Agent-based simulations for coverage extensions in 5G networks and beyond

Device-to-device (D2D) communications is one of the key emerging technologies for the fifth generation (5G) networks and beyond. It enables direct communication between mobile users and thereby extends coverage for devices lacking direct access to the cellular infrastructure and hence enhances network capacity. D2D networks are complex, highly dynamic and will be strongly augmented by intelligence for decision making at both the edge and core of the network, which makes them particularly difficult to predict and analyze. Conventionally, D2D systems are evaluated, investigated and analyzed using analytical and probabilistic models (e.g., from stochastic geometry). However, applying classical simulation and analytical tools to such a complex system is often hard to track and inaccurate. In this paper, we present a modeling and simulation framework from the perspective of complex-systems science and exhibit an agent-based model for the simulation of D2D coverage extensions. We also present a theoretical study to benchmark our proposed approach for a basic scenario that is less complicated to model mathematically. Our simulation results show that we are indeed able to predict coverage extensions for multi-hop scenarios and quantify the effects of street-system characteristics and pedestrian mobility on the connection time of devices to the base station (BS). To our knowledge, this is the first study that applies agent-based simulations for coverage extensions in D2D

AVALIAÇÃO DA CAPTAÇÃO DE OLIGOELEMENTOS NO CARANGUEJO INVASIVO PORTUNUS SEGNIS HEPATOPANCREAS USANDO UMA ABORDAGEM BIOQUÍMICA MULTIVARIADA

In the current investigation, we evaluated the biological consequences of trace elements contamination in the two Tunisian gulfs (Gabes gulf and Tunis gulf) on the blue swimming crabs hepatopancreas (Portunus segnis). The concentrations of three trace elements (cadmium, copper, and lead) in the hepatopancreas P.segnis were evaluated. Additionally, acetylcholinesterase (AChE), metallothioneins (MTs), hydroxide peroxidase (H2O2) and advanced oxidation protein products (AOPP) levels, were chosen as measurements to evaluate the environmental effects on the two crabs’ populations from different gulfs. Macromolecular (lipids, proteins, and DNA) were also determined in P.segnis hepatopancreas. The results of trace elements bioaccumulation in soft P. segnis hepatopancreas showed a high pollution in the Gabes gulf as evidence by significant accumulation of cadmium, cooper, and lead. These findings were confirmed by significant increases of metal pollution index (MPI) and metallothioneins (MTs) levels in the hepatopancreas of P. segnis from Gabes gulf than these from Tunis gulf. Consequently, the trace elements accumulation in P.segnis from Gabes gulf conduct to the generation of lipid peroxidation processes as documented by the high levels of H2O2and LOOH. A significant decrease of AChE activity was recorded in crabs collected from Gabes gulf as compared to these from Tunis gulf. The present study revealed depletion of proteins and lipids contents, while DNA showed significant degradation on crab hepatopancreas collected from Gabes gulf comparing to Tunis gulf. These evidences must be taken in consideration when using P. segnis as an ecological indicator species in the biomonitoring programs.Na investigação atual, avaliamos as consequências biológicas da contaminação por oligoelementos nos dois golfos tunisinos (Golfo de Gabes e Golfo de Tunísia) sobre os caranguejos nados azuis hepatopâncreas (Portunus segnis). Foram avaliadas as concentrações de três oligoelementos (cádmio, cobre e chumbo) no hepatopâncreas P.segnis. Além disso, os níveis de acetilcolinesterase (AChE), metalotioninas (MTs), peroxidase hidróxida (H2O2) e produtos de proteína de oxidação avançada (AOPP) foram escolhidos como medidas para avaliar os efeitos ambientais nas populações dos dois caranguejos de diferentes golfos. Macromoleculares (lipídios, proteínas e DNA) também foram determinados em P.segnis hepatopâncreas. Os resultados da bioacumulação de elementos vestigiais em P. segnis hepatopancreas mole mostraram uma alta poluição no Golfo de Gabes como evidência pelo acúmulo significativo de cádmio, cobre e chumbo. Estes resultados foram confirmados por aumentos significativos do índice de poluição de metais (MPI) e dos níveis de metalotioninas (MTs) no hepatopâncreas de P. segnis do Golfo de Gabes do que estes do Golfo de Tunísia. Consequentemente, o acúmulo de elementos vestigiais em P.segnis do Golfo de Gabes conduz à geração de processos de peroxidação lipídica, como documentado pelos altos níveis de H2O2e LOOH. Uma diminuição significativa da atividade de AChE foi registrada em caranguejos coletados do Golfo de Gabes em comparação com estes do Golfo de Tunísia. O presente estudo revelou esgotamento de proteínas e conteúdo de lipídios, enquanto o DNA mostrou degradação significativa no hepatopâncreas de caranguejo coletado do Golfo de Gabes em comparação com o Golfo de Tunísia. Estas evidências devem ser levadas em consideração ao utilizar P. segnis como uma espécie indicadora ecológica nos programas de biomonitoramento

Allocation des ressources efficaces en énergie dans les environnements Cloud

L'informatique en nuage (Cloud Computing) a émergé comme un nouveau paradigme pour offrir des ressources informatiques à la demande et pour externaliser des infrastructures logicielles et matérielles. Le Cloud Computing est rapidement et fondamentalement en train de révolutionner la façon dont les services informatiques sont mis à disposition et gérés. Ces services peuvent être demandés à partir d'un ou plusieurs fournisseurs de Cloud d'où le besoin de la mise en réseau entre les composants des services informatiques distribués dans des emplacements géographiquement répartis. Les utilisateurs du Cloud veulent aussi déployer et instancier facilement leurs ressources entre les différentes plateformes hétérogènes de Cloud Computing. Les fournisseurs de Cloud assurent la mise à disposition des ressources de calcul sous forme des machines virtuelles à leurs utilisateurs. Par contre, ces clients veulent aussi la mise en réseau entre leurs ressources virtuelles. En plus, ils veulent non seulement contrôler et gérer leurs applications, mais aussi contrôler la connectivité réseau et déployer des fonctions et des services de réseaux complexes dans leurs infrastructures virtuelles dédiées. Les besoins des utilisateurs avaient évolué au-delà d'avoir une simple machine virtuelle à l'acquisition de ressources et de services virtuels complexes, flexibles, élastiques et intelligents. L'objectif de cette thèse est de permettre le placement et l'instanciation des ressources complexes dans des infrastructures de Cloud distribués tout en permettant aux utilisateurs le contrôle et la gestion de leurs ressources. En plus, notre objectif est d'assurer la convergence entre les services de cloud et de réseau. Pour atteindre ces objectifs, cette thèse propose des algorithmes de mapping d'infrastructures virtuelles dans les centres de données et dans le réseau tout en respectant les exigences des utilisateurs. Avec l'apparition du Cloud Computing, les réseaux traditionnels sont étendus et renforcés avec des réseaux logiciels reposant sur la virtualisation des ressources et des fonctions réseaux. En plus, le nouveau paradigme d'architecture réseau (SDN : Software Defined Networks) est particulièrement pertinent car il vise à offrir la programmation du réseau et à découpler, dans un équipement réseau, la partie plan de données de la partie plan de contrôle. Dans ce contexte, la première partie de la thèse propose des algorithmes optimaux (exacts) et heuristiques de placement pour trouver le meilleur mapping entre les demandes des utilisateurs et les infrastructures sous-jacentes, tout en respectant les exigences exprimées dans les demandes. Cela inclut des contraintes de localisation permettant de placer une partie des ressources virtuelles dans le même nœud physique. Ces contraintes assurent aussi le placement des ressources dans des nœuds distincts. Les algorithmes proposés assurent le placement simultané des nœuds et des liens virtuels sur l'infrastructure physique. Nous avons proposé aussi un algorithme heuristique afin d'accélérer le temps de résolution et de réduire la complexité du problème. L'approche proposée se base sur la technique de décomposition des graphes et la technique de couplage des graphes bipartis. Dans la troisième partie de la thèse, nous proposons un cadriciel open source (framework) permettant d'assurer la mise en réseau dynamique entre des ressources Cloud distribués et l'instanciation des fonctions réseau dans l'infrastructure virtuelle de l'utilisateur. Ce cadriciel permettra de déployer et d'activer les composants réseaux afin de mettre en place les demandes des utilisateurs. Cette solution se base sur un gestionnaire des ressources réseaux "Cloud Network Gateway Manager" et des passerelles logicielles permettant d'établir la connectivité dynamique et à la demande entre des ressources cloud et réseau [...]Cloud computing has rapidly emerged as a successful paradigm for providing IT infrastructure, resources and services on a pay-per-use basis over the past few years. As, the wider adoption of Cloud and virtualization technologies has led to the establishment of large scale data centers that consume excessive energy and have significant carbon footprints, energy efficiency is becoming increasingly important for data centers and Cloud. Today data centers energy consumption represents 3 percent of all global electricity production and is estimated to further rise in the future. This thesis presents new models and algorithms for energy efficient resource allocation in Cloud data centers. The first goal of this work is to propose, develop and evaluate optimization algorithms of resource allocation for traditional Infrastructutre as a Service (IaaS) architectures. The approach is Virtual Machine (VM) based and enables on-demand and dynamic resource scheduling while reducing power consumption of the data center. This initial objective is extended to deal with the new trends in Cloud services through a new model and optimization algorithms of energy efficient resource allocation for hybrid IaaS-PaaS Cloud providers. The solution is generic enough to support different type of virtualization technologies, enables both on-demand and advanced resource provisioning to deal with dynamic resource scheduling and fill the gap between IaaS and PaaS services and create a single continuum of services for Cloud users. Consequently, in the thesis, we first present a survey of the state of the art on energy efficient resource allocation in cloud environments. Next, we propose a bin packing based approach for energy efficient resource allocation for classical IaaS. We formulate the problem of energy efficient resource allocation as a bin-packing model and propose an exact energy aware algorithm based on integer linear program (ILP) for initial resource allocation. To deal with dynamic resource consolidation, an exact ILP algorithm for dynamic VM reallocation is also proposed. This algorithm is based on VM migration and aims at constantly optimizing energy efficiency at service departures. A heuristic method based on the best-fit algorithm has also been adapted to the problem. Finally, we present a graph-coloring based approach for energy efficient resource allocation in the hybrid IaaS-PaaS providers context. This approach relies on a new graph coloring based model that supports both VM and container virtualization and provides on-demand as well as advanced resource reservation. We propose and develop an exact Pre-coloring algorithm for initial/static resource allocation while maximizing energy efficiency. A heuristic Pre-coloring algorithm for initial resource allocation is also proposed to scale with problem size. To adapt reservations over time and improve further energy efficiency, we introduce two heuristic Re-coloring algorithms for dynamic resource reallocation. Our solutions are generic, robust and flexible and the experimental evaluation shows that both proposed approaches lead to significant energy savings while meeting the users' requirement

Allocation des ressources efficaces en énergie dans les environnements Cloud

Cloud computing has rapidly emerged as a successful paradigm for providing IT infrastructure, resources and services on a pay-per-use basis over the past few years. As, the wider adoption of Cloud and virtualization technologies has led to the establishment of large scale data centers that consume excessive energy and have significant carbon footprints, energy efficiency is becoming increasingly important for data centers and Cloud. Today data centers energy consumption represents 3 percent of all global electricity production and is estimated to further rise in the future. This thesis presents new models and algorithms for energy efficient resource allocation in Cloud data centers. The first goal of this work is to propose, develop and evaluate optimization algorithms of resource allocation for traditional Infrastructutre as a Service (IaaS) architectures. The approach is Virtual Machine (VM) based and enables on-demand and dynamic resource scheduling while reducing power consumption of the data center. This initial objective is extended to deal with the new trends in Cloud services through a new model and optimization algorithms of energy efficient resource allocation for hybrid IaaS-PaaS Cloud providers. The solution is generic enough to support different type of virtualization technologies, enables both on-demand and advanced resource provisioning to deal with dynamic resource scheduling and fill the gap between IaaS and PaaS services and create a single continuum of services for Cloud users. Consequently, in the thesis, we first present a survey of the state of the art on energy efficient resource allocation in cloud environments. Next, we propose a bin packing based approach for energy efficient resource allocation for classical IaaS. We formulate the problem of energy efficient resource allocation as a bin-packing model and propose an exact energy aware algorithm based on integer linear program (ILP) for initial resource allocation. To deal with dynamic resource consolidation, an exact ILP algorithm for dynamic VM reallocation is also proposed. This algorithm is based on VM migration and aims at constantly optimizing energy efficiency at service departures. A heuristic method based on the best-fit algorithm has also been adapted to the problem. Finally, we present a graph-coloring based approach for energy efficient resource allocation in the hybrid IaaS-PaaS providers context. This approach relies on a new graph coloring based model that supports both VM and container virtualization and provides on-demand as well as advanced resource reservation. We propose and develop an exact Pre-coloring algorithm for initial/static resource allocation while maximizing energy efficiency. A heuristic Pre-coloring algorithm for initial resource allocation is also proposed to scale with problem size. To adapt reservations over time and improve further energy efficiency, we introduce two heuristic Re-coloring algorithms for dynamic resource reallocation. Our solutions are generic, robust and flexible and the experimental evaluation shows that both proposed approaches lead to significant energy savings while meeting the users' requirementsL'informatique en nuage (Cloud Computing) a émergé comme un nouveau paradigme pour offrir des ressources informatiques à la demande et pour externaliser des infrastructures logicielles et matérielles. Le Cloud Computing est rapidement et fondamentalement en train de révolutionner la façon dont les services informatiques sont mis à disposition et gérés. Ces services peuvent être demandés à partir d'un ou plusieurs fournisseurs de Cloud d'où le besoin de la mise en réseau entre les composants des services informatiques distribués dans des emplacements géographiquement répartis. Les utilisateurs du Cloud veulent aussi déployer et instancier facilement leurs ressources entre les différentes plateformes hétérogènes de Cloud Computing. Les fournisseurs de Cloud assurent la mise à disposition des ressources de calcul sous forme des machines virtuelles à leurs utilisateurs. Par contre, ces clients veulent aussi la mise en réseau entre leurs ressources virtuelles. En plus, ils veulent non seulement contrôler et gérer leurs applications, mais aussi contrôler la connectivité réseau et déployer des fonctions et des services de réseaux complexes dans leurs infrastructures virtuelles dédiées. Les besoins des utilisateurs avaient évolué au-delà d'avoir une simple machine virtuelle à l'acquisition de ressources et de services virtuels complexes, flexibles, élastiques et intelligents. L'objectif de cette thèse est de permettre le placement et l'instanciation des ressources complexes dans des infrastructures de Cloud distribués tout en permettant aux utilisateurs le contrôle et la gestion de leurs ressources. En plus, notre objectif est d'assurer la convergence entre les services de cloud et de réseau. Pour atteindre ces objectifs, cette thèse propose des algorithmes de mapping d'infrastructures virtuelles dans les centres de données et dans le réseau tout en respectant les exigences des utilisateurs. Avec l'apparition du Cloud Computing, les réseaux traditionnels sont étendus et renforcés avec des réseaux logiciels reposant sur la virtualisation des ressources et des fonctions réseaux. En plus, le nouveau paradigme d'architecture réseau (SDN : Software Defined Networks) est particulièrement pertinent car il vise à offrir la programmation du réseau et à découpler, dans un équipement réseau, la partie plan de données de la partie plan de contrôle. Dans ce contexte, la première partie de la thèse propose des algorithmes optimaux (exacts) et heuristiques de placement pour trouver le meilleur mapping entre les demandes des utilisateurs et les infrastructures sous-jacentes, tout en respectant les exigences exprimées dans les demandes. Cela inclut des contraintes de localisation permettant de placer une partie des ressources virtuelles dans le même nœud physique. Ces contraintes assurent aussi le placement des ressources dans des nœuds distincts. Les algorithmes proposés assurent le placement simultané des nœuds et des liens virtuels sur l'infrastructure physique. Nous avons proposé aussi un algorithme heuristique afin d'accélérer le temps de résolution et de réduire la complexité du problème. L'approche proposée se base sur la technique de décomposition des graphes et la technique de couplage des graphes bipartis. Dans la troisième partie de la thèse, nous proposons un cadriciel open source (framework) permettant d'assurer la mise en réseau dynamique entre des ressources Cloud distribués et l'instanciation des fonctions réseau dans l'infrastructure virtuelle de l'utilisateur. Ce cadriciel permettra de déployer et d'activer les composants réseaux afin de mettre en place les demandes des utilisateurs. Cette solution se base sur un gestionnaire des ressources réseaux "Cloud Network Gateway Manager" et des passerelles logicielles permettant d'établir la connectivité dynamique et à la demande entre des ressources cloud et réseau [...

Exact and heuristic graph-coloring for energy efficient advance cloud resource reservation

International audienceThis paper presents a new graph-coloring model for advance resource reservation with minimum energy consumption in heterogeneous IaaS cloud data centers. We start with an exact integer linear programming (ILP) formulation which generalizes the graph coloring problem and follow with a fast Energy Efficient Graph Pre-coloring (EEGP) heuristic to address the scalability and to reduce convergence times. The results of performance evaluation and comparisons of EEGP with our exact algorithm and the Haizea advance reservation (AR) algorithm demonstrate the efficiency of EEGP for the energy efficient advance resource reservation problem. Our proposed EEGP heuristic is shown to perform very close to optimal, to scale well with problem size and to achieve convergence times close to the simple and fast AR algorithm that is however suboptima

A green VNFs placement and chaining algorithm

International audienceThis paper proposes an Integer Linear Program (ILP) to address Virtualized Network Function Forwarding Graph (VNF-FG) placement and chaining with Virtualized Network Functions (VNFs) shared across tenants to optimize resource usage and increase provider revenue. The proposed algorithm selects a limited number of candidate hosts from the infrastructure to reduce the complexity of the ILP and scale with problem size. Results from extensive simulations report performance improvements in terms of rejection rate, energy consumption and scalability. Limiting the number of candidates is an efficient heuristic to ensure scalabilit

Online and batch algorithms for VNFs placement and chaining

International audienceThis paper proposes an Integer Linear Program (ILP) to address the Virtualized Network Function Forwarding Graph (VNF-FG) placement and chaining problem when VNFs are shared across tenants to optimize resource usage and increase provider revenue. Since ILP based approaches do not scale well with problem size, the proposed algorithm (R-ILP for reduced exploration) selects a limited number of candidate hosts from the infrastructure to control complexity. Since the online R-ILP treats the requests sequentially, a batch strategy that operates on a set of requests is also proposed to improve performance. The online algorithm processes the VNF-FG requests on a sequential basis as they arrive while the batch mode treats several requests jointly over a batch window. This work focuses on energy consumption optimization as a general objective. The proposed solutions are shown to outperform competitor algorithms from the state of the art that rely also on VNFs sharing. Results from extensive simulations, based on realistic and large scale topologies, report the performance in terms of rejection of service requests, energy consumption, scalability and achieved revenues. The performance benefits of operating our R-ILP in batch mode are highlighted

Agent-based modeling and simulation for malware spreading in D2D networks

This paper presents a new multi-agent model for simulating malware propagation in device-to-device (D2D) 5G networks. This model allows to understand and analyze mobile malware-spreading dynamics in such highly dynamical networks. Additionally, we present a theoretical study to validate and benchmark our proposed approach for some basic scenarios that are less complicated to model mathematically and also to highlight the key parameters of the model. Our simulations identify critical thresholds for em no propagation and for em maximum malware propagation and make predictions on the malware-spread velocity as well as device-infection rates. To the best of our knowledge, this paper is the first study applying agent-based simulations for malware propagation in D2D