Au cours des dernières années, la croissance des architectures de réseaux de télécommunication a rapidement augmenté pour suivre un trafic en plein essor. En outre, leur consommation d'énergie est devenue un enjeu Important, tant pour son impact économique qu'écologique. De multiples approches ont été proposées pour la réduire. Dans cette thèse, nous nous concentrons sur l'approche Energy Aware Routing (EAR) qui consiste à fournir un routage valide tout en diminuant le nombre d'équipements réseau actifs. Cependant, les réseaux actuels ne sont pas adaptés au déploiement de politiques vertes globales en raison de leur gestion distribuée et de la nature fermée des périphériques réseau actuels. Les paradigmes de Software Defined Network (SDN) et de Network Function Virtualization (NFV) promettent de faciliter le déploiement de politiques vertes. En effet, le premier sépare le plan de contrôle et de données et offre donc une gestion centralisée du réseau. Le second propose de découpler le logiciel et le matériel des fonctions réseau et permet une plus grande flexibilité dans la création et la gestion des services réseau. Dans cette thèse, nous nous concentrons sur les défis posés par ces paradigmes pour le déploiement de politiques EAR. Nous consacrons les deux premières parties aux SDNs. Nous étudions d'abord les contraintes de taille de table de routage causées par la complexité accrue des règles, puis le déploiement progressif de périphériques SDN dans un réseau actuel. Nous concentrons notre attention sur NFV dans la dernière partie, et plus particulièrement nous étudions les chaines de fonctions de services. / In the recent years, the growth of the architecture of telecommunication networks has been quickly increasing to keep up with a booming traffic. Moreover, the energy consumption of these infrastructures is becoming a growing issue, both for its economic and ecological impact. Multiple approaches were proposed to reduce the networks' power consumption such as decreasing the number of active elements. Indeed, networks are designed to handle high traffic, e.g., during the day, but are over-provisioned during the night. In this thesis, we focus on disabling links and routers inside the network while keeping a valid routing. This approach is known as Energy Aware Routing (EAR). However current networks are not adapted to support the deployment of network-wide green policies due to their distributed management and the black-box nature of current network devices. The SDN and NFV paradigms bear the promise of bringing green policies to reality. The first one decouples the control and data plane and thus enable a centralized control of the network. The second one proposes to decouple the software and hardware of network functions and allows more flexibility in the creation and management of network services. In this thesis, we focus on the challenges brought by these two paradigms for the deployment of EAR policies. We dedicated the first two parts to the SDN paradigm. We first study the forwarding table size constraints due to an Increased complexity of rules. We then study the progressive deployment of SDN devices alongside legacy ones. We focus our attention on the NFV paradigm in the last part, and more particularly, we study the Service Function Chaining problem.

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The operational life of wireless sensor network based distributed sensing systems is limited by the energy provided by a portable battery pack. Owing to the inherently resource constrained nature of wireless sensor networks and nodes, a major research thrust in this field is the search for energy-aware methods of operation. Communication is among the most energy-intensive operations on a wireless device. It is therefore, the focus of our efforts to develop an energy-aware method of communication and to introduce a degree of reconfigurability to ensure autonomous operation of such devices. Given this background, three research tasks have been identified and investigated during the course of this research. 1) Devising an energy-efficient method of communication in a framework of reconfigurable operation: The dependence of the energy consumed during communication on the number of bits transmitted (and received) was identified from prior research work. A novel method of data compression was designed to exploit this dependence. This method uses the time-limited, orthonormal Walsh functions as basis functions for representing signals. The L2 norm of this representation is utilized to further compress the signals. From Parseval’s relation, the square of the L2 norm represents the energy content of a signal. The application of this theorem to our research makes it possible to use the L2 norm as a control knob. The operation of this control knob makes it possible to optimize the number of terms required to represent signals. The time-limited nature of the Walsh functions was leveraged to inject dynamic behaviour into our coding method. This time-limited nature allows decomposition of finite time-segments, without attendant limitations like loss of resolution that are inherent to derived, discrete transforms like the discrete Fourier transform or the discrete time Fourier transform. This decomposition over successive, finite time-segments, coupled with innovative operation of the previously mentioned control knob on every segment, gives us a dynamic scaling technique. The amount of data to be transmitted is in turn based on the magnitude of the coefficients of decomposition of each time-segment, leading to the realization of a variable word length coding method. This dynamic coding method can identify evolving changes or events in the quantity being sensed. The coefficients of decomposition represent features present in successive time-segments of signals and therefore enable identification of evolving events. The ability to identify events as they occur enables the algorithm to react to events as they evolve in the system. In other words the data transmission and the associated energy consumption are imparted a reconfigurable, event-driven nature by implementation of the coding algorithm. Performance evaluation of this method via simulations on machine generated (bearing vibration) and biometric (electro-cardio gram) signals shows it be a viable method for energy-aware communication. 2) Developing a framework for reconfigurable triggering: A framework for completely autonomous triggering of the coding method has been developed. This is achieved by estimating correlations of the signal with the representative Walsh functions. The correlation coefficient of a signal segment with a Walsh function gives a picture of the amount of energy localized by the function. This information is used to autonomously tune the abovementioned control knob or, in more proper terms, the degree of thresholding used in compression. Evaluation of this framework on bearing vibration and electro-cardio gram signals has shown results consistent with those of previous simulations. 3) Devising a computationally compact method of feature classification: A method of investigating time series measurements of dynamic systems in order to classify features buried in the signal measurements was investigated. The approach involves discretizing time-series measurements into strings of pre-defined symbols. These strings are transforms of the original time-series measurements and are a representation of the system dynamics. A method of statistically analyzing the symbol strings is presented and its efficacy is studied through representative simulations and experimental investigation of vibration signals recorded from a rolling bearing element. The method is computationally compact because it obviates the need for local signal processing tasks like denoising, detrending and amplification. Results indicate that the method can effectively classify deteriorating machine health, changing operating conditions and evolving defects. In addition to these major foci, another research task was the design and implementation of a wireless network testbed. This testbed consists of a network of netbooks, connected together wirelessly and was utilized for experimental verification of the variable word length coding method.

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Green computing techniques aim to reduce the power foot print of modern embedded devices with particular emphasis on processors, the power hot-spots of these devices. In this thesis we propose compiler-driven and profile-driven optimizations that reduce power consumption in a modern embedded processor. We show that these optimizations reduce power consumption in functional units and memory subsystems with very low performance loss. We present three new techniques to reduce power consumption in processors, namely, transition aware scheduling, leakage reduction in data caches using criticality analysis, and dynamic power reduction in data caches using locality analysis of data regions. A novel instruction scheduling technique to address leakage power consumption in functional units is proposed. This scheduling technique, transition aware scheduling, is motivated by idle periods that arise in the utilization of functional units during program execution. A continuously large idle period in a functional unit can be exploited to place the unit in low power state. This novel scheduling algorithm increases the duration of idle periods without hampering performance and drives power gating in these periods. A power model defined with idle cycles as a parameter shows that this technique saves up to 25% of leakage power with very low performance impact. In modern embedded programs, data regions can be classified as critical and non-critical. Critical data regions significantly impact the performance. A new technique to identify such data regions through profiling is proposed. This technique along with a new criticality based cache policy is used to control the power state of the data cache. This scheme allocates non-critical data regions to low-power cache regions, thereby reducing leakage power consumption by up to 40% without compromising on the performance. This profiling technique is extended to identify data regions that have low locality. Some data regions have high data reuse. A locality based cache policy based on cache parameters like size and associativity is proposed. This scheme reduces dynamic as well as static power consumption in the cache subsystem. This optimization reduces 25% of the total power consumption in the data caches without hampering the execution time. In this thesis, the problem of power consumption of a program is decoupled from the number of processor cores. The underlying architecture model is simplified to abstract away a variety of processor scenarios. This simplified model can be scaled up to be implemented in various multi-core architecture models like Chip Multi-Processors, Simultaneous Multi-Threaded Processors, Chip Multi-Threaded Processors, to name a few. The three techniques proposed in this thesis leverage underlying hardware features like low power functional units, drowsy caches and split data caches. These techniques reduce power consumption of a wide range of benchmarks with low performance loss.

This work starts from the proposition that it is beneficial to conserve communication energy in Wireless Sensor Networks (WSNs). For WSNs there is an added incentive for energy-efficient communication. The power supply of a sensor is often finite and small. Replenishing the power may be impractical and is likely to be costly. Wireless Sensor Networks are an important area of research. Data about the physical environment may be collected from hostile or friendly environments. Data is then transmitted to a destination without the need for communication cables. There are power and resource constraints upon WSNs, in addition WSN networks are often application specific. Different applications will often have different requirements. Further, WSNs are a shared medium system. The features of the MAC (Medium Access Control) protocol together with the application behaviour shape the communication states of the node. As each of these states have different power requirements the MAC protocol impacts upon the operation and power consumption efficiency. This work focuses on the development of an energy conservation protocol for WSNs where direct communication between sources and a base station is feasible. Whilst the multi-hop approach has been regarded as the underlying communication paradigm in WSNs, there are some scenarios where direct communication is applicable and a significant amount of communication energy can be saved. The Power & Reliability Aware Protocol has been developed. Its main objectives are to provide efficient data communication by means of energy conservation without sacrificing required reliability. This has been achieved by using direct communication, adaptive power adaptation and intelligent scheduling. The results of simulations illustrate the significance of communication energy and adaptive transmission. The relationship between Received Signal Strength Indicator (RSSI) and Packet Reception Rate (PRR) metrics is established and used to identify when power adaptation is required. The experimental results demonstrate an optimal region where lower power can be used without further reduction in the PRR. Communication delays depend upon the packet size whilst two-way propagation delay is very small. Accurate scheduling is achieved through monitoring the clock drift. A set of experiments were carried out to study benefits of direct vs. multi-hop communication. Significant transmitting current can be conserved if the direct communication is used. PoRAP is compared to Sensor-MAC (S-MAC), Berkeley-MAC (B-MAC) and Carrier Sense Multiple Access (CSMA). Parameter settings used in the Great Duck Island (GDI) a production habitat monitoring WSNs were applied. PoRAP consumes the least amount of energy.

Cloud Computing presents a novel way for businesses to procure their IT needs. Its elasticity and on-demand provisioning enables a shift from capital expenditures to operating expenses, giving businesses the technological agility they need to respond to an ever-changing marketplace. The rapid adoption of Cloud Computing, however, poses a unique challenge to Cloud providers—their already very large electricity bill and carbon footprint will get larger as they expand; managing both costs is therefore essential to their growth. This thesis squarely addresses the above challenge. Recognizing the presence of Cloud data centers in multiple locations and the differences in electricity price and emission intensity among these locations and over time, we develop an optimization framework that couples workload distribution with time-varying signals on electricity price and emission intensity for financial and environmental benefits. The framework is comprised of an optimization model, an aggregate cost function, and 6 scheduling heuristics. To evaluate cost savings, we run simulations with 5 data centers located across North America over a period of 81 days. We use historical data on electricity price, emission intensity, and workload collected from market operators and research data archives. We find that our framework can produce substantial cost savings, especially when workloads are distributed both geographically and temporally—up to 53.35% on electricity cost, or 29.13% on carbon cost, or 51.44% on electricity cost and 13.14% on carbon cost simultaneously.

Nowadays, computer systems are everywhere. From simple portable devices such as watches and MP3 players to large stationary installations that control nuclear power plants, computer systems are now present in all aspects of our modern and every-day life. In about only 70 years, they have completely perturbed our way of life and they reached a so high degree of sophistication that they will be soon capable of driving our cars and cleaning our houses without any human intervention. As computer systems gain in responsibilities, it becomes essential that they provide both safety and reliability. Indeed, a failure in systems such as the anti-lock braking system (ABS) in cars could threaten human lives and generate catastrophic and irreversible consequences. Hence, for many years, researchers have addressed these emerging problems of system safety and reliability which come along with this fulgurant evolution. This thesis provides a general overview of embedded real-time computer systems, i.e., a particular kind of computer system whose number grows daily. We provide the reader with some preliminary knowledge and a good understanding of the concepts that underlie this emerging technology. We focus especially on the theoretical problems related to the real-time issue and briefly summarizes the main solutions, together with their advantages and drawbacks. This brings the reader through all the conceptual layers constituting a computer system, from the software level---the logical part---that specifies both the system behavior and requirements to the hardware level---the physical part---that actually performs the expected treatments and reacts to the environment. In the meanwhile, we introduce the theoretical models that allow researchers for theoretical analyses which ensure that all the system requirements are fulfilled. Finally, we address the energy consumption problem in embedded systems. We describe the various factors of power dissipation in modern technologies and we introduce different solutions to reduce this consumption./Cette thèse se focalise sur un type de systèmes informatiques bien précis appelés “systèmes embarqués temps réel”. Un système est dit “embarqué” lorsqu’il est développé afin de servir un but bien précis. Un téléphone portable est un parfait exemple de système embarqué étant donné que toutes ses fonctionnalités sont rigoureusement définies avant même sa conception. Au contraire, un ordinateur personnel n’est généralement pas considéré comme un système embarqué, les concepteurs ne sachant pas à l’avance à quelles fins il sera utilisé. Une grande partie de ces systèmes embarqués ont des contraintes temporelles très fortes, ce qui les distingue encore plus des ordinateurs grand public. A titre d’exemple, lorsqu’un conducteur de voiture freine brusquement, l’ordinateur de bord déclenche l’application ABS et il est primordial que cette application soit traitée endéans une courte échéance. Autrement dit, cette fonctionnalité ABS doit être traitée prioritairement par rapport aux autres fonctionnalités du véhicule. Ce type de système embarqué est alors dit “temps réel”, dû à ces notions de temps et de priorités entre les applications. La problèmatique posée par les systèmes temps réel est la suivante. Comment déterminer, à tout moment, un ordre d’exécution des différentes fonctionnalités de telle sorte qu’elles soient toutes exécutées entièrement endéans leur échéance ? De plus, avec l’apparition récente des systèmes multiprocesseurs, cette problématique s’est fortement complexifiée, vu que le système doit à présent déterminer quelle fonctionnalité s’exécute à quel moment sur quel processeur afin que toutes les contraintes temporelles soient respectées. Pour finir, ces systèmes embarqués temp réel multiprocesseurs se sont rapidement retrouvés confrontés à un problème de consommation d’énergie. Leur demande en terme de performance (et donc en terme d’énergie) à évolué beaucoup plus rapidement que la capacité des batteries qui les alimentent. Ce problème est actuellement rencontré par de nombreux systèmes, tels que les téléphones portables par exemple. L’objectif de cette thèse est de parcourir les différents composants de tels système embarqués et de proposer des solutions afin de réduire leur consommation d’énergie.

Cloud Computing presents a novel way for businesses to procure their IT needs. Its elasticity and on-demand provisioning enables a shift from capital expenditures to operating expenses, giving businesses the technological agility they need to respond to an ever-changing marketplace. The rapid adoption of Cloud Computing, however, poses a unique challenge to Cloud providers—their already very large electricity bill and carbon footprint will get larger as they expand; managing both costs is therefore essential to their growth. This thesis squarely addresses the above challenge. Recognizing the presence of Cloud data centers in multiple locations and the differences in electricity price and emission intensity among these locations and over time, we develop an optimization framework that couples workload distribution with time-varying signals on electricity price and emission intensity for financial and environmental benefits. The framework is comprised of an optimization model, an aggregate cost function, and 6 scheduling heuristics. To evaluate cost savings, we run simulations with 5 data centers located across North America over a period of 81 days. We use historical data on electricity price, emission intensity, and workload collected from market operators and research data archives. We find that our framework can produce substantial cost savings, especially when workloads are distributed both geographically and temporally—up to 53.35% on electricity cost, or 29.13% on carbon cost, or 51.44% on electricity cost and 13.14% on carbon cost simultaneously.