (RA), which uses variable transmission energy. We have also developed another transmission energy saving method for comparison, where sleep cycles (SC) are employed. Both methods are compared against an acceptable download time. To reduce the operational energy, we first optimise the locations of the NSs by developing a mixed integer linear programming (MILP) model, which takes into account the hourly variation of the traffic. The model is validated through a Genetic Algorithm (GA1). Furthermore, to reduce the gross delay over the entire vehicular network, the available renewable energy (wind farm) is optimally allocated to each NS according to piece demand. This, in turn, also reduces the network carbon footprint. A Genetic Algorithm (GA2) is also developed to validate the MILP results associated with this system. Through transmission energy savings, RA and SC further reduce the NSs energy consumption by 19% and 18% respectively, however at the expense of higher download time. MILP model 4 (with RA) and model 5 (with SC) reduced the delay by 81% and 83% respectively, while minimising the carbon footprint by 96% and 98% respectively, compared to the initial MILP model

Disaster Resilient Optical Core Networks

During the past few years, the number of catastrophic disasters has increased and its impact sometimes incapacitates the infrastructures within a region. The communication network infrastructure is one of the affected systems during these events. Thus, building a resilient network backbone is essential due to the big role of networks during disaster recovery operations. In this thesis, the research efforts in building a disaster-resilient network are reviewed and open issues related to building disaster-resilient networks are discussed. Large size disasters not necessarily impact the communication networks, but instead it can stimulate events that cause network performance degradation. In this regard, two open challenges that arise after disasters are considered one is the short-term capacity exhaustion and the second is the power outage. First, the post-disaster traffic floods phenomena is considered. The impact of the traffic floods on the optical core network performance is studied. Five mitigation approaches are proposed to serve these floods and minimise the incurred blocking. The proposed approaches explore different technologies such as excess or overprovisioned capacity exploitation, traffic filtering, protection paths rerouting, rerouting all traffic and finally using the degrees of freedom offered by differentiated services. The mitigation approaches succeeded in reducing the disaster induced traffic blocking. Second, advance reservation provisioning in an energy-efficient approach is developed. Four scenarios are considered to minimise power consumption. The scenarios exploit the flexibility provided by the sliding-window advance reservation requests. This flexibility is studied through scheduling and rescheduling scenarios. The proposed scenarios succeeded in minimising the consumed power. Third, the sliding-window flexibility is exploited for the objective of minimising network blocking during post-disaster traffic floods. The scheduling and rescheduling scenarios are extended to overcome the capacity exhaustion and improve the network blocking. The proposed schemes minimised the incurred blocking during traffic floods by exploiting sliding window. Fourth, building blackout resilient networks is proposed. The network performance during power outages is evaluated. A remedy approach is suggested for maximising network lifetime during blackouts. The approach attempts to reduce the required backup power supply while minimising network outages due to limited energy production. The results show that the mitigation approach succeeds in keeping the network alive during a blackout while minimising the required backup power

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

A fast robust optimization-based heuristic for the deployment of green virtual network functions

Network Function Virtualization (NFV) has attracted a lot of attention in the telecommunication field because it allows to virtualize core-business network functions on top of a NFV Infrastructure. Typically, virtual network functions (VNFs) can be represented as chains of Virtual Machines (VMs) or containers that exchange network traffic which are deployed inside datacenters on commodity hardware. In order to achieve cost efficiency, network operators aim at minimizing the power consumption of their NFV infrastructure. This can be achieved by using the minimum set of physical servers and networking equipment that are able to provide the quality of service required by the virtual functions in terms of computing, memory, disk and network related parameters. However, it is very difficult to predict precisely the resource demands required by the VNFs to execute their tasks. In this work, we apply the theory of robust optimization to deal with such parameter uncertainty. We model the problem of robust VNF placement and network embedding under resource demand uncertainty and network latency constraints using robust mixed integer optimization techniques. For online optimization, we develop fast solution heuristics. By using the virtualized Evolved Packet Core as use case, we perform a comprehensive evaluation in terms of performance, solution time and complexity and show that our heuristic can calculate robust solutions for large instances under one second.Peer ReviewedPostprint (author's final draft

Energy efficiency in content delivery networks

The increasing popularity of bandwidth-intensive video Internet services has positioned Content Distribution Networks (CDNs) in the limelight as the emerging provider platforms for video delivery. The goal of CDNs is to maximise the availability of content in the network while maintaining the quality of experience expected by users. This is a challenging task due to the scattered nature of video content sources and destinations. Furthermore, the high energy consumption associated with content distribution calls for developing energy-efficient solutions able to cater for the future Internet. This thesis addresses the problem of content placement and update while considering energy consumption in CDNs. First, this work contributed a new energy-efficient caching scheme that stores the most popular content at the edge of the core network and optimises the size of cached content to minimise energy usage. It takes into account the trend of daily traffic and recommends putting inactive segments of caches in sleep-mode during off-peak hours. Our results showed that power minimisation is achieved by deploying switch-off capable caches, and the trend of active cache segments over the time of day follows the trend of traffic. Second, the study explores different content popularity distributions and determines their influence on power consumption. The distribution of content popularity dictates the resultant cache hit ratio achieved by storing a certain number of videos. Therefore, it directly influences the power consumption of the cache. The evaluation results indicated that under video services where the popularity of content is very diverse, the optimum solution is to store the few most popular videos in caches. In contrast, when video popularities are similar, the most power efficient scheme is either to cache the whole library or to avoid caching completely depending on the size of the video library. Third, this thesis contributed an evaluation of the power consumption of the network under real world TV data and considering standard and high definition TV programmes. We proposed a cache replacement algorithm based on the predictable nature of TV viewings. The time-driven proactive cache replacement algorithm replaces cache contents several times a day to minimise power consumption. The algorithm achieves major power savings on top of the power reductions introduced by caching. CDNs are expected to continue to be the backbone for Internet video applications. This work has shown that storing the right amount of popular videos in core caches reduces from 42% to 72% of network power consumption considering a range of content popularity distributions. Maintaining up-to-date cache contents reduces up to 48% and 86% of power consumption considering fixed and sleep-mode capable caches, respectively. Reducing the energy consumption of CDNs provides a valuable contribution for future green video delivery

Enabling Hardware Green Internet of Things: A review of Substantial Issues

Between now and the near future, the Internet of Things (IoT) will redesign the socio-ecological morphology of the human terrain. The IoT ecosystem deploys diverse sensor platforms connecting millions of heterogeneous objects through the Internet. Irrespective of sensor functionality, most sensors are low energy consumption devices and are designed to transmit sporadically or continuously. However, when we consider the millions of connected sensors powering various user applications, their energy efficiency (EE) becomes a critical issue. Therefore, the importance of EE in IoT technology, as well as the development of EE solutions for sustainable IoT technology, cannot be overemphasised. Propelled by this need, EE proposals are expected to address the EE issues in the IoT context. Consequently, many developments continue to emerge, and the need to highlight them to provide clear insights to researchers on eco-sustainable and green IoT technologies becomes a crucial task. To pursue a clear vision of green IoT, this study aims to present the current state-of-the art insights into energy saving practices and strategies on green IoT. The major contribution of this study includes reviews and discussions of substantial issues in the enabling of hardware green IoT, such as green machine to machine, green wireless sensor networks, green radio frequency identification, green microcontroller units, integrated circuits and processors. This review will contribute significantly towards the future implementation of green and eco-sustainable IoT