Cloud computing data centers are becoming increasingly popular for the provisioning of computing resources. The cost and operating expenses of data centers have skyrocketed with the increase in computing capacity. Several governmental, industrial, and academic surveys indicate that the energy utilized by computing and communication units within a data center contributes to a considerable slice of the data center operational costs. In this paper, we present a simulation environment for energy-aware cloud computing data centers. Along with the workload distribution, the simulator is designed to capture details of the energy consumed by data center components (servers, switches, and links) as well as packet-level communication patterns in realistic setups. The simulation results obtained for two-tier, three-tier, and three-tier high-speed data center architectures demonstrate the effectiveness of the simulator in utilizing power management schema, such as voltage scaling, frequency scaling, and dynamic shutdown that are applied to the computing and networking components.
International audience ; The number of civilian and military applications using Unmanned Aerial Vehicles (UAVs) has increased during the last years and the forecasts for upcoming years are exponential. One of the current major challenges consist in considering UAVs as autonomous swarms to address some limitations of single UAV usage such as autonomy, range of operation and resilience. In this article we propose novel mobility models for multi-level swarms of collaborating UAVs used for the coverage of a given area. These mobility models generate unpredictable trajectories using a chaotic solution of a dynamical system. We detail how the chaotic properties are used to structure the exploration of an unknown area and enhance the exploration part of an Ant Colony Optimization method. Empirical evidence of the improvement of the coverage efficiency obtained by our mobility models is provided via simulation. It clearly outperforms state-of-the-art approaches.
International audience ; The development and usage of Unmanned Aerial Vehicles (UAVs) quickly increased in the last decades, mainly for military purposes. Nowadays, this type of technology is used in non-military contexts mainly for civil and environment protection: search & rescue teams, fire fighters, police officers , environmental scientific studies, etc. Although the technology for operating a single UAV is now mature, additional efforts are still necessary for using UAVs in fleets (or swarms). This position paper presents the ASIMUT project (Aid to SItuation Management based on MUltimodal, MUltiUAVs, MUltilevel acquisition Techniques). The challenges of this project consist of handling several fleets of UAVs including communication, networking and positioning aspects. This motivates the development of novel multilevel cooperation algorithms which is an area that has not been widely explored , especially when autonomy is an additional challenge. Moreover, we will provide techniques to optimize communications for multilevel swarms. Finally, we will develop distributed and localized mobility management algorithms that will cope with conflicting objectives such as connectiv-ity maintenance and geographical area coverage.
International audience ; The development and usage of Unmanned Aerial Vehicles (UAVs) quickly increased in the last decades, mainly for military purposes. Now, this type of technology is also used in non-military contexts mainly for civil and environment protection: search & rescue teams, fire fighters, police officers, environmental scientific studies, etc. Although the technology for operating a single UAV is now mature, additional efforts are still necessary for using UAVs in fleets (or swarms). Therefore the ASIMUT project (Aid to SItuation Management based on MUltimodal, MUltiUAVs, MUltilevel acquisition Techniques). The major challenge of this project consists in handling several fleets of UAVs including communication, networking and positioning aspects. This motivates the development of novel multilevel cooperation algorithms which have not been widely explored, especially when autonomy is an additional challenge. Techniques to optimize communications for multilevel swarms are also required. Finally, distributed andlocalized mobility management algorithms that cope with conflicting objectives such as connectivity maintenance and geographical areacoverage must be provided.
International audience ; Unmanned Aerial Vehicles (UAVs) applications have seen an important increase in the last decade for both military and civilian applications ranging from fire and high seas rescue to military surveillance and target detection. While this technology is now mature for a single UAV, new methods are needed to operate UAVs in swarms, also referred to as fleets. This work focuses on the mobility management of one single autonomous swarm of UAVs which mission is to cover a given area in order to collect information. Several constraints are applied to the swarm to solve this problem due to the military context. First, the UAVs mobility must be as unpredictable as possible to prevent any UAV tracking. However the Ground Control Station (GCS) operator(s) still needs to be able to forecast the UAVs paths. Finally, the UAVs are autonomous in order to guarantee the mission continuity in a hostile environment and the method must be distributed to ensure fault-tolerance of the system. To solve this problem, we introduce the Chaotic Ant Colony Optimization to Coverage (CACOC) algorithm that combines an Ant Colony Optimization approach (ACO) with a chaotic dynamical system. CACOC permits to obtain a deterministic but unpredictable system. Its performance is compared to other state-of-the art models from the literature using several coverage-related metrics, i.e. coverage rate, recent coverage and fairness. Numerical results obtained by simulation underline the performance of our CACOC method: a deterministic method with unpredictable le UAV trajectories that still ensures a high area coverage.
The ongoing increase of energy consumption by IT infrastructures forces data center managers to find innovative ways to improve energy efficiency. The latter is also a focal point for different branches of computer science due to its financial, ecological, political, and technical consequences. One of the answers is given by scheduling combined with dynamic voltage scaling technique to optimize the energy consumption. The way of reasoning is based on the link between current semiconductor technologies and energy state management of processors, where sacrificing the performance can save energy. This paper is devoted to investigate and solve the multi-objective precedence constrained application scheduling problem on a distributed computing system, and it has two main aims: the creation of general algorithms to solve the problem and the examination of the problem by means of the thorough analysis of the results returned by the algorithms. The first aim was achieved in two steps: adaptation of state-of-the-art multi-objective evolutionary algorithms by designing new operators and their validation in terms of performance and energy. The second aim was accomplished by performing an extensive number of algorithms executions on a large and diverse benchmark and the further analysis of performance among the proposed algorithms. Finally, the study proves the validity of the proposed method, points out the best-compared multi-objective algorithm schema, and the most important factors for the algorithms performance.
In the near future, more than two thirds of the world's population is expected to be living in cities and hence, with the aim of being proactive and finding innovative and sustainable solutions, governments have made smart cities one of their priority areas of research. Smart cities are sustainable, inclusive and prosperous greener cities that foster enabling smart Information and Communication Technologies (smart ICT) like Internet-of-Things (IoT), cloud computing and big data to facilitate services such as mobility, governance, utility and energy management. As these services depend heavily on data collected by sensors, Unmanned Aerial Vehicles (UAVs) have quickly become one of the promising IoT devices for smart cities thanks to their mobility, agility and customizability of onboard sensors. UAVs found use in a wide array of applications expanding beyond military to more commercial ones, ranging from monitoring, surveillance, mapping to parcel delivery and more demanding applications that require UAVs to operate in heterogeneous swarms in a shared low-altitude airspace over populated cities. However, as the number of UAVs continues to grow and as their sensing, actuation, communication and control capabilities become increasingly sophisticated, UAV deployment in smart cities is faced with a set of fundamental challenges in their safe operation and management. These challenges emphasize the need for establishing globally-harmonised regulations and internationally-agreed-upon technical standards to govern the rapid technological advancements, as well as ensure a fair economy by encouraging market competition and lowering barriers to entry for newcomers. As various Standardisation Development Organisations (SDOs) recently recognised the need, importance and potential of such regulations, most have established dedicated working groups addressing UAVs. However, most current SDO committees focus on aspects such as vehicle categorisation, specifications and operational procedures, but one usually overlooked elementary topic is UAV localisation. Due to its importance and close relation to other technical subsystems, the lack of a resilient, scalable and efficient standardised UAV localisation and tracking system is one of the main obstructing barriers hindering the integration and interoperability of UAV swarms in smart cities and hence impeding the realisation of their vast application benefits. In this work, we focus on studying the fundamental technical requirements, specifications and functions of such UAV localisation and tracking system, and explore its relationship to and importance in 1) optimising path planning, flight scheduling and utilising shared airspace, 2) collision avoidance and conflict resolution in highly populated residential areas and 3) addressing privacy and data protection concerns that could arise from UAV monitoring and surveillance applications. Furthermore, for each of the three aspects, we analyse current SDOs efforts such as those put forth by EASA, EUROCAE WG73 and ISO TC20/SC16 on UAV systems, ISO JTC1/SC41 on IoT and related technologies and ISO JTC1/SC27, EU Directive 95/46 EC and GDPR on security, privacy and data protection, in order to identify and prioritise future research questions in relation to UAV localisation, aiming to make a contribution towards narrowing the gap between research and existing technical standards by encouraging multimode standardisation. This research was conducted in collaboration with ILNAS - the Institut Luxembourgeois de la Normalisation, de l'Accréditation, de la Sécurité et qualité des produits et services (ILNAS) under the authority of the Minister of Economy, Luxembourg.
Unmanned Aerial Vehicles (UAVs) have quickly become one of the promising Internet-of-Things (IoT) devices for smart cities. Thanks to their mobility, agility, and onboard sensors'customizability, UAVs have already demonstrated immense potential for numerous commercial applications. The UAVs expansion will come at the price of a dense, high-speed and dynamic traffic prone to UAVs going rogue or deployed with malicious intent. Counter UAV systems (C-UAS) are thus required to ensure their operations are safe. Existing C-UAS, which for the majority come from the military domain, lack scalability or induce collateral damages. This paper proposes a C-UAS able to intercept and escort intruders. It relies on an autonomous defense UAV swarm, capable of self-organizing their defense formation and to intercept the malicious UAV. This fully localized and GPS-free approach follows a modular design regarding the defense phases and it uses a newly developed balanced clustering to realize the intercept- and capture-formation. The resulting networked defense UAV swarm is resilient to communication losses. Finally, a prototype UAV simulator has been implemented. Through extensive simulations, we demonstrate the feasibility and performance of our approach.
International audience ; The development and usage of Unmanned Aerial Vehicles (UAVs) quickly increased in the last decades, mainly for military purposes. This technology is also now of high interest in non-military contexts like logistics, environmental studies and different areas of civil protection. While the technology for operating a single UAV is rather mature, additional efforts are still necessary for using UAVs in fleets (or swarms). The Aid to SItuation Management based on MUltimodal, MUltiUAVs, MUltilevel acquisition Techniques (ASIMUT) project which is supported by the European Defence Agency (EDA) aims at investigating and demonstrating dedicated surveillance services based on fleets of UAVs. The aim is to enhance the situation awareness of an operator and to decrease his workload by providing support for the detection of threats based on multi-sensor multi-source data fusion. The operator is also supported by the combination of information delivered by the heterogeneous swarms of UAVs and by additional information extracted from intelligence databases. As a result, a distributed surveillance system increasing detection, high-level data fusion capabilities and UAV autonomy is proposed.
