The placement of Virtual Network Functions (VNFs) plays a critical role in the provisioning of services across nextgeneration networks. However, the constantly changing conditions in network topologies, traffic demands and resource availability pose significant challenges to achieving efficient and reliable placement strategies. While Deep Reinforcement Learning (DRL) has been widely explored to address this problem, these approaches have limited generalization capabilities and require retraining when faced with unfamiliar scenarios, such as network topology changes, failure events or evolving service requests (e.g., VNF Forwarding Graphs or VNF-FGs). To address these limitations, we propose GPG-VNFE, a foundation Graph Neural Network (GNN) architecture that integrates graph contrastive learning with generative models as Learnable Priors (GraphCL-LP) within a Transformer-based DRL framework. GPG-VNFE first pretrains two domain-specific GNN encoders using GraphCL-LP, one for substrate network topologies and another for VNF-FG service graphs. Through extensive simulation on a medium-size network topology, we show that GPG-VNFE improves the acceptance ratio of incoming VNF requests by up to 57.45% compared with three baseline state-of-the-art placement approaches. We also show that our proposal lowers the power consumption at the physical layer by up to 11%, outperforming two of the baseline approaches.

Cardiovascular diseases (CVDs) are a leading cause of mortality worldwide, highlighting the critical need for accurate and continuous heart health monitoring. Electrocardiograms (ECG), considered as the golden standard for diagnosing and monitoring heart-related conditions, offer precise measurements but require direct skin contact, limiting their practicality for long-term and everyday use. On the other hand, existing RF sensing techniques that analyze signals reflected off the skin struggle to distinguish micro cardiac motions of the heart due to weak motion amplitude and respiration interference at the chest wall. To overcome these limitations, we introduce WiCG, a novel contact-less cardiac motion monitoring system that employs 2.4 GHz Wi-Fi signals to penetrate the chest and detect subtle cardiac movements. A mix-medium Wi-Fi Fresnel zone model is developed to explain the enhanced phase sensitivity of in-body Wi-Fi signals, which is crucial for accurately detecting cardiac motions. By strategically positioning antennas near the heart, WiCG captures ventricular motions effectively. A novel cardiac Doppler method is proposed to suppress phase noise and interference from static paths and extract the time interval between the systole and diastole of the ventricular. Extensive experiments demonstrate that the proposed system can robustly estimate the R-R and Q-T intervals of human cardiac cycles across 21 subjects and different environments with an average accuracy of 99.22% and 92.8%, achieving performance comparable to ECG.

International audience

Tools for automated deployment and management are often used in the cloud computing platforms. Since this technology is rapidly evolving, it is important to guarantee the reliability and the efficiency of these tools. The paper proposes a framework CMT-VSE for validation of configuration management tools and for providing tenant requests with timed recommendations for their safe execution. Such recommendations can be utilized to execute the chains of commands in an asynchronous mode. Experimental evaluation with Ansible-2.16 and SaltStack-3006 showcases the efficiency of CMT-VSE.

This paper addresses uncertainty in resource demands and heterogeneous requests with affinity and anti-affinity constraints on virtual nodes and links in traditional Virtual Network Embedding. This is realized using stochastic modeling and methods based on an initial Integer-Linear Programming (ILP) model formulation of the VNE problem. The ILP is extended to build a non-linear Chance-Constrained Programming (CCP) model to address uncertainty. The derived CCP model is then linearized for exploitation by standard solvers. Numerical experiments and comparisons with state-of-the-art methods illustrate the efficiency of our approaches. The results provide insight to cloud service providers on their resource investment to serve clients with affinity and anti-affinity requirements under uncertainty.

This paper sheds the light on road active safety measurements implemented in unmanned aerial vehicles assisted vehicular networks. Despite the great potential of deploying high computing drones, the drone battery life is the major concern, on one hand. On the other hand, road active safety is a critical real-time process that should be tackled in a tight time window in vehicular networks. To meet the mentioned concerns, we adopt federated machine learning on the local vehicles, sending local updates to drone servers. Moreover, a dynamic frequency adaptation framework is proposed to achieve the optimal trade-off between the road active safety performance and drone’s energy consumption. The thresholds for the local update frequency are calibrated according to road safety measurements (i.e., collision rate, risky and impolite driving time on the road) and drone energy consumption. Additionally, an accurate mathematical modeling based on M/G/1 multi-class was conducted in order to access the queuing time at the drone.

Most existing RF-based vital sign monitoring systems either assume that a human subject is stationary or discard measurements when motion is detected in order to output reliable respiration rates and heart rates. Such an assumption greatly limits the usability of these systems in practice. Even during sleep, one can undergo various body states including turns and involuntary twitches in light sleep, motionlessness during deep sleep, or abnormal limb movements due to sleep disorders such as restless legs syndrome. In this work, we develop SlpRoF, a low-cost contact-free system using a commercial-off-the-shelf UWB radar that achieves high temporal coverage and high accuracy in vital sign monitoring during sleep. By classifying body states into the motionless state, limb movement state, and torso movement state, and extracting vital signs during the first two states, it directly increases effective reporting periods over nights. By analyzing high-order harmonics and leveraging spatial diversity in captured signals from multiple on-body areas, it improves the accuracy of heart rate estimations and thus indirectly increases temporal coverage through reliable assessments. Experiment results show that SlpRoF is able to achieve an average median absolute error (MAE) of 0.44 beats per minute (bpm) in respiration rates, 1.55s in respiration intervals, and 0.9 bpm for heart rates, respectively.

Designing smart mechanisms to facilitate and accelerate service deployment and management is one of the most challenging aspects for network infrastructure providers. This is due to the massive amount of traffic that they are expected to support, the decentralized nature of the architectures, and the services they run to meet quality targets and avoid Service Level Agreement (SLA) violations. Therefore, Communications Service Providers (CSPs) are devoting much of their efforts on reducing energy consumption and reducing carbon foot-print of their network infrastructures. In future communication networks, traditional management mechanisms, and centralized legacy solutions show their limitations in ensuring revenue for the infrastructure providers, the service providers, and a good Quality of Experience (QoE) for the end-users. The deployment of these services requires, typically, an efficient allocation of Virtual Network Function Forwarding Graph (VNF-FG). In this context, we propose an intelligent energy efficient VNF-FG embedding approach based on multi-agent attention-based Deep Reinforcement Learning (DRL). Our contribution uses a semi-distributed DRL mechanism for VNF-FG placement. The proposed algorithm is shown to outperform previous state-of-the-art approaches in terms of acceptance rate, power consumption, and execution time.

International audience

This paper introduces a drone-assisted lane change platform based on a reinforcement learning agent, more specifically, DEAR (DEep Q-network with a dynAmic Reward). In order to achieve a satisfactory lane change performance, the reward function is designed from safety, comfort and efficiency perspectives. In particular, the weights of the three rewards are adjusted according to the surrounding traffic condition. On the other hand, the drones hovering over the highway provide global information (i.e. road vehicular density) to the ego vehicle. In addition, the drones also perform global control by 1) computing and sending a dynamic collision reward to the ego vehicle; 2) sending an Urgent Lane Change Request (ULCR) to the ego vehicle when a road risk ahead, or an emergency vehicle behind the ego vehicle is detected. The proposed lane change platform is tested with the authentic Next Generation Simulation (NGSIM) dataset. Simulation results prove that the platform is able to perform safe and efficient lane change on a road prone to risks and emergency vehicles.

This paper focuses on a dynamic embedding of client-constrained heterogeneous Virtual Network (VN) requests with multiple nodes and links affinity and anti-affinity requirements. For this Virtual Network Embedding (VNE) problem, we formulate an Integer-Linear Programming (ILP)-based model that achieves joint mapping of virtual nodes and links of each VN onto the dynamically updated Substrate Network (SN). This model not only meets the clients expressed isolation constraints but also includes VN request arrivals and departures to update SN information. Numerical experiments illustrate the efficiency of the proposed methods and their ability to find optimal solutions. Performance reports provide cloud service providers with insights into additional investments in nodes and links they should make to serve clients with anti-affinity requirements.

This paper proposes an Attention-based Multi-Agent Cooperation (AMAC) approach to reduce message exchange overhead in Multi-Agent Reinforcement Learning-based smart load balancing. AMAC shares only most relevant messages across agents to coordinate decision-making without degrading original performance. Experiments show that AMAC significantly lowers inter-agent communications overhead and learning complexity and outperforms multiple MARL benchmarks in Key Performance Indicators (KPIs) and Key Quality Indicators (KQIs).

This paper explores the use of multi-agent deep learning as well as learning to cooperate principles to meet strict service level agreements, in terms of throughput and end-to-end delay, for a set of classified network flows. We consider agents built on top of a weighted fair queuing algorithm that continuously set weights for three flow groups: gold, silver, and bronze. We rely on a novel graph-convolution based, multi-agent reinforcement learning approach known as DGN. As benchmarks, we propose centralized and distributed deep Q-network algorithms and evaluate their performances in different network, traffic, and routing scenarios, highlighting both the effectiveness of our proposals and the importance of agent cooperation. We show that our DGN-based approach meets stringent throughput and delay requirements across different scenarios, decreasing silver and bronze flow median waiting delays by more than 50 % and reducing the SLA violations of the latter by nearly 60 %, with respect to a classic priority queuing approach.

In this paper, we explore the use of an actor-critic architecture for Deep Reinforcement Learning (DRL) to improve load balancing beyond traditional algorithms. Some centralized Reinforcement Learning (RL) algorithms have targeted in the reward function expression the Quality of Experience (QoE) for video flows, but this requires access to clients, or the Maximum Link Utilization (MLU) for other types of flows. In our approach, we tune the actor-critic algorithm to only leverage on QoS parameters in order to load balance traffic in the network and maximize the QoE experienced by the users. This avoids having to collect observations and performance measurements from client applications, as it only focuses on network metrics that can be easily measured. We explore both centralized and distributed solutions to assess the feasibility of the proposed smart load balancing solutions. We compare them to ECMP, QoE-based reward methods, and RILNET that uses an underlying DDPG optimization approach. The proposed algorithms are shown to outperform previous approaches.

8 pages, as submitted to the 17th International Conference on Evaluation of Novel Approaches to Software Engineering

Network as a Service (NaaS) enables tenants and cloud users to connect their distributed services across multiple clouds without relying on their own networking services and resources. Cloud service providers (CSPs) offer networking services as bundles delivered on a pay-per-use basis. Although service bundling is a common practice in the cloud providing multiple components as a single service, forcing the packaging of network resources in a pure bundle can severely limit visibility and control over network services. We argue that current descriptions of NaaS services lack transparency making cloud users unable to efficiently discover and consume these services from different providers. To address this issue, we propose to describe network services based on their functionality and according to the OSI model. Our approach explicitly describes bundling between network services that promotes discovery and enables users to create their own network services bundles from different CSPs.

We present a novel formalism for describing the evolution of dynamic-link network parameters; it is based on the Cellular Automaton (CA) model. Such formalism is of wide-use for modeling natural (e.g., physical, chemical, etc.) processes. We propose a particular model and survey the related work, with respect to the use of CA to simulate various communication networks. We showcase the flexibility of the proposed approach to model different evolution patterns. These patterns can be used to emulate / simulate different network scenarios (states of the network parameters), and test novel implementations under distinct conditions. Additionally, we propose an algorithm for guaranteeing that the described patterns hold properties of interest, within a bounded time.

A smart Load Balancing (LB) policy based on Graph Convolutional Multi-Agent Reinforcement Learning (GC-MARL) is proposed to improve load balancing in networks beyond what can be realized by traditional methods and state of the art machine learning based approaches. GC-MARL models the network as a graph and derives through a graph convolutional method the policy that splits traffic flows across end-to-end candidate paths while meeting application QoE requirements. The proposed method uses the throughput and the delay, observed at the network level, as the key performance indicators embedded in the reward expression as opposed to observing QoE at the application level. The results confirm the effectiveness of the proposed solution in terms of KPIs (such as throughput, delay, jitter, packet loss), and KQIs (such as QoE, average video bitrate, stalling, etc…).

This paper provides a Lane Change Assistance (LCA) platform that communicates with Unmanned Aerial Vehi-cles (UAV). The proposed platform is based on a reinforcement learning technique, where a Deep Q-Network (DQN) is trained to make lane change decisions. The reward function of the DQN agent considers safety, comfort and efficiency perspectives. Specifically, the safety reward, based on the road vehicular density, is adapted dynamically by the drone during the training phase. Performance analysis proves that the proposed platform improves the total travel time while reducing the collision rate and responding to urgent lane changes in a timely manner.

Software Defined Networking (SDN) is a novel network management technology, which currently attracts a lot of attention due to the provided capabilities. Recently, different works have been devoted to testing / verifying the (correct) configurations of SDN data planes. In general, SDN forwarding devices (e.g., switches) route (steer) traffic according to the configured flow rules; the latter identifies the set of virtual paths implemented in the data plane. In this paper, we propose a novel preventive approach for verifying that no misconfigurations (e.g., infinite loops), can occur given the requested set of paths. We discuss why such verification is essential, namely, how, when synthesizing a set of data paths, other not requested and undesired data paths (including loops) may be unintentionally configured. Furthermore, we show that for some cases the requested set of paths cannot be implemented without adding such undesired behavior, i.e., only a superset of the requested set can be implemented. Correspondingly, we present a verification technique for detecting such issues of potential misconfigurations and estimate the complexity of the proposed method; its polynomial complexity highlights the applicability of the obtained results. Finally, we propose a technique for debugging and repairing a set of paths in such a way that the corrected set does not induce undesired paths into the data plane, if the latter is possible.

Optimal store placement aims to identify the optimal location for a new brick-and-mortar store that can maximize its sale by analyzing and mining users’ preferences from large-scale urban data. In recent years, the expansion of chain enterprises in new cities brings some challenges because of two aspects: (1) data scarcity in new cities, so most existing models tend to not work (i.e., overfitting), because the superior performance of these works is conditioned on large-scale training samples; (2) data distribution discrepancy among different cities, so knowledge learned from other cities cannot be utilized directly in new cities. In this article, we propose a task-adaptative model-agnostic meta-learning framework, namely, MetaStore, to tackle these two challenges and improve the prediction performance in new cities with insufficient data for optimal store placement, by transferring prior knowledge learned from multiple data-rich cities. Specifically, we develop a task-adaptative meta-learning algorithm to learn city-specific prior initializations from multiple cities, which is capable of handling the multimodal data distribution and accelerating the adaptation in new cities compared to other methods. In addition, we design an effective learning strategy for MetaStore to promote faster convergence and optimization by sampling high-quality data for each training batch in view of noisy data in practical applications. The extensive experimental results demonstrate that our proposed method leads to state-of-the-art performance compared with various baselines.

Store site recommendation aims to predict the value of the store at candidate locations and then recommend the optimal location to the company for placing a new brick-and-mortar store. Most existing studies focus on learning machine learning or deep learning models based on large-scale training data of existing chain stores in the same city. However, the expansion of chain enterprises in new cities suffers from data scarcity issues, and these models do not work in the new city where no chain store has been placed (i.e., cold-start problem). In this article, we propose a unified approach for cold-start store site recommendation, Weighted Adversarial Network with Transferability weighting scheme (WANT), to transfer knowledge learned from a data-rich source city to a target city with no labeled data. In particular, to promote positive transfer, we develop a discriminator to diminish distribution discrepancy between source city and target city with different data distributions, which plays the minimax game with the feature extractor to learn transferable representations across cities by adversarial learning. In addition, to further reduce the risk of negative transfer, we design a transferability weighting scheme to quantify the transferability of examples in source city and reweight the contribution of relevant source examples to transfer useful knowledge. We validate WANT using a real-world dataset, and experimental results demonstrate the effectiveness of our proposed model over several state-of-the-art baseline models.

In this paper, a novel model-based approach to verify dynamic networks is proposed. The network topology and dynamic link parameters are described as a many sorted first order logic formula, that is verified by an SMT-solver (z3 in our case) with respect to a set of properties. The formula is also used for run-time network verification when a given static network instance is implemented. Preliminary experiments showcase the expressiveness and current limitations of the proposed approach.

This paper addresses Virtualized Network Function Forwarding Graph (VNF-FG) embedding with the objective of realizing long term reward compared to placement algorithms that aim at instantaneous optimal placement. The long term reward is obtained using Reinforcement Learning (RL), following a Markov Decision Process (MDP) model, enhanced through the injection of expert knowledge in the learning process. A comparison with an Integer Linear Programming (ILP) approach, a reduced candidate set (R-ILP), and an algorithm that treats the requests in batch reveals the potential improvements using the RL approach. The instantaneous and short term reward solutions are efficient only in finding instant solutions as they make decisions only on current infrastructure status for a given request at a time or eventually a batch of requests. They are efficient only for present conditions without anticipating future requests. RL possesses instead the learning and anticipation capabilities lacking in instantaneous and snapshot optimizations. A Reinforcement Learning based approach, called EQL (Enhanced Q-Learning), aiming at balancing the load on hosting infrastructures is proposed to achieve the desired longer term reward. EQL employs RL to learn the network and control it based on the usage patterns of the physical resources. Results from extensive simulations, based on realistic and large scale topologies, report the superior performance of EQL in terms of acceptance rate, quality, scalability and achieved gains.

This paper proposes a set of algorithms to extend initially deployed Virtualized Network Functions Forwarding Graphs (VNF-FG or Service Function Chains - SFCs) previously requested and acquired by tenants. The latter are likely to request extension of their already acquired, deployed, and running dedicated slices to embed additional networking functions to handle for instance new application flows requiring additional classification, authentication, authorization, processing, traffic steering, etc. The tenants (or end-user service providers) are also likely to introduce gradually their services as needs arise and as their consumer base and profiles evolve. Increasing traffic load often will also require extension of the service graphs and chaining such as introducing additional load balancers and servers, new security functions, and simply new VNFs related to new tenant services. The related service graph extension problem is addressed first through an ILP algorithm that serves as a reference for performance comparisons with a set of proposed heuristic algorithms. A heuristic directly inspired by and based on the ILP and the proposed alternative approaches are shown to scale much better than the ILP while providing good solutions. The solutions include an adaptation of an eigendecomposition approach that provides very good performance for highly connected graphs. The problem is also cast into the well know Steiner Tree search problem whose complexity and performance are formally characterized in the literature. The Steiner based algorithm is shown to provide the best overall tradeoff (in rejection rate, complexity, and quality) with performance closest to optimal.

An ensemble learning approach for classification in intrusion detection is proposed. Its application to the KDD Cup 99 and NSL-KDD datasets consistently increases the classification accuracy compared to previous techniques. The cascade-structured meta-specialists architecture is based on a three-step optimization method: data augmentation, hyperparameters optimization and ensemble learning. Classifiers are first created with a strong specialization in each specific class. These specialists are then combined to form meta-specialists, more accurate than the best classifiers that compose them. Finally, meta-specialists are arranged in a cascading architecture where each classifier is successively given the opportunity to recognize its own class. This method is particularly useful for datasets where training and test sets differ greatly, as in this case. The cascade-structured meta-specialists approach achieved a very high classification accuracy (94.44% on KDD Cup 99 test set and 88.39% on NSL-KDD test set) with a low false positive rate (0.33% and 1.94% respectively).

The paper presents a work in progress on the Quality of Experience (QoE) assessment in the context of Software Defined Networking (SDN). Preliminary experimental results clearly demonstrate the necessity of considering QoE parameters, which are not taken into account in traditional networks. Dynamic network (re-) configuration requires monitoring and measuring attributes not only at the network level but also at the control and application planes. We discuss a list of such parameters and propose a monitoring algorithm to consider distributed and asynchronous points of observation for the QoE assessment of SDN network services. Further, for the cases when a low QoE level is detected (or predicted using machine learning), we propose an algorithm for effective diagnosis of the QoE degradation, based on an iterative deduction of a QoE attribute contributing the most to a low QoE value.

In Software Defined Networking (SDN), different applications may configure different coexisting forwarding rules, the resulting (running) data-paths a specific network flow traverses may not be the intended ones. Furthermore, the SDN components may be defective or compromised. In order to provide reliable communication within the SDN driven data-planes, assuring that the running data-paths are the requested (and expected) ones is necessary. In this paper, we propose an approach that relies on distributed traffic generation and monitoring to identify the running data-paths in a given SDN driven data-plane. We show that differently from the existing approaches, under certain assumptions, there exist necessary and sufficient conditions for formally guaranteeing that all running datapaths are discovered using our approach. A data-path discovery toolkit has been implemented using the proposed approach. We describe the corresponding set of tools, and showcase the obtained experimental results that reveal inconsistencies in well-known SDN applications. Additionally, we show the scalability of our approach.

We propose a meta-heuristic based on the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to address the NP-Hard service function chain placement problem. This work considers the minimization of the mapping cost and of the physical links utilization for virtualized network functions (VNF) chaining. The proposed NSGA-II based algorithm finds a Pareto front to select solutions that meet the multiple objectives and performance tradeoffs of providers. Simulation results and comparison with a multi-stage algorithm and a matrix based heuristic from the literature, highlight the efficiency and usefulness of the proposed NSGA-II-based approach.

International audience

This paper proposes Steiner-based algorithms to extend already deployed tenant slices or Virtualized Network Functions Forwarding Graphs (or Service Function Chains) as demand grows or additional services are appended to prior service functions and chains. The tenant slices are hosted by Network Function Virtualization Infrastructure (NVFI) providers that can make use of the proposed algorithms to extend tenant slices on demand for growing traffic loads and service extensions including protection and security services(such as extending a slice with a dedicated security slice). The paper proposes a Steiner-based ILP as an exact solution for small graphs and Steiner based approximation algorithms to improve scalability for larger problems.

This work presents algorithms to extend tenant Virtualized Network Functions Forwarding Graphs (VNF-FG) to embed new networking functions and introduce gradually additional services into the already deployed tenant dedicated slices. The related service graph extension problem is addressed through an Integer Linear Programming (ILP) algorithm that serves as a reference for performance comparisons with a proposed heuristic algorithm. An eigendecomposition approach is proposed, evaluated and compared.

This 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 scalability.

An energy model for Internet of Things devices is proposed. The model is based on security mechanisms that are constantly used but never accounted for to allow for longer term conclusions on the energetic cost of security. It is suitable for networking scenarios by slicing the energy spent by a device in individual connections with other network elements. It presents a three phase view of a connection with external network elements: establishment, active communication phase and termination. It maps security mechanisms and offers the ability to quantify the costs of security algorithms in all the phases. It enhances the traditional models by introducing security as a basis and being suitable for networking considerations. Simulation results are presented that show better visibility of a device's energy consumption while networking in cooperation scenarios and due to security options, using Bluetooth's Low Energy most secure mode of operation.

This paper deals with virtualised Network Function Placement and chaining over cloud and NFV environments. We propose a polynomial-time algorithm for joint VNF chain placement that is based on a recursive optimization approach. The key idea is to benefit from the robustness of the recursion paradigm that was used to solve many optimization problems. We demonstrate the potential of our solution by comparing it to previous work. We conduct performance evaluation first through extensive simulations and then using a real cloud testbed. The obtained results show that our proposed algorithm achieves better performance in terms of resource usage, execution time, acceptance rate and revenue of the cloud provider.

This paper addresses energy efficient VNF placement and chaining over NFV enabled infrastructures. VNF placement and chaining are formulated as a decision tree search to overcome this NP-Hard problem complexity. The proposed approach is an extension of the Monte Carlo Tree Search(MCTS) method to achieve energy savings using physical resourceconsolidation and sharing VNFs between multiple tenants. A realcloud testbed and extensive simulations are used to assessperformance and ability to scale with problem size. Evaluationresults show significant reduction in energy consumption of theproposed placement solution compared to related work. The polynomialcomplexity of our proposal is highlighted by the simulation results.

The virtualized network functions placement and chaining problem is formulated as a decision tree to reduce significantly the complexity of service function chaining (SFC) in clouds. Each node in the tree corresponds to a virtual resource embedding and each tree branch to the mapping of a client request in some physical candidate. This transforms the placement problem to a decision tree search. We devise a new algorithm based on the Monte Carlo Tree Search (MCTS) to incrementally build and search within the decision tree. Thanks to the proposed SFC-MTCS strategy, an optimized solution is computed in a reasonable time. Extensive simulations assess the performance and show that SFC-MCTS outperforms state of the art strategies in terms of: i) acceptance rate, ii) providers revenue and iii) execution time.

Network Functions Virtualization (NFV) has been revolutionizing and improving the way networking services are deployed but also requires the dynamic scaling of resources during the lifecycle management of Virtual Network Functions (VNFs) with increasing service demand. We propose an Integer Linear Programming (ILP) approach and a Greedy heuristic to address this NP-Hard scaling problem. Both horizontal (scale out/in) and vertical (scale up/down) scaling are considered and the scalability of the ILP is addressed by reducing the number of candidate hosts in the search space. Extensive simulations evaluate the performance of the algorithms in successful scalings and VNF migrations and highlight the importance of using the algorithms to tidy up the infrastructure to accept more users. The results show that the ILP can outperform the Greedy heuristic if appropriate actions are selected.

International audience

This demo presents a SFC orchestrator that provides intelligent, dynamic and automated VNFs and network deployment over heterogeneous NFV and SDN enabled Cloud environments. The proposed architecture is based on the ETSI NFV reference architecture and TOSCA standards. In this demo, reliable VNF placement and chaining is ensured using the algorithm proposed in [1] “A Link Failure Recovery Algorithm For Virtual Network Function Chaining”.

This paper addresses Virtual Network Functions (VNFs) placement and chaining in the presence of physical link failures. A decision tree approach to the NP-Hard VNF placement and chaining problem is used to minimize the penalties induced by service interruptions due to link outages. Formulating the problem as decision tree reduces the complexity significantly and leads to a new reliable algorithm, named R-SFC-MCTS, that builds incrementally the decision tree to efficiently search for good placement and chaining solutions. Execution time is improved thanks to the Monte-Carlo Tree Search strategy. The proposed link failure recovery algorithm selects and assigns reliable paths to prevent and avoid the negative effects of link failures and reactively re-maps impacted virtual links in safer physical paths once an outage occurs in the infrastructure. The performance of R-SFC-MCTS is compared via extensive simulations with a baseline and a reactive solution in terms of: i) acceptance rate, ii) induced penalties iii) provider revenue loss and iv) the final provider's profit.

In this paper, we propose a new protocol for the key establishment without any prior trust relationship between an UE and a Machine Type Communication (MTC) device, building security from 5G towards the IoT. We use the protocol with ProSe to increase 5G coverage and propose a new secure operating mode that has the potential to save energy and bandwidth by securing direct sessions between UEs and MTC devices.

This paper assesses the benefits of cloud federation for cloud providers. Outsourcing and insourcing are explored as means to maximize the revenues of the providers involved in the federation. An exact method using a linear integer program is proposed to optimize the partitioning of the incoming workload across the federation members. A pricing model is suggested to enable providers to set their offers dynamically and achieve highest revenues. The conditions leading to highest gains are identified and the benefits of cloud federation are quantified.

This paper addresses the problem of cloud and networking resources mapping in distributed and hybrid cloud environments. In this context private and public resources are acquired and combined to set up tenant dedicated virtual infrastructures to fulfil distributed applications requirements. An exact algorithm is proposed to map jointly nodes and links of the requested virtual infrastructure graph to the physical graph from multiple providers (data centers and network providers). We view the problem as a virtual network mapping and use integer linear programming to find optimal solutions. To address complexity and scalability for large virtual and physical networks of thousands of nodes, an efficient heuristic algorithm, relying on topology patterns and bipartite matching, is used to provide close to optimal solutions and reduce mapping delays by three to four orders of magnitude.

This paper presents a model for Virtual Network Function (VNF) placement and chaining across Cloud environments. We propose a new analytical approach for joint VNFs placement and traffic steering for complex service chains and different VNF types. A custom greedy algorithm is also proposed to compare with our solution. Performance evaluation results show that our approach is fast and stable and has a execution time that essentially depends only on the NFV infrastructure size.

This paper presents an eigendecomposition approach for virtual resource embedding in cloud infrastructures. Instead of relying on combinatorial or iterative approaches, the method uses structural description of resource graphs to achieve near optimal joint node and link mapping. The solution extends and generalizes prior work on eigendecomposition of the adjacency matrices for undirected graphs to handle weighted graph requests and substrate graphs of different topologies and sizes. The method is compared to an optimal combinatorial solution and a heuristic using topology patterns and a bipartite graph matching that embed simultaneously nodes and links in cloud resources. A matching close to optimal can be found more efficiently by the proposed method as reported in the simulation experiments used for performance evaluation and comparisons. The proposed algorithm exhibits better scalability properties and naturally achieves consolidation that can be tuned according to desired performance tradeoffs.

This paper addresses dynamic bandwidth allocation for virtual network (VN) resources to respond to increasing or decreasing applications requirements in cloud environments. A distributed and local-view framework, composed of a controller and three algorithms running in substrate nodes, is proposed to deal with all types of bandwidth demand fluctuations in embedded virtual networks. The framework is based on the Self-Stabilization concept to drive the system back to a “stable state” when new bandwidth demands drift the system away into an “unstable state”. Performance evaluation results demonstrate the effectiveness of our proposal in handling bandwidth demand fluctuations in convergence speed and cost.

This paper presents an underlying framework to support and accelerate the production of applications and services in the context of programmable networks (SDN and NFV, clouds). The proposed framework addresses moreover the 5G KPI of “reducing the average service creation time from 90 hours to 90 minutes” as declared by 5G-PPP association in the early of 2015 among other KPIs. The proposed framework relies on SDN, NFV and Cloud principles and technologies and proposes extensions towards the end to end abstraction that is required for automation of service production. A Service Manager Architecture fulfilling the agility, acceleration and automation requirements is presented along with its relationships and interfaces with the applications and network levels. An application requiring network services, expressed in a network service descriptor, is used to illustrate the architecture usage and benefits and highlights the remaining future research needs and trails.

We address the online virtual network embedding problem within the Cloud's backbone to optimally map the virtual routers and links in the substrate network in order to maximize the Cloud's provider revenue. Since the problem is NP-hard, we propose a novel approach, named VNE-GH, to significantly reduce the problem size using the Gomory-Hu transformation without losing useful information on the virtual network embedding problem. Starting from the Gomory-Hu compact tree structure, we formulate the virtual network embedding as an Integer Linear Program and resolve the reduced size problem using the branch- and-cut algorithm. Results obtained via extensive simulations show that VNE-GH outperforms the most prominent related work strategies in terms of i) acceptance rate of virtual network requests and ii) Cloud provider's revenue.

Network function virtualization (NFV) decouples software implementations of network functions from their hosts (or hardware). NFV exposes a new set of entities, the virtualized network functions (VNFs). The VNFs can be chained with other VNFs and physical network functions to realize network services. This flexibility introduced by NFV allows service providers to respond in an agile manner to variable service demands and changing business goals. In this context, the efficient establishment of service chains and their placement becomes essential to reduce capital and operational expenses and gain in service agility. This paper addresses the placement aspect of these service chains by finding the best locations and hosts for the VNFs and to steer traffic across these functions while respecting user requirements and maximizing provider revenue. We propose a novel eigendecomposition-based approach for the placement of virtual and physical network function chains in networks and cloud environments. A heuristic based on a custom greedy algorithm is also presented to compare performance and assess the capability of the eigendecomposition approach. The performance of both algorithms is compared to a multi-stage-based method from the state of the art that also addresses the chaining of network services. Performance evaluation results show that our matrix-based method, eigendecomposition of adjacency matrices, has reduced complexity and convergence times that essentially depend only on the physical graph sizes. Our proposal also outperforms the related work in provider's revenue and acceptance rate.

In this paper, we consider last level cache (LLC) sharing problems in large-scale cloud networks with a fair payoff function. We formulate the problem as a strategic decision-making problem (i.e., a game). We examine the resource-sharing game with finite and infinite number of players. Exploiting the aggregate structure of the payoff functions, we show that the resource-sharing game has a Nash equilibrium in a wide range of return index. We show that the Nash equilibrium is not an evolutionarily stable strategy in the finite regime. Then, we introduce a myopic mean-field response where each player implements a mean-field-taking strategy. We show that such a mean-field-taking strategy is an evolutionarily stable strategy in both finite and infinite regime. We provide closed-form expression of the optimal pricing that gives an efficient resource-sharing policy. As the number of active players grows without bound, we show that the equilibrium strategy converges to a mean-field equilibrium, and the optimal prices for resources converge to the optimal price of the mean-field game. Then, we address the demand satisfaction problem for which a necessary and sufficient condition for satisfactory solutions is provided. In addition, a very fast mean-field learning algorithm is provided.

An algorithm to adapt dynamically virtual networks to additional resource requirements is proposed and evaluated. The optimization is achieved while balancing load and avoiding fragmentation in the infrastructure (often referred as substrate or physical network). The algorithm focuses on virtual nodes requiring more resources by extending their allocations and maintaining their connectivity (even if the node is migrated) to other resources while tidying up (or consolidating) the infrastructure. The algorithm outperforms existing approaches.

This paper addresses profit optimization through insourcing and outsourcing virtual resources (seen as VMs) for cloud infrastructure providers. A linear program is proposed to assist IaaS providers, involved in a cloud federation, in adjusting their hosting and cooperation decisions in response to their workloads and available resources. The proposed exact formulation takes into account the prices and quotas proposed by the federation members and the costs of resources and their networking to maximize providers revenues. The algorithm performance evaluation and the identified benefits show the conditions for profitable cloud federations and the efficiency of the proposed model in improving profit.

Virtual networks (VNs) are considered today as a critical IT resource that connects distributed and virtualized Cloud services and components like servers, applications and storage. Network virtualization allows traffic isolation between different tenants, security, efficient migration of virtual machines and reduced cloud service management complexity. One of the goals of this work is to develop a multi-objective VN embedding algorithm for optimal placement taking into account constraints such as power consumption, availability and load balancing. Adaptive provisioning is also required to maintain VNs in cloud environments and deal with user mobility, resource re-optimization and to respond to anomalies, faults and evolving demands. An exact adaptive fault-tolerant embedding algorithm to cope with node and link failures in cloud networks is also proposed. The embedding problem is formulated and solved as a mixed integer program. Performance of the proposed algorithms is reported.

This 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 suboptimal.

This paper addresses the dynamic adaptation of already embedded virtual network (VN) resources to respond to increasing network services demands and load on network nodes. The proposed algorithm focuses on the virtual nodes, of the embedded VN, requiring more resources. The adaptation scheme goes beyond the reallocation of the virtual nodes by considering their topological neighborhood. The proposed algorithm outperforms existing approaches in reallocation cost and in execution time (or convergence time) for larger graphs.

With the recent popularity of social network services, a significant volume of heterogeneous social media data is generated by users, in the form of texts, photos, videos and collections of points of interest, etc. Such social media data provides users with rich resources for exploring content, such as looking for an interesting video or a favorite point of interest. However, the rapid growth of social media causes difficulties for users to efficiently retrieve their desired media items. Fortunately, users' digital footprints on social networks such as comments massively reflect individual's fine-grained preference on media items, that is, preference on different aspects of the media content, which can then be used for personalized social media search. In this article, we propose SESAME, a fine-grained preference-aware social media search framework leveraging user digital footprints on social networks. First, we collect users' direct feedback on media content from their social networks. Second, we extract users' sentiment about the media content and the associated keywords from their comments to characterize their fine-grained preference. Third, we propose a parallel multituple based ranking tensor factorization algorithm to perform the personalized media item ranking by incorporating two unique features, viz., integrating an enhanced bootstrap sampling method by considering user activeness and adopting stochastic gradient descent parallelization techniques. We experimentally evaluate the SESAME framework using two datasets collected from Foursquare and YouTube, respectively. The results show that SESAME can subtly capture user preference on social media items and consistently outperform baseline approaches by achieving better personalized ranking quality.

Sensor Networks (SN) have been developed for several domains which makes each person or organization has heterogeneous sensors. When considering that the data generated by sensor nodes should be permanently stored and permanently accessible from all over the world by scientists or doctors who need to be notified when some events are triggered, the data sharing, publication and notification become a crucial challenge. This paper present a convergence of social networks, cloud computing and sensor networks to resolve the aforementioned requirements while ensuring privacy and security of users.

This paper presents two exact algorithms for energy efficient scheduling of virtual machines (VMs) in cloud data centers. Modeling of energy aware allocation and consolidation to minimize overall energy consumption leads us to the combination of an optimal allocation algorithm with a consolidation algorithm relying on migration of VMs at service departures. The optimal allocation algorithm is solved as a bin packing problem with a minimum power consumption objective. It is compared with an energy aware best fit algorithm. The exact migration algorithm results from a linear and integer formulation of VM migration to adapt placement when resources are released. The proposed migration is general and goes beyond the current state of the art by minimizing both the number of migrations needed for consolidation and energy consumption in a single algorithm with a set of valid inequalities and conditions. Experimental results show the benefits of combining the allocation and migration algorithms and demonstrate their ability to achieve significant energy savings while maintaining feasible convergence times when compared with the best fit heuristic.

This paper presents a Cloud Networking Gateway (CNG) Manager, to enable networking of distributed cloud resources by authorized customers and to provide network control and configuration capabilities. The CNG Manager interconnects virtual machines, acquired from distributed heterogeneous resources and multiple providers, using generic gateways according to user connectivity requests. The CNG Manager is integrated with a "Cloud Broker" architecture for evaluation in the multiple providers context. The proposed framework extends and supports the current state of the art in cloud and software defined networks technologies with compatible connectivity control and management capabilities.

This work presents an open cloud interface extension to merge cloud computing and networks into one modeling and representation framework to extend clouds beyond data centers and enable cloud networking to set up distributed cloud infrastructures. The proposed extension to the de facto standard interface in cloud computing (OCCI) is named Open Cloud Networking Interface (OCNI). OCNI is presented and evaluated in terms of its ability to integrate smoothly with existing cloud computing interfaces and to provide all the required capabilities to express connectivity requirements and ease connection establishment between cloud computing, network and storage nodes at the virtual and physical level irrespective of underlying transport technologies.

Competitive Mobile Network Operators (MNOs) are typically long-known for their shrewdness to conceal their underlying network topology information. Having said this, in this article, we propose a quasi-distributed topology information sharing framework for network operators based on logically centralized controllers. Through our approach, we present a topology information sharing scheme in which two or more MNOs can cooperatively and more importantly, discreetly reveal their topology information for the sake of utilizing the unused available resources of each other, at times of network failure situations. Our approach has been formulated and developed based on a novel key metric to ‘tune’ the amount of information sharing. Based on extensive simulations, we then investigate the impacts of network topology information sharing on the network capacity. The overall feasibility is illustrated through significant numerical results.

In this article we illustrate that, management of network resources can yield notably different performances leading to different restoration behaviors -- for different network topologies. In consideration towards this, we first briefly summarize our earlier work on a novel fault restoration mechanism. The proposed mechanism addresses the problem of finding the minimum bandwidth cooperative route in the backhaul of mobile network operators that is shared among multiple operators. We then present our recent results on the extended performance evaluations of the same, for four different synthetic network topologies carried out using extensive simulations. Through these evaluations, we conclude on the best topology that mobile network operators should construct in order for them to maximally benefit by the proposed cooperative routing scheme to yield optimum performance for their capital expenditure.

A minimum cost maximum flow algorithm is proposed for resources(e.g. virtual machines) placement in clouds confronted to dynamic workloads and flows variations. The algorithm is compared to an exact method generalizing the classical Bin-Packing formulation using a linear integer program. A directed graph is used to model the allocation problem for cloud resources organized in a finite number of resource types; a common practice in cloud services. Providers can use the minimum cost maximum flow algorithm to opportunistically select the most appropriate physical resources to serve applications or to ensure elastic platform provisioning. The modified Bin-Packing algorithm is used to benchmark the minimum cost maximum flow solution. The latter combined with a prediction mechanism to handle dynamic variations achieves near optimal performance.

This paper presents the architecture design and the performance evaluation of a composite-based back end and visualization systems. The first one is used to host composites representing sensor nodes, store, index and manage water quality measurements collected by wireless sensor networks (WSNs) deployed in rivers, lakes and coastal regions. Embedded communication systems in each sensor node enable ad hoc network operations to relay the measurements to its composite in charge of managing, processing and providing data to end users and large communities through a standard web RESTful API. The visualization system is in charge of providing web interfaces to scientifics and users to monitor and configure the wireless sensor network (WSN) and visualize measurements in a real-time manner. The focus is on both the back end and the visualization systems architecture description and on the evaluation of their performance.

Through several studies, it has been highlighted that mobility patterns in mobile networks are driven by human behaviors. This effect has been particularly observed in intermittently connected networks like DTN (Delay Tolerant Networks). Given that common social intentions generate similar human behavior, it is relevant to exploit this knowledge in the network protocols design, e.g. to identify the closeness degree between two nodes. In this paper, we propose a temporal link prediction technique for DTN which quantifies the behavior similarity between each pair of nodes and makes use of it to predict future links. We attest that the tensor-based technique is effective for temporal link prediction applied to the intermittently connected networks. The validity of this method is proved when the prediction is made in a distributed way (i.e. with local information) and its performance is compared to well-known link prediction metrics proposed in the literature.

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In this paper, we reconsider the resource allocation and routing problem in multi-hop Multiple-Input Multiple-Output (MIMO) Wavelet Packet Modulation (WPM) Cognitive Radio (CR) ad-hoc networks. We comply with four principles to design our resource allocation scheme with the goal to maximize the capacity of entire network. First, Primary Users (PUs) have higher priority to access to the subcarrier in private bands, while PUs and Secondary Users (SUs) have the equal opportunity in public bands. Second, the MIMO channel of each link on each subcarrier in each time slot is decomposed by Block Diagonal Geometric Mean Decomposition (BD-GMD) into multiple sub-channels with identical gains. In addition, the sub-channel gain of one link is determined by all other links sharing the same source node on the same subcarrier in the same time slot. Third, PUs have the privilege of higher signal to interference and noise ratio (SINR) threshold for the successful decoding in private bands, but PUs and SUs have the same SINR threshold in public bands. Fourth, the interference caused by one link to all other links having the same destination node should be avoided, moreover, the power of all other links is not assumed to be known but also need to be allocated at the same time. Based on our proposed resource allocation scheme, we investigate a multicast routing strategy that aims to maximize network throughput. Numerical results demonstrate that our proposed resource allocation and routing scheme can noticeably improve network throughput.

In this paper, we provide a new availability analysis of backhaul wireless communication networks which are supported by microwave links which evolved out of sharing between two different mobile network operators (MNOs) based on the Multi-State System (MSS) approach using Discrete-state Continuous-time Markov chain model. Our results show that such a jointly- constructed network was available to meet the required demand of the total system more than 99.8 % of the time.

Ad-hoc networks are independent of any infrastructure. The nodes are autonomous and make their own decisions. They also have limited energy resources. Thus, a node tends to behave selfishly when it is asked to forward the packets of other nodes. Indeed, it would rather choose to reject a forwarding request in order to save its energy. To overcome this problem, the nodes need to be motivated to cooperate. To this end, we propose a self-learning repeated game framework to enforce cooperation between the nodes of a network. This framework is inspired by the concept of “The Weakest Link” TV game. Each node has a utility function whose value depends on its cooperation in forwarding packets on a route as well as the cooperation of all the nodes that form this same route. The more these nodes cooperate the higher is their utility value. This would establish a cooperative spirit within the nodes of the networks. All the nodes will then more or less equally participate to the forwarding tasks which would then eventually guarantee a more efficient packets forwarding from sources to respective destinations. Simulations are run and the results show that the proposed framework efficiently enforces nodes to cooperate and outperforms two other self-learning repeated game frameworks which we are interested in.

Several works have outlined the fact that the mobility in intermittently connected wireless networks is strongly governed by human behaviors as they are basically human-centered. It has been shown that the users' moves can be correlated and that the social ties shared by the users highly impact their mobility patterns and hence the network structure. Tracking these correlations and measuring the strength of social ties have led us to propose an efficient distributed tensor-based link prediction technique. In fact, we are convinced that the feedback provided by such a prediction mechanism can enhance communication protocols such as opportunistic routing protocols. In this paper, we aim to bring out that measuring the stabilities of the link and the proximity at two hops can improve the efficiency of the proposed link prediction technique. To quantify these two parameters, we propose an entropy estimator in order to measure the two stability aspects over successive time periods. Then, we join these entropy estimations to the tensor-based link prediction framework by designing new prediction metrics. To assess the contribution of these entropy estimations in the enhancement of tensor-based link prediction efficiency, we perform prediction on two real traces. Our simulation results show that by exploiting the information corresponding to the link stability and/or to the proximity stability, the performance of the tensor-based link prediction technique is improved. Moreover, the results attest that our proposal's ability to outperform other well-known prediction metrics.

This paper presents an approach to speed up and enhance matching of virtual network requests to available resources in virtual network provisioning frameworks. The method consists of introducing a weight or score expressing the importance of the resources, their attributes and the values taken by these attributes. The scores are obtained through statistical analysis of the requests for virtual network resources. Previous methods treat resources equally and experience longer delays and lower efficiency during the matching process. They were originally not designed for virtual network provisioning and the proposed method in this work aims at addressing their weaknesses in scalability, in resource representation and incremental updates. To highlight this new method, a functional comparison with these previous techniques is proposed. Evaluation results of the score based clustering and matching algorithm show improved scalability and performance in clustering efficiency and matching delays for virtual network provisioning.

To meet the continuously increasing demands and requirements of mobile users, next generation wireless systems are more and more relaying on the coexistence of heterogeneous wireless networks. The objective is to provide any time and anywhere connectivity to users. One of the main challenges in such a heterogeneous environment is mobility management, including network selection and Vertical Handover (VHO) procedures. In this paper, we propose a VHO management solution combining the use of reputation as a Quality of Experience (QoE) indicator for fast decision-making and the Stream Control Transmission Protocol (SCTP) as a mobility protocol. This reputation-based Vertical Handover solution operates on the previous observed experiences of users within the different available networks to make fast decisions and uses the SCTP protocol that provides multihoming facilities. Performance results demonstrate that the proposed handover scheme provides seamless mobility, with low latency, good throughput and almost no packet loss.

In this paper, we propose an architecture for Network of Information mobile node (NetInf MN). It bears characteristics and features of basic NetInf node architecture with features introduced in the LISP MN architecture. We also introduce a virtual node layer for mobility management in the Network of Information. Therefore, by adopting this architecture no major changes in the contemporary network topologies is required. Thus, making our approach more practical.

Many studies have tried to evaluate wireless networks and especially the IEEE 802.15.4 standard. Hence, several papers have aimed to describe the functionalities of the physical (PHY) and medium access control (MAC) layers. They have highlighted some characteristics with experimental results and/or have attempted to reproduce them using theoretical models. In this paper, we use the first way to better understand IEEE 802.15.4 standard. Indeed, we provide a comprehensive model, able more faithfully to mimic the functionalities of this standard at the PHY and MAC layers. We propose a combination of two relevant models for the two layers. The PHY layer behavior is reproduced by a mathematical framework, which is based on radio and channel models, in order to quantify link reliability. On the other hand, the MAC layer is mimed by an enhanced Markov chain. The results show the pertinence of our approach compared to the model based on a Markov chain for IEEE 802.15.4 MAC layer. This contribution allows us fully and more precisely to estimate the network performance with different network sizes, as well as different metrics such as node reliability and delay. Our contribution enables us to catch possible failures at both layers.

Radio resource and mobility managements are becoming more and more complex within nowadays rich and heterogeneous wireless access networking systems. Multiple requirements, challenges and constraints, at both technical and economical perspectives have to be considered. While the main objective remains guaranteeing the best Quality of Service and optimal radio resource utilization, economical aspects have also to be considered including cost minimization for users and revenue maximization for network providers. In this paper, we propose a game theoretic scheme where each available network plays a Stackelberg game with a finite set of users, while users are playing a Nash game among themselves to share the limited radio resources. A Nash equilibrium point is found and used for vertical handover decision making and admission control. We also introduce in the proposed model the user's requirements in terms of quality of service according to its running application and the network reputation that is conducted from the users' quality of experience and we study the effect of these parameters on the network pricing and revenue maximization problems.

Constrained pricing in a cloud computing environment is addressed using game theory. The objective of the model and the game is to enable cloud providers to maximize their revenue while satisfying users by maximizing their utilities. The users net utility is modeled as a function of resource demand with a corresponding price. The game consists in the cloud provider suggesting differentiated prices according to demand and users updating their requests in view of the proposed price. The objective is to determine the optimal suggested prices by the cloud-provider and the optimal user demands. A theoretical model based on Stackelberg game is proposed and a Stackelberg/Nash equilibrium solution is found. Performance results show that rejecting a certain number of users is not the best decision to select. Cloud provider should deploy the right amount of resources to maintain good reputation while satisfying users' requirements.

This paper addresses the problem of power allocation of the downlink in cognitive radio networks (CRNs). In a CRN the channel availability for the secondary users (SUs) is a random process determined by primary user (PU) behavior. In an overlay CRN where the PU has the priority and CRN may use only the idle channels, CRN loses the symbols that are being transmitted during that time slot when the PU takes the channel and if the error correction code cannot handle the length of symbol loss. Further, in the next time slot, the CRN has to select again a free channel to continue the communication or in the event that all channels are occupied by PUs and other SUs it enters an indefinite waiting process. Thus it is intuitive that the time averaged data rate of the CRN does not only depend on the channel characteristics (governed by Shannon's law) but also on the channels' PU activity level. CRNs, with the spectrum sensing capabilities inherent to them, have the ability to characterize those activity statistics. In this paper, we propose two schemes named ENPA (equivalent noise of PU activity) and EBPA (effective bandwidth of PU activity), which take into account the PU activity in CRN downlink power allocation. These schemes are shown to achieve higher average rates than the water-filling algorithm which is optimal for a linear filter channel.

This paper presents a platform for virtual network (VN) provisioning across multiple domains. The platform decomposes VN provisioning into multiple steps to address the implications of limited information disclosure on resource discovery and allocation. A new VN embedding algorithm with simultaneous node and link mapping allows to assign resources within each domain. For inter-domain virtual link setup, we design and realize a signaling protocol that also integrates resource reservations for providing virtual links with Quality-of-Service guarantees. Experimental results show that small VNs can be provisioned within a few seconds.

The great success of social technologies such as media sharing, blogs and wikis, is transforming the Internet into a collaborative community. This paper presents our research towards the exploitation of P2P networks, semantic metadata and social tagging for home media sharing, with a vision of P2P-based Collaborative Home Media Community (CHMC). The goal of the proposed CHMC is to enable sharing, searching and tagging of multimedia contents based on semantic metadata within home networks as well as between homes connected by broadband networks. We will firstly present a hierarchical P2P network architecture where the super-peer model is applied. The super peers act as a gateway bridging the inner home network with the external Internet. We further propose the Resource Description Framework (RDF) triple-based metadata indexing, retrieving and tagging algorithms on structured P2P networks. Finally, a prototype is developed for the purposes of system validation and performance evaluation.

Virtualization is a promising approach to meet the objectives of next generation networks. It will enable building pervasive systems, with the possibilities of automated adaptation and provision of personalized services for users with different needs and contexts. In this paper, we propose the use of Avatars: software delegates to hide heterogeneity between entities such as networks, users, services and any resource or device. Avatars act at an abstraction overlay level, on behalf of their represented entities. They carry information and intelligence (workflows) and cooperate to make decisions like the reconfiguration of the entity they stand for. A software agents-based implementation is then proposed and evaluated.

Provisioning continuous services while moving through heterogeneous wireless networks is a main issue in the fourth generation wireless Networks. An efficient vertical handover decision making algorithm that takes into account services' requirements, considers users' preferences and guaranties seamless handover over heterogeneous technologies is required. In this paper, we propose an intelligent context-aware solution that considers both users and services requirements. It is based on advanced decision approaches like fuzzy logic and analytic hierarchy processes.

Recent developments in computer science and networking technologies motivate new families of applications that are sensitive to user and ambient contexts. The objective is to offer adaptive services that can be personalized to users' needs within heterogeneous, complex and dynamic environments. This requires enhanced and generic solutions for context representation and retrieval as well as for the interactions between heterogeneous systems and components. In this paper, we propose a conceptual model and a software framework using ontologies to build semantic context databases. Contextual information is modeled and implemented using first order logic and OWL to promote expressiveness and interoperability. The different steps to build the framework are detailed. The approach is then applied to the heterogeneous wireless access networks' use case. Measurements and performance results are provided and discussed.

Identity-Based Cryptography (IBC) offers the very interesting property that the user's public key is related directly to her/his identity. However, IBC suffers from key escrow because the user private key is generated by an external entity called the Private Key Generator (PKG). To resolve this problem, the Certificateless Cryptography CL-PKC was proposed. This mechanism uses a different method to generate the public key. Although CL-PKC resolves the IBC problem, it removes the IBC attractive advantage of easy public key generation. This system is also the target of Denial of Decryption attack (DoD) when an adversary replaces the user's public key. To overcome these weaknesses, we propose a new solution that merges these two cryptographic systems into only one in order to keep their advantages and to resolve their problems. We prove that our system is secure by using the random oracle model and we demonstrate that it offers a self-generated certificate. This solution can be used to secure object-centric systems that are expected to compose the future internet.

Beamforming techniques can enhance both capacity and coverage in wireless networks. To be efficient, most of these techniques require accurate estimation of the destination position. In vehicular environments, standard beamforming is not suitable when continuous tracking of node location is required. To handle inaccuracies in location estimation, this work uses Linearly Constrained Minimum Variance (LCMV) beamforming to form beams in adjoining directions to facilitate tracking. The directions are selected using the last accurate destination direction and movement parameters such as direction and speed variations of the source. Performance evaluations show that the proposed approach enhances system capacity and connectivity while reducing localization overhead.

This paper introduces I-MAC a new hybrid medium access control protocol for wireless sensor networks (WSNs). I- MAC combines both CSMA and TDMA techniques while introducing prioritization. It aims to improve energy efficiency as well as channel utilization. I-MAC is composed of two phases: a setup phase and a transmission phase. During the setup phase, several operations such as neighborhood discovery, slot assignment, local framing and global synchronization are run. During the transmission phase, a priority scheme is investigated to control the access to the channel. We focus in this paper on the description and evaluation of the different setup phase operations particularly DNIB, a new TDMA free collision slot assignment algorithm proposed for WSNs.

Network visualization is a promising concept to diversify the future Internet architecture into separate virtual networks (VN) that can support simultaneously multiple network experiments, services and architectures over a shared substrate network. To take full advantage of this paradigm this paper addresses the challenge of assigning VNs to the underlying physical network in a distributed and efficient manner. A distributed algorithm responsible for load balancing and mapping virtual nodes and links to substrate nodes and links has been designed, implemented and evaluated. A VN mapping protocol is proposed to communicate and exchange messages between agent-based substrate nodes to achieve the mapping. Results of the implementation and a performance evaluation of the distributed VN mapping algorithm using a multi-agent approach are reported.

This paper introduces I-MAC, a new medium access control protocol for wireless sensor networks. I-MAC targets at improving both channel utilisation and energy efficiency while taking into account traffic load for each sensor node according to its role in the network. I-MAC reaches its objectives through prioritized and adaptive access to the channel. I-MAC performances obtained through simulations for different network topologies, scenarios and traffic loads show significant improvements in energy efficiency, channel utilization, loss ratio and delay compared to existing protocols.

The personal network (PN) concept extends the personal area network (PAN) by including remote personal nodes, such as nodes at home or in the office. This extension is achieved through dynamic tunnels established between the remote PN entities. Scalability presents a major challenge for tunnel management in PNs due to their dynamic nature and the increasing number of simultaneously supported PNs. To reduce the PN management complexity, this paper proposes to use a peer-to-peer-based publish/subscribe naming (PPNS) system. The PPNS system enables an automated provisioning of the location information in the tunnel endpoints. A centralised management approach is proposed for the on-demand PN establishment and a distributed management approach is proposed for the always-on PN establishment.

A slot assignment (or scheduling) in TDMA based wireless sensor networks (WSNs) considering energy efficiency, scalability and complexity is presented. The algorithm named DNIB is a new TDMA free collision distributed slot assignment algorithm for WSNs. DNIB relies on two-hop neighborhood information. Performance analysis and simulations show that DNIB is scalable and outperforms existing mechanisms.

In this paper, we consider the multiple sinks placement problem in large-scale wireless sensor networks (WSN). The objective is to determine optimal sinks' positions that maximize the network lifetime by reducing energy consumption related to data transmissions from sensor nodes to different sinks. Balanced graph partitioning techniques are used to split the entire WSN into connected sub-networks. Smaller subnetworks are created, having similar characteristics and where energy consumption can be optimized independently but in the same way. Therefore, different approaches and mechanisms that enhance the network lifetime in small-size WSN can be deployed inside each sub-network. In this paper, we propose a simple and efficient approach for the placement of multiple sinks within large-scale WSNs. Performance results show that the proposed technique significantly enhances the network lifetime.

In this paper we propose the Mobile Party protocol, a new scheme for mobility management in the context of mesh networks. While most of proposed solutions for mobility management issue are routing protocol-independent mechanisms, our proposal integrates routing and mobility management functionalities together in the same scheme. This enhances considerably the performance of our proposal as shown in this paper. Through simulations and performance studies, we demonstrate that the proposed scheme provides a viable solution for mobility and routing management, assuring scalability and seamless communications.

In this paper we study a self organizing network architecture, party. Party is a new routing protocol intended to be applied in environments with large number of nodes where the scalability of the routing protocol plays an important role. Party's routing is unique and only depends on the current node's neighborhood. Routing tables are created on the basis of the first hop neighborhood only. We will show the protocol performance with a large number of nodes in the network, and compare it to the legacy ad hoc routing protocols. Results show a large improvement in terms of overhead and throughput.

A peer-to-peer (P2P)-based service discovery architecture for large-scale networks is presented. Services and queries are described using XML with descriptions mapped to numeric keys that preserve expressiveness. The architecture relies on a trie-based data structure built on top of a distributed hash table (DHT) to perform key-based routing of descriptions in service resolvers. Performance analysis and evaluation results collected from an emulation of the system show that the architecture is scalable, efficient and capable of load balancing for both service distribution and query resolution.

Ensuring seamless mobility for users is becoming one of the main objectives of ongoing research activities in the field of data telecommunications. More over, if some proposals are efficient in reducing handoff latency for single mobile hosts, adequate solutions are still required for moving networks. The NEtwork MObility protocol (NEMO), designed to provide network mobility, is not efficient to offer low latency handoff in the case of nested mobile networks because it uses fairly sub-optimal routing. In this paper, an optimized solution to reduce handoff latency for Nested Mobile Networks is proposed. This solution minimizes the registration delay component of the overall handoff latency. The paper describes the new architecture and mechanisms and provides simulation results indicating performance compared to the basic NEMO solution. Index Terms—low latency handoff, MIPv6, NEMO, Nested mobile network.

Streaming technique, widely developed and used in the internet to convey multimedia application (e.g., audio, video clip, etc.), is supposed to occupy a large share of the third generation system bandwidth. Recently, studies are focused on the transmission of streaming services over high speed downlink packet access (HSDPA) system using time shared channels. In this case, the stringent QoS constraints of these services may result in a loss of HSDPA cell capacity if an appropriate scheduler is not used. In this paper, we propose a new opportunistic scheduler that allows to transmit streaming traffic over HSDPA without losing much cell capacity. Results afforded by this scheduler are promising and show that our proposed scheduler outperforms the existing scheduling algorithms

This paper focuses on the interaction between the transport control protocol (TCP) layer and the radio interface in the high-speed downlink packet access (HSDPA) wireless system. In the literature, studies of the interaction between TCP and wireless networks are focused on the evaluation of user bit rate in the case of dedicated channels. In this paper, the interaction between TCP, hybrid automatic repeat request (HARQ), and scheduling techniques (especially, proportional fair scheduling) is conducted. Analytical models to evaluate HSDPA cell capacity, user bit rate, and interaction with TCP layer are presented. Even if as expected the bit rate per flow decreases strongly with the congestion frequency in the wired network, it is shown that the overall capacity achieved by HSDPA is not as affected by the TCP layer. Using this result, a method to reduce the effect of TCP on wireless network without losing much cell capacity is proposed. This method has the advantage of modifying the scheduling algorithm only and of not requiring any change to the TCP protocol.

This paper focuses on the effect of circuit switched (CS) services on high speed downlink packet access (HSDPA) packet services in the context of the UMTS FDD system. An objective of the analysis is to provide guidelines for the UMTS planning process to prioritize services when resources are dynamically shared between circuit and packet services. An analytical model, taking into account some of the features introduced in HSDPA (namely adaptive modulation and coding (AMC), hybrid automatic repeat request (HARQ) and fast cell selection) and the interaction between CS services and HSDPA, is proposed to approximate the HSDPA cell capacity. Simulation results to assess the efficiency of the analytical model are also reported.

MIMO/OFDM has recently been the subject of much interest because of its potential for meeting the stringent requirements of the next-generation broadband wireless communication systems. In this paper, a multiuser resource allocation and management approach which combines adaptive sub-carrier allocation as well as a power and bit distribution based on partial channel state information is proposed to significantly improve the performance of a multiuser MIMO/OFDM system. It is shown that the proposed methodology, which relies on the combination of beamforming and orthogonal space-time block coding, guarantees all the users with a quality of service required for a minimum targeted BER. It can also optimize the power efficiency as well as the sub-carriers allocation in order to maximize the data rate while ensuring fairness among all the users. In particular, it is shown that by taken advantage of the channel side information obtained through both, mean and covariance feedback, the proposed methodology exhibits resistance to the Doppler effects due to correlated channel variations while achieving good system performance.

In emerging personal area networks (PANs), consisting of heterogeneous devices and nodes, dynamic intra-PAN and PAN-to-infrastructure connectivity should be achieved transparently to end users and applications. This paper proposes a distributed virtual network interface (DVNI) solution for PAN device discovery, seamless handover and routing. DVNI is an extension of the VNI concept designed for seamless vertical handover in the case of a single device with multiple interfaces. The DVNI concept consists of making all PAN interfaces available via a common distributed virtual interface to mask changes in the PAN dynamics and connectivity

This paper focuses on the study of scheduling techniques in HSDPA system. In this context, we propose to use the score based (SB) scheduling algorithm in HSDPA. This algorithm is compared to another scheduling algorithm, called proportional fair (PF), widely used and studied in the literature. To make this comparison, an analytical model allowing to evaluate the cell capacity and the user bit rate is proposed. The radio channel is assumed to he a dense multipath frequency selective channel with uncorrelated signal envelope following a Rayleigh distribution and wide-sense stationary uncorrelated scattering (WSSUS). Results show that SB seems to be a better trade-off between "fairness" and "efficiency" than PF

The objective of this work is to extend universal plug and play (UPnP) that provides currently only local service discovery, to scalable and wide area discovery. This is achieved by combining the widely deployed UPnP framework with the INS/Twine intentional naming system that achieves naming and resource discovery over distributed overlay networks suitable for wide area coverage. A transcoder has been designed to achieve the required interworking between the two systems

This paper explores the viability and feasibility of using various virtual networking for personal networks (PNs). The virtual personal overlay network (VPON) concept is addressed and a comparison of several solutions to provide remote connectivity to personal nodes is reported. This is conducted for user and service provider provisioned based VPONs in terms of scalability, management complexity, node capacity and trust relationships.

Abstract This paper focuses on the interaction between the transmission control protocol (TCP) layer and the radio interface in a 3G wireless system. An analytical model to evaluate the impact of TCP on the UMTS‐HSDPA capacity is presented. A method to minimize the effect of TCP on wireless networks using shared channels is also proposed. High speed downlink packet access (HSDPA) is an evolution of the UMTS standard over the air interface to achieve higher aggregate bit rates through the introduction of adaptive modulation and coding, hybrid automatic repeat request (ARQ), fast scheduling, fast cell selection, and multiple input multiple output (MIMO) (space time coding and blast) techniques. The proposed model is used to evaluate the effect of the TCP protocol on the bit rate of various data services (at 64 and 128 kbps). As expected, the bit rate per flow decreases strongly if the congestion frequency in the wired network increases. However, the overall capacity achieved by HSDPA is not as affected by the TCP layer. Using this result, a method to maintain the bit rate per TCP flow at a given value without loosing much cell capacity is proposed. The findings are supported by simulation results. Copyright © 2005 John Wiley & Sons, Ltd.

An analytical model to evaluate the impact of TCP on the UMTS-HSDPA capacity is presented. A method to minimize the effect of TCP on wireless networks using shared channels is also proposed. HSDPA (high speed downlink packet access) achieves higher aggregate bit rates through the introduction of adaptive modulation and coding, hybrid ARQ, fast scheduling, fast cell selection and MIMO (space time coding and BLAST) techniques. The proposed model is used to evaluate the effect of the TCP protocol on the bit rate of various data services (at 64 and 128 Kbps). As expected, the bit rate per flow decreases strongly if congestion in the wired network increases. However, the capacity achieved by HSDPA is not as affected by TCP. Using this result, a method to maintain the bit rate per TCP flow at a given value without losing much cell capacity is proposed. The findings are supported by simulation results.

A semi analytical model and framework for the estimation of MIMO/HSDPA cell capacity for UMTS is presented. The model includes the effect of fast fading, mobility and coverage on the capacity. An algorithm to select the number of HS-DSCH codes allocated to a given user and the associated modulation and coding schemes is proposed. The capacity model is used to compare MIMO systems and provide insight on expected performance and capacity. Results indicate that adaptive MIMO in the sense of number of activated antennas at the transmitter increases capacity and that adaptive space time coding outperforms adaptive BLAST techniques for the explored scenarios. The effect of coverage on MIMO/HSDPA cell capacity is also reported.

An analytical model for the evaluation of HSDPA capacity is proposed and used to carry out a comparison of several MIMO systems. HSDPA is an evolution of the UMTS standard over the air interface to achieve higher aggregate bit rates through the introduction of adaptive modulation and coding, hybrid ARQ, fast scheduling, fast cell selection and MIMO (space time coding and Blast) techniques. The model is used to compare MIMO systems, a typically difficult task to provide some insight. The results are supported by a simulation.

Quality of service for Voice over IP on wireless links (WiFi) has to be measured by the perceptual speech quality. How can this subjective measures be "captured" by models that rely on objective measures based on transmission delays and packet losses monitoring. These parameters which are common on such links can affect dramatically speech quality. In this paper, The ITU-T legacy e-Model, recently proposed to qualify audio and well adapted to circuit switched networks is adapted for packet switched networks. A new model to automatically quantify or measure the speech quality perception for voice/audio communications over IP. named here "packet-e-Model", adapts the e-Model to packet networks in general with a special emphasis on WiFi.

This paper describes a policy based management system envisaged to achieve management of networks and services in diversified radio environments. This work is conducted in the European project MONASIDRE currently developing a distributed management system for three coexisting technologies. The technologies are UMTS, Hiperlan and DVB. The common open policy service (COPS) has been selected to achieve part of the service and network management infrastructure.

The focus of this paper is the performance of the AAL2/ATM transport technology for the Universal Terrestrial Radio Access Network (UTRAN) in UMTS/IMT2000. We particularly consider the case of voice and data streams, with a service differentiation between them conducted at the AAL2 level by the use of the weighted round robin (WRR) scheme. We obtain closed-form expressions for bounds on the delay violation probabilities for both voice and data frames, taking into account the impact of WRR as well as the traffic patterns inherent to UMTS.

Efficient radio resource allocation techniques are vital for providing acceptable capacity over the UMTS radio access network (RAN). Interaction among services and power requirements according to service bit rates can seriously limit the capacity. This work presents a resource allocation framework based on service differentiation and analyzes the capacity benefits achieved through service prioritization and dynamic rate adaptation. Conversational, streaming, interactive and background service classes are considered in the investigation. Real time (RT) services are offered over dedicated radio access bearers. Non real time (NRT) services use downlink shared channels to achieve multiplexing and better resource utilization. Combining prioritized connection admission control based on service class to minimize interference and dynamic rate adaptation for NRT services leads to significant improvements in overall system capacity. Without these capacity enhancement techniques the UNITS downlink can be fairly limited by interference and resource availability in the presence of multiple services operating at various rates and requiring diverse QoS.

A prioritized call admission control scheme for UMTS WCDMA networks to support multiple services is investigated. Admission is based on maximum base station transmit power and service class priority. The objective is to evaluate the interactions that exist among the various services and to assess how system performance and service behavior are related. Conversational, streaming and interactive services with distinct QoS requirements are considered. Prioritized resource allocation to achieve differentiated service treatment is used to improve spectrum utilization and revenue. Real time services with stringent QoS requirements operated in circuit switched mode use dedicated channels as radio bearers. Downlink shared channels are used to transport non real time data handled in packet switched mode. The investigation indicates that allocation policies that favor lower rate real time services achieve better overall performance in the examined mixed services scenarios.

A new scheme to maximize resource utilization in a cellular network while respecting constraints on handover dropping probability is proposed and analyzed. The constraints are set for each traffic class separately and have to be respected by the network independently of the area in a localized manner. The problem is formulated as a Markov Decision Process (MDP) and solved by making use of the model-free simulation-based Q-learning algorithm that runs at each cell. Integration of the handover limit in the model is achieved by observing which of the new call arrivals, at a particular state of the system, are mostly responsible for violation of the handover dropping limit. Through trial and error, the algorithm proceeds to the statistical elimination of new admissions in the system, those causing excessive dropping. Results obtained via the proposed Reinforcement Learning (RL) based approach are compared with a resource allocation that takes into consideration heterogeneous and unevenly distributed traffic over the geographical area under consideration. For the scenarios examined, comparable results and performance are observed with an advantage for RL in blocking and utilization.

The focus of this paper is the performance of ATM/AAL2 as a transport technology for Universal Terrestrial Radio Access Network (UTRAN) in UMTS/IMT-2000 setting. In this work, we propose an analytical model for the transparent multiplexing and transport through UTRAN of several voice channels onto a single AAL2/ATM VC. We investigate the queue building up at the AAL2 multiplexer in order to derive some characteristics of the sojourn time of voice frames within the UTRAN. Our results quantify the performance of AAL2/ATM-based transport of voice in terms of mean queue at the multiplexer, mean sojourn time and delay violation probability within the UTRAN.

Capacity and performance issues in UMTS FDD WCDMA system for the support of mixed heterogeneous services are investigated. The focus is on the downlink with traffic corresponding to UMTS quality of service (QoS) classes. After an initial capacity evaluation on a single service basis, the work is directed to assess the system performance in mixed services. The study aims at discerning the mutual interactions that exist among the services and evaluating the relationships between system performance and service characteristics. The emphasis is on how system performance and service behavior are inter-related in mixed traffic configurations and thus get an insight into resource control. The results are collected in terms of percentage of satisfied users, user blocking, probability of outage, throughput and delay. The analysis indicates that high data rate users are the most demanding and care must be taken at resource allocation to achieve QoS targets.

Performance of a prioritized resource control scheme for the UMTS W-CDMA downlink to support mixed multiple services is analyzed. The resource control strategy relies on connection admission and packet data scheduling on the basis of maximum base station transmit power and service class priority. The study aims at discerning the mutual interactions that exist among the services and evaluating the relationships between system performance and service characteristics. Conversational, streaming, interactive and background services with distinct QoS requirements are considered. Dedicated channels transport real time services with stringent QoS requirements while non real time data handled in packet switched mode are transported on downlink shared channels. The analysis indicates that service prioritization provides enhanced flexibility and control in meeting QoS constraints and results in better overall performance in the examined mixed services scenarios.

This paper addresses the potential benefits of using CIR based soft handover schemes for the UTRA WCDMA uplink. The objective is to find out whether additional improvement in cellular coverage and capacity can be achieved over gain based handover methods. Capacity, outage probability, throughput, interference and active set update rate serve as the basis for comparison. The system model implements in great detail the logical channel structure of the WCDMA air interface as specified by 3GPP and takes into account non uniform traffic load conditions to conduct the study.

The paper presents a nonpreemptive priority queueing system model for voice and HSCSD service integration in GSM. The study is conducted using a multidimensional Markov chain. An HSCSD connection can be established if the number of available channels is between a minimum acceptable capacity, b, and a maximum required capacity, B. This leads to an effective service time for HSCSD calls, proportional to the bandwidth negotiated with the base station. Finite buffers are used for new and handoff requests to increase the total carried traffic, while maintaining the quality-of-service (QoS) requirements. The analysis can be applied when mean service times for voice and HSCSD calls are different. The key parameters which control the new data service performance are identified and adequate formulas are obtained. Especially, delays experienced by HSCSD calls are evaluated and compared to an optimal transmission operating at the maximum capacity.

An integrated voice and data system using the channel segregation protocol (CS) is analyzed. To achieve each service target carrier to interference ratio (C/I), and meet the associated bit error rate (BER) requirements, the learning process inherent in the CS is extended by weighting the slot priorities, based on previous successful attempts, according to each service C/I requirement. Simulation results show an improvement in call block and achieved C/I levels for voice while the average delays for packet data are improved as well. The study is conducted for non real time packet data services exhibiting heavy tailed packet size distributions.

A mathematical model for analyzing the new GSM HSCSD (high speed circuit switched data) service specified by the European Telecommunications Standards Institute (ETSI) as a part of the GSM phase 2+ standard is presented. The study is conducted using a multidimensional Markov chain for a system carrying both HSCSD and the pre existing voice traffic. The key parameters which control the new data service performance are identified and adequate formulas obtained. Delays experienced by HSCSD calls are evaluated and compared to an optimal transmission operating at the maximum capacity. The model takes into account the channel allocation constraints for HSCSD connections and their impact on system performance is investigated by comparing results with an ideal unconstrained system. The analysis is applicable when mean service times for voice and HSCSD calls are different.

We analyze the link adaptation (LA) performance of a wireless personal communication system supporting two types of voice connections, full-rate and half-rate. Full-rate connections are used when the channel conditions are poor. When the channel conditions are good a full-rate connection is switched to a half-rate for improving spectral efficiency. For analysis simplicity, we consider flat uniform propagation conditions where, for a given propagation exponent, the received powers are determined by distances. Using this propagation model, users in the outer region of the cell will use full-rate channels while users in the proximity of the base station will use half-rate channels. The numerical results are extracted by forming a multi-dimensional Markov chain model of the proposed scheme and are compared with those obtained through a comprehensive computer simulation for call blocking probability, hand-off failure probability and carried traffic. A 42% increase in capacity is achieved when 75% of the users can use halfrate channels.

An analytical model for a nonpreemptive priority queueing system incorporating voice and data users in cellular mobile networks is presented. Upon arrival, handoff requests are inserted in a head of line (HOL) queue with finite capacity if no idle channels are available and the queue is not full. Priority is given to voice handoffs over data handoffs to avoid forced termination for voice users. This is achieved under a given time delay constraint for data users. A number of channels can be reserved for handover exclusive use. New attempts which find channels occupied are cleared from the system. For this priority policy we conduct a steady state queueing analysis when the mean service times of voice and data are different. Performance results show blocking and forced termination probabilities for both types of calls, waiting time for voice calls in the handoff area and delay experienced by data calls.

We analyze the performance of a priority oriented multiple access mechanism for wireless PCS. The scheme supports multiple services (including data transmission in ATM cell formats) to comply with future provisions in wireless networks. Numerical results are provided (through the definition of a multi-dimensional Markov chain model for the information slot queueing process) for the proposed technique which is called priority-based multiple access (PBMA). The overall access delay due to collision resolution and information slot allocation is also extracted from a comprehensive computer simulation. Finally, we set limits on the achievable multiplexing gains with priority-based bandwidth reservation schemes by considering an ideal scheduler for the information slot allocation.

This paper introduces an aggressive packet reservation multiple access scheme for integrated voice/data services in a TDMA mobile network. This access scheme uses slotted ALOHA with signatures. Different signatures are considered for voice and data in order to distinguish users and resolve collisions. Aggressive reservation is achieved by temporarily interrupting on-going data calls to make room for new talkspurt access requests. By taking advantage of data users tolerance to delay we show that a capacity increase can be achieved by introducing aggressive reservation in favor of voice users. Analytical models for voice users and data users are developed for characterizing message access delay and channel utilization. Numerical results show that system capacity improves while keeping voice packet dropping probability below a certain threshold.

Aggressive reservation as an additional means of reducing call loss due to failed hand over requests and call blocking in a multiple call hand-off context is proposed. Channel assignments of on-going calls are reconfigured to make room for new hand-off and access requests. Channel borrowing is used and priority for hand-offs is achieved by queuing and reserving a subset of available channels for their exclusive use. Simulation is conducted to assess the potential gains of the proposed approach.>