The vision of intelligent networks capable of automatically configuring crucial parameters for tasks such as resource provisioning, anomaly detection or load balancing largely hinges upon efficient AI-based algorithms. Time series forecasting is a fundamental building block for network-oriented AI and current trends lean towards the systematic adoption of models based on deep learning approaches. In this paper, we pave the way for a different strategy for the design of predictors for mobile network environments, and we propose the Thresholded Exponential Smoothing (TES) framework, a hybrid Statistical Modeling and Deep Learning tool that allows for improving the performance of network Key Performance Indicator (KPI) forecasting. We adapt our framework to two state-of-the-art deep learning tools for time series forecasting, based on Recurrent Neural Networks and Transformer architectures. We experiment with TES by showcasing its superior support for three practical network management use cases, i.e. (i) anticipatory allocation of network resources, (ii) mobile traffic anomaly prediction, and (iii) mobile traffic load balancing. Our results, derived from traffic measurements collected in operational mobile networks, demonstrate that the TES framework can yield substantial performance gains over current state-of-the-art predictors in the applications considered.

Open and virtualized Radio Access Networks (vRANs) are breeding a new market with unprecedented opportunities. However, carrier-grade vRANs today are expensive and energy-hungry, as they rely on hardware accelerators (HAs) that are dedicated to individual distributed units (DUs). In this talk, we advocate for a paradigm shift in the design of vRANs, where we share pools of heterogeneous processors among DUs. This enables cost savings resulting from resource pooling, in addition to reducing operating costs by efficiently scheduling different tasks across different processors. The talk will address the challenges involved in deploying such a paradigm, addressing the coordination between DUs and heterogeneous computing infrastructure as well as the design of compute-aware radio scheduling leveraging AI techniques. Performance evaluation results will be discussed for different processor platforms ranging from GPUs, CPUs and Quantum computing. Finally, a discussion on future work will include the seamless combination of in-line and look-aside HAs in a common platform.

In the past few years, network infrastructures have transitioned from prominently hardware-based models to networks of functions, where software components provide the required functionalities with unprecedented scalability and flexibility. However, this new vision entails a completely new set of problems related to resource provisioning and the network function operation, making it difficult to manage the network function lifecycle management with traditional, human-in-the-loop approaches. Novel zero-touch management solutions promise autonomous network operation with limited human interactions. However, modeling network function behavior into compelling variables and algorithm is an aspect that such solutions must take into account. In this paper, we propose AZTEC+, a data-driven solution for anticipatory resource provisioning in network slicing scenarios. By leveraging a hybrid and modular deep learning architecture, AZTEC+ not only forecasts the future demands for target services but also identifies the best trade-offs to balance the costs due to the instantiation and reconfiguration of such resources. Our experimental evaluation, based on real-world network data, shows how AZTEC+ can outperform state-of-the-art management solutions for a large set of metrics.

Open and virtualized Radio Access Networks (vRANs) are breeding a new market with unprecedented opportunities. However, carrier-grade vRANs today are expensive and energy-hungry, as they rely on hardware accelerators (HAs) that are dedicated to individual distributed units (DUs). In this paper, we argue that sharing pools of heterogeneous processors among DUs leads to more cost- and energy-efficient vRANs. We then design CloudRIC, a system that, powered by lightweight data-driven models, meets specific reliability targets while (i) coordinating access between DUs and heterogeneous computing infrastructure; and (ii) assisting DUs with compute-aware radio scheduling procedures. Experiments on a GPU-accelerated O-Cloud show that CloudRIC can achieve, respectively, 3x and 15x mean gains in energy- and cost-efficiency under real RAN workloads while ensuring 99.999% reliability even in dense scenarios.

Open and virtualized Radio Access Networks (vRANs) are breeding a new market with unprecedented opportunities. However, carrier-grade vRANs today are expensive and energy-hungry, as they rely on hardware accelerators (HAs) that are dedicated to individual distributed units (DUs). We demonstrate CloudRIC [17], a system that, powered by lightweight data-driven models, meets specific reliability targets while (i) coordinating access between DUs and heterogeneous computing infrastructure; and (ii) assisting DUs with compute-aware radio scheduling procedures. Using a user-friendly dashboard to control an experimental testbed remotely, we demonstrate that CloudRIC achieves comparable reliability performance to a DU-dedicated platform while offering up to 40x higher cost-efficiency and up to 6x higher energy efficiency when pooling resources for up to 70 DUs.

Next-generation mobile networks will rely on their autonomous operation. Virtual Network Functions empowered by Artificial Intelligence (AI) and Machine Learning (ML) can adapt to varying environments that encompass both network conditions and the cloud platform executing them. In this view, it becomes paramount tounderstand whyAI/ML algorithms made a decision, to be able to reason upon those decisions and, eventually, take further decisions related toe.g., network orchestration. In this paper, we present ATHENA, an ML-based radio resource scheduler for virtualized Radio Access Network (RAN) system. Our real-software implementation shows that the proposed ML-based approach can outperform the baseline solution. We discuss how additional re-orchestration actions can be taken by analyzing our scheduling decisions and learning from the past.

Reconfigurable intelligent surfaces (RISs) are expected to play a crucial role in reaching the key performance indicators (KPIs) for future 6G networks. Their competitive edge over conventional technologies lies in their ability to control the wireless environment propagation properties at will, thus revolutionizing the traditional communication paradigm that perceives the communication channel as an uncontrollable black box. As RISs transition from research to market, practical deployment issues arise. Major roadblocks for commercially viable RISs are i) the need for a fast and complex control channel to adapt to the ever-changing wireless channel conditions, and ii) an extensive grid to supply power to each deployed RIS. In this paper, we question the established RIS practices and propose a novel RIS design combining self-configuration and energy self-sufficiency capabilities. We analyze the feasibility of devising fully-autonomous RISs that can be easily and seamlessly installed throughout the environment, following the new internet-of-surfaces (IoS) paradigm, requiring modifications neither to the deployed mobile network nor to the power distribution system. In particular, we introduce ARES, an Autonomous RIS with Energy harvesting and Self-configuration solution. ARES achieves outstanding communication performance while demonstrating the feasibility of energy harvesting (EH) for RISs power supply in future deployments.

Auto-scaling techniques aim to keep the right number of active servers for the current load: if this number is too small we risk service disruption, but if it is too large we waste resources. Despite the interest in the efficient operation of this type of systems, no prior work has addressed auto-scaling techniques for Network Function Virtualization (NFV) with stringent reliability requirements such as those envisioned in 5G (5 or 6 nines). To achieve such levels of reliability, we need to account for both the activation delay until servers become available (i.e., the wake-up or activation time) and the fallible nature of servers (which may fail with some probability). In this article, we build on control theory to design an auto-scaling technique for a server farm for NFV that guarantees certain reliability while minimizing the number of active resources. We show that the use of well-established tools from control theory results in convergence times much shorter than those obtained with state-of-the-art reinforcement learning techniques. This shows that, despite the current trend to apply machine learning to all sorts of networking problems, there may be some cases where other techniques (such as control theory) can be more suitable.

Zero-touch network management is one of the most ambitious yet strongly required paradigms for beyond 5G and 6G mobile communication systems. Achieving full automation requires a closed loop that combines (i) network status data collection and processing, (ii) predictive capabilities based on such data to anticipate upcoming needs, and (iii) effective decision making that best addresses such future needs through proper network control and orchestration. Recent seminal works have proposed approaches to jointly implement the last two phases above via a single deep learning model trained on past network status to directly optimize future decisions. This is achieved by designing custom loss functions that directly embed the management task objective. Experiments with real-world measurement data have demonstrated that this strategy leads to substantial performance gains across diverse network management tasks. In this paper, we go one step beyond the loss tailoring schemes above, and introduce a loss meta-learning paradigm that (i) reduces the need for human intervention at model design stage, (ii) eases explainability and transferability of trained deep learning models for network management, and (iii) outperforms custom losses across a range of controlled experiments and practical use cases.

As upcoming, beyond-5G (B5G) wireless network generations are expected to deliver much better performance than existing solutions, Reconfigurable intelligent surfaces (RISs) are gaining relevance as one of the new key technologies able to facilitate such improvement. Interestingly, they can redesign how the propagation environment is conceived by giving an opportunity to programmatically alter it: they can be configured to behave as orientable mirrors, scatterers, or lenses. This flexibility allows for the successful exploitation of bands which provide superior performance in wireless links but present poor propagation properties. However, this fascinating technology comes at not negligible costs: RISs require ad-hoc design, deployment and management operations to be fully exploited. In this paper, we tackle one of the open problems in the RISs literature: the optimal placement. We propose a model-based and a model-free approach, respectively RISA and AI-RISA, showcasing their large-scale solutions on synthetic topologies to improve communication performance while solving the “dead-zone” coverage problem. Additionally, our frameworks are empirically validated within a realistic indoor scenario, the Rennes railway station, showing how a complex indoor propagation environment can be fully disciplined by an advanced RISs installation.

A variety of network management and orchestration (MANO) tasks take advantage of predictions to support anticipatory decisions. In many practical scenarios, such predictions entail two largely overlooked challenges: (i) the exact relationship between the predicted values (e.g., reserved resources) and the performance objective (e.g., quality of experience of end users) is often tangled and cannot be known a priori, and (ii) the objective is linked in many cases to multiple predictions that contribute to it in an intertwined way (e.g., resources to reserved are limited and must be shared among competing flows). We present AutoManager, a novel meta-learning model that can support complex MANO tasks by addressing these two challenges. Our solution learns how multiple intertwined predictions affect a common performance goal, and steers them so as to attain the correct operation point under a-priori unknown loss functions. We demonstrate AutoManager in practical, complex use cases based on real-world traffic measurements; our experiments show that the model produces forecasts that are accurate and tailored to the MANO task in a fully automated way.

Cloud-native mobile networks pave the road for Network Slicing as a Service (NSaaS), where slice overbooking is a promising management strategy to maximize the revenues from admitted slices by exploiting the fact they are unlikely to fully utilize their reserved resources concurrently. While seminal works have shown the potential of overbooking for NSaaS in simplistic cases, its realization is challenging in practical scenarios with realistic slice demands, where its actual performance remains to be tested. In this paper, we propose kaNSaaS, a complete solution for NSaaS management with slice overbooking that combines deep learning and classical optimization to jointly solve the key tasks of admission control and resource allocation. Experiments with large-scale measurement data of actual tenant demands show that kaNSaaS increases the network operator profits by 300% with respect to NSaaS management strategies that do not employ overbooking, while outperforming by more than 20% state-of-the-art overbooking-based approaches.

The roll-out phase of the next generation of mobile networks (5G) has started and operators are required to devise deployment solutions while pursuing localization accuracy maximization. Enabling location-based services is expected to be a unique selling point for service providers now able to deliver critical mobile services, e.g., autonomous driving, public safety, remote operations. In this paper, we propose a novel roll-out base station placement solution that, given a Throughput-Positioning Ratio (TPR) target, selects the location of new-generation base stations (among available candidate sites) such that the throughput and localization accuracy are jointly maximized. Moving away from the canonical position error bound (PEB) analysis, we develop a realistic framework in which each positioning measurement is affected by errors depending upon the actual wireless channel between the measuring base station and the target device. Our solution, referred to as LOKO, is a fast-converging algorithm that can be readily applied to current 5G (or future) roll-out processes. LOKO is validated by means of an exhaustive simulation campaign considering real existing deployments of a major European network operator as well as synthetic scenarios.

Network slicing is a key capability for next generation mobile networks. It enables infrastructure providers to cost effectively customize logical networks over a shared infrastructure. A critical component of network slicing is resource allocation, which needs to ensure that slices receive the resources needed to support their services while optimizing network efficiency. In this paper, we propose a novel approach to slice-based resource allocation named Guaranteed seRvice Efficient nETwork slicing (GREET). The underlying concept is to set up a constrained resource allocation game, where ($i$) slices unilaterally optimize their allocations to best meet their (dynamic) customer loads, while ($ii$) constraints are imposed to guarantee that, if they wish so, slices receive a pre-agreed share of the network resources. The resulting game is a variation of the well-known Fisher market, where slices are provided a budget to contend for network resources (as in a traditional Fisher market), but (unlike a Fisher market) prices are constrained for some resources to ensure that the pre-agreed guarantees are met for each slice. In this way, GREET combines the advantages of a share-based approach (high efficiency by flexible sharing) and reservation-based ones (which provide guarantees by assigning a fixed amount of resources). We characterize the Nash equilibrium, best response dynamics, and propose a practical slice strategy with provable convergence properties. Extensive simulations exhibit substantial improvements over network slicing state-of-the-art benchmarks.

Intent-Based Networking mandates that high-level human-understandable intents are automatically interpreted and implemented by network management entities. As a key part in this process, it is required that network orchestrators activate the correct automated decision model to meet the intent objective. In anticipatory networking tasks, this requirement maps to identifying and deploying a tailored prediction model that can produce a forecast aligned with the specific –and typically complex– network management goal expressed by the original intent. Current forecasting models for network demands or network management optimize generic, non-flexible, and manually designed objectives, hence do not fulfil the needs of anticipatory Intent-Based Networking. To close this gap, we propose LossLeaP, a novel forecasting model that can autonomously learn the relationship between the prediction and the target management objective, steering the former to minimize the latter. To this end, LossLeaP adopts an original deep learning architecture that advances current efforts in automated machine learning, towards a spontaneous design of loss functions for regression tasks. Extensive experiments in controlled environments and in practical application case studies prove that LossLeaP outperforms a wide range of benchmarks, including state-of-the-art solutions for network capacity forecasting.

Forecasting is a task of ever increasing importance for the operation of mobile networks, where it supports anticipatory decisions by network intelligence and enables emerging zero-touch service and network management models. While current trends in forecasting for anticipatory networking lean towards the systematic adoption of models that are purely based on deep learning approaches, we pave the way for a different strategy to the design of predictors for mobile network environments. Specifically, following recent advances in time series prediction, we consider a hybrid approach that blends statistical modelling and machine learning by means of a joint training process of the two methods. By tailoring this mixed forecasting engine to the specific requirements of network traffic demands, we develop a Thresholded Exponential Smoothing and Recurrent Neural Network (TES-RNN) model. We experiment with TES-RNN in two practical network management use cases, i.e., (i) anticipatory allocation of network resources, and (ii) mobile traffic anomaly prediction. Results obtained with extensive traffic workloads collected in an operational mobile network show that TES-RNN can yield substantial performance gains over current state-of-the-art predictors in both applications considered.

The virtualization of radio access networks (vRAN) is the last milestone in the NFV revolution. However, the complex dependencies between computing and radio resources make vRAN resource control particularly daunting. We present vrAIn, a dynamic resource orchestrator for vRANs based on deep reinforcement learning. First, we use an autoencoder to project high-dimensional context data (traffic and channel quality patterns) into a latent representation. Then, we use a deep deterministic policy gradient (DDPG) algorithm based on an actor-critic neural network structure and a classifier to map contexts into resource control decisions. We have evaluated vrAIn experimentally, using an open-source LTE stack over different platforms, and via simulations over a production RAN. Our results show that: (i) vrAIn provides savings in computing capacity of up to 30% over CPU-agnostic methods; (ii) it improves the probability of meeting QoS targets by 25% over static policies; (iii) upon computing capacity under-provisioning, vrAIn improves throughput by 25% over state-of-the-art schemes; and (iv) it performs close to an optimal offline oracle. To our knowledge, this is the first work that thoroughly studies the computational behavior of vRANs and the first approach to a model-free solution that does not need to assume any particular platform or context.

The softwarization of mobile networks enables an efficient use of resources, by dynamically scaling and re-assigning them following variations in demand. Given that the activation of additional servers is not immediate, scaling up resources should anticipate traffic demands to prevent service disruption. At the same time, the activation of more servers than strictly necessary results in a waste of resources, and thus should be avoided. Given the stringent reliability requirements of 5G applications (up to 6 nines) and the fallible nature of servers, finding the right trade-off between efficiency and service disruption is particularly critical. In this paper, we analyze a generic auto-scaling mechanism for communication services, used to de(activate) servers in a cluster, based on occupation thresholds. We model the impact of the activation delay and the finite lifetime of the servers on performance, in terms of power consumption and failure probability. Based on this model, we derive an algorithm to optimally configure the thresholds. Simulation results confirm the accuracy of the model both under synthetic and realistic traffic patterns as well as the effectiveness of the configuration algorithm. We also provide some insights on the best strategy to support an energy-efficient highly-reliable service: deploying a few powerful and reliable machines versus deploying many machines, but less powerful and reliable.

Fifth generation (5G) and beyond systems require flexible and efficient monitoring platforms to guarantee optimal key performance indicators (KPIs) in various scenarios. Their applicability in Edge computing environments requires lightweight monitoring solutions. This work evaluates different candidate technologies to implement a monitoring platform for 5G and beyond systems in these environments. For monitoring data plane technologies, we evaluate different virtualization technologies, including bare metal servers, virtual machines, and orchestrated containers. We show that containers not only offer superior flexibility and deployment agility, but also allow obtaining better throughput and latency. In addition, we explore the suitability of the Function-as-a-Service (FaaS) serverless paradigm for deploying the functions used to manage the monitoring platform. This is motivated by the event oriented nature of those functions, designed to set up the monitoring infrastructure for newly created services. When the FaaS warm start mode is used, the platform gives users the perception of resources that are always available. When a cold start mode is used, containers running the application’s modules are automatically destroyed when the application is not in use. Our analysis compares both of them with the standard deployment of microservices. The experimental results show that the cold start mode produces a significant latency increase, along with potential instabilities. For this reason, its usage is not recommended despite the potential savings of computing resources. Conversely, when the warm start mode is used for executing configuration tasks of monitoring infrastructure, it can provide similar execution times to a microservice-based deployment. In addition, the FaaS approach significantly simplifies the code logic in comparison with microservices, reducing lines of code to less than 38%, thus reducing development time. Thus, FaaS in warm start mode represents the best candidate technology to implements such management functions.

RAN virtualization will become a key technology for the last mile of next-generation mobile networks driven by initiatives such as the O-RAN alliance. However, due to the computing fluctuations inherent to wireless dynamics and resource contention in shared computing infrastructure, the price to migrate from dedicated to shared platforms may be too high. Indeed, we show in this paper that the baseline architecture of a base station's distributed unit (DU) collapses upon moments of deficit in computing capacity. Recent solutions to accelerate some signal processing tasks certainly help but do not tackle the core problem: a DU pipeline that requires predictable computing to provide carrier-grade reliability.

We demonstrate Nuberu. The scenario consists of a DU under test (DuT), and one or more DUs sharing computing resources. A dashboard lets us control (𝑖) the type of DuT: “Baseline”, implemented with a legacy full-fledged eNB, or Nuberu; (𝑖𝑖) the number of competing vDUs; and (𝑖𝑖𝑖) their SNR. A second screen shows real-time metrics: (𝑖) the processing latency of the TBs from each vDU instance; (𝑖𝑖) the throughput performance of DuT; (𝑖𝑖𝑖) the processing latency of DU jobs from DuT; and (𝑖𝑣) the ratio of latency constraint violations of DuT jobs. We show how the throughput attained by the baseline DU approach collapses upon sufficiently high computing interference from the competing DUs. Conversely, we show that the DU design introduced in [3] preserves reliability irrespective of the computing interference.

The introduction of the Software-Defined Networking paradigm for the operation, administration and management of WAN environments, known as SD-WAN, is becoming increasingly popular due to the clear advantages that this technology provides, such as the reduction of CAPEX and OPEX related to the networking infrastructure or the flexibility provided by the development and deployment of network applications regardless of the underlying infrastructure. However, there is still a lack of solutions for enterprise networks, without carrying a substantial increase in cost, adapted to their daily reality, in which the WAN can be split in several domains, each of them possibly managed by different operators and based on different technologies and protocols. As a result, today it is not possible to have a complete solution for SD-WAN that covers all domains, reducing its scope to a single set of domains which may interact with other external domains. To address this issue, this paper presents Alviu, a SD-WAN network orchestrator based on open-source technologies that assures end-to-end network slicing to managed enterprise and academic networks thanks to a dynamic intentbased configuration of the different elements of the managed network. In addition to positioning Alviu in the current state of the art and detailing its architecture, we also evaluate Alviu’s operation in a testbed that emulates the interconnection between a set of SD-WAN domains with are also connected to other external domains based on legacy routing protocols, assessing the deployment time depending on the domains present in the complete network.

The combination of network softwarization with network slicing enables the provisioning of very diverse services over the same network infrastructure. However, it also creates a complex environment where the orchestration of network resources cannot be guided by traditional, human-in-the-loop network management approaches. New solutions that perform these tasks automatically and in advance are needed, paving the way to zero-touch network slicing. In this paper, we propose AZTEC, a data-driven framework that effectively allocates capacity to individual slices by adopting an original multi-timescale forecasting model. Hinging on a combination of Deep Learning architectures and a traditional optimization algorithm, AZTEC anticipates resource assignments that minimize the comprehensive management costs induced by resource overprovisioning, instantiation and reconfiguration, as well as by denied traffic demands. Experiments with real-world mobile data traffic show that AZTEC dynamically adapts to traffic fluctuations, and largely outperforms state-of-the-art solutions for network resource orchestration.

The dynamic management of network resources is both a critical and challenging task in upcoming multi-tenant mobile networks, which requires allocating capacity to individual network slices so as to accommodate future time-varying service demands. Such an anticipatory resource configuration process must be driven by suitable predictors that take into account the monetary cost associated to overprovisioning or underprovisioning of networking capacity, computational power, memory, or storage. Legacy models that aim at forecasting traffic demands fail to capture these key economic aspects of network operation. To close this gap, we present DeepCog, a deep neural network architecture inspired by advances in image processing and trained via a dedicated loss function. Unlike traditional traffic volume predictors, DeepCog returns a cost-aware capacity forecast, which can be directly used by operators to take short- and long-term reallocation decisions that maximize their revenues. Extensive performance evaluations with real-world measurement data collected in a metropolitan-scale operational mobile network demonstrate the effectiveness of our proposed solution, which can reduce resource management costs by over 50% in practical case studies.

Abstract Anonymous communication tools, such as Tor, are extensively employed by users who want to keep their web activity private. But recent works have shown that when a local, passive adversary observes nothing more than the timestamp, size and direction (incoming or outgoing) of the packets, it can still identify with high accuracy the website accessed by a user. Several defenses against these website fingerprinting attacks have been proposed but they come at the cost of a significant overhead in traffic and/or website loading time. We propose a defense against website fingerprinting which exploits multihoming, where a user can access the Internet by sending the traffic through multiple networks. With multihoming, it is possible to protect against website fingerprinting by splitting traffic among the networks, i.e., by removing packets from one network and sending them through another, whereas current defenses can only add packets. This enables us to design a defense with no traffic overhead that, as we show through extensive experimentation against state-of-the-art attacks, reaches the same level of privacy as the best existing practical defenses. We describe and evaluate a proof-ofconcept implementation of our defense and show that is does not add significant loading-time overhead. Our solution is compatible with other state-of-the-art defenses, and we show that combining it with another defense further improves privacy.

It is now commonly agreed that future 5G Networks will build upon the network slicing concept. The ability to provide virtual, logically independent “slices” of the network will also have an impact on the models that will sustain the business ecosystem. Network slicing will open the door to new players: the infrastructure provider, which is the owner of the infrastructure, and the tenants, which may acquire a network slice from the infrastructure provider to deliver a specific service to their customers. In this new context, how to correctly handle resource allocation among tenants and how to maximize the monetization of the infrastructure become fundamental problems that need to be solved. In this paper, we address this issue by designing a network slice admission control algorithm that (i) autonomously learns the best acceptance policy while (ii) it ensures that the service guarantees provided to tenants are always satisfied. The contributions of this paper include: (i) an analytical model for the admissibility region of a network slicing-capable 5G Network, (ii) the analysis of the system (modeled as a Semi-Markov Decision Process) and the optimization of the infrastructure providers revenue, and (iii) the design of a machine learning algorithm that can be deployed in practical settings and achieves close to optimal performance.

Network slicing is a new paradigm for future 5G networks where the network infrastructure is divided into slices devoted to different services and customized to their needs. With this paradigm, it is essential to allocate to each slice the needed resources, which requires the ability to forecast their respective demands. To this end, we present DeepCog, a novel data analytics tool for the cognitive management of resources in 5G systems. DeepCog forecasts the capacity needed to accommodate future traffic demands within individual network slices while accounting for the operator's desired balance between resource overprovisioning (i.e., allocating resources exceeding the demand) and service request violations (i.e., allocating less resources than required). To achieve its objective, DeepCog hinges on a deep learning architecture that is explicitly designed for capacity forecasting. Comparative evaluations with real-world measurement data prove that DeepCog's tight integration of machine learning into resource orchestration allows for substantial (50% or above) reduction of operating expenses with respect to resource allocation solutions based on state-of-the-art mobile traffic predictors. Moreover, we leverage DeepCog to carry out an extensive first analysis of the trade-off between capacity overdimensioning and unserviced demands in adaptive, sliced networks and in presence of real-world traffic.

The virtualization of radio access networks (vRAN) is the last milestone in the NFV revolution. However, the complex dependencies between computing and radio resources make vRAN resource control particularly daunting. We present vrAIn, a dynamic resource controller for vRANs based on deep reinforcement learning. First, we use an autoencoder to project high-dimensional context data (traffic and signal quality patterns) into a latent representation. Then, we use a deep deterministic policy gradient (DDPG) algorithm based on an actor-critic neural network structure and a classifier to map (encoded) contexts into resource control decisions. We have implemented vrAIn using an open-source LTE stack over different platforms. Our results show that vrAIn successfully derives appropriate compute and radio control actions irrespective of the platform and context: (i) it provides savings in computational capacity of up to 30% over CPU-unaware methods; (ii) it improves the probability of meeting QoS targets by 25% over static allocation policies using similar CPU resources in average; (iii) upon CPU capacity shortage, it improves throughput performance by 25% over state-of-the-art schemes; and (iv) it performs close to optimal policies resulting from an offline oracle. To the best of our knowledge, this is the first work that thoroughly studies the computational behavior of vRANs, and the first approach to a model-free solution that does not need to assume any particular vRAN platform or system conditions.

While the application of the NFV paradigm into the network is proceeding full steam ahead, there is still one last mile- stone to be achieved in this context: the virtualization of the radio access network (vRAN). Due to the very complex de- pendency between the radio conditions and the computing resources needed to provide the baseband processing func- tionality, attaining an efficient resource control is particularly challenging. In this demonstration, we will showcase vrAIn, a vRAN dynamic resource controller that employs deep re- inforcement learning to perform resource assignment deci- sions. vrAIn, which is implemented using an open-source LTE stack over a Linux platform, can achieve substantial sav- ings in the used CPU resources while maintaining the target QoS for the attached terminals and maximize throughput when there is a deficit of computational capacity.

This paper focuses on optimizing resource allocation amongst a set of tenants, network slices, supporting dynamic customer loads over a set of distributed resources, e.g., base stations. The aim is to reap the benefits of statistical multiplexing resulting from flexible sharing of `pooled' resources, while enabling tenants to differentiate and protect their performance from one another's load fluctuations. To that end we consider a setting where resources are grouped into Virtual Resource Pools (VRPs) wherein resource allocation is jointly and dynamically managed. Specifically for each VRP we adopt a Share-Constrained Proportionally Fair (SCPF) allocation scheme where each tenant is allocated a fixed share (budget). This budget is to be distributed equally amongst its active customers which in turn are granted fractions of their associated VRP resources in proportion to customer shares. For a VRP with a single resource, this translates to the well known Generalized Processor Sharing (GPS) policy. For VRPs with multiple resources SCPF provides a flexible means to achieve load elastic allocations across tenants sharing the pool. Given tenants' per resource shares and expected loads, this paper formulates the problem of determining optimal VRP partitions which maximize the overall expected shared weighted utility while ensuring protection guarantees. For a high load/capacity setting we exhibit this network utility function explicitly, quantifying the benefits and penalties of any VRP partition, in terms of network slices' ability to achieve performance differentiation, load balancing, and statistical multiplexing. Although the problem is shown to be NP-Hard, a simple greedy heuristic is shown to be effective. Analysis and simulations confirm that the selection of optimal VRP partitions provide a practical avenue towards improving network utility in network slicing scenarios with dynamic loads.

The economic sustainability of future mobile networks will largely depend on the strong specialization of its offered services. Network operators will need to provide added value to their tenants, by moving from the traditional one-size-fits-all strategy to a set of virtual end-to-end instances of a common physical infrastructure, named network slices, which are especially tailored to the requirements of each application. Implementing network slicing has significant consequences in terms of resource management: service customization entails assigning to each slice fully dedicated resources, which may also be dynamically reassigned and overbooked in order to increase the cost-efficiency of the system. In this paper, we adopt a data-driven approach to quantify the efficiency of resource sharing in future sliced networks. Building on metropolitan-scale real-world traffic measurements, we carry out an extensive parametric analysis that highlights how diverse performance guarantees, technological settings, and slice configurations impact the resource utilization at different levels of the infrastructure in presence of network slicing. Our results provide insights on the achievable efficiency of network slicing architectures, their dimensioning, and their interplay with resource management algorithms at different locations and reconfiguration timescales.

Network slicing to enable resource sharing among multiple tenants-network operators and/or services-is considered as a key functionality for next generation mobile networks. This paper provides an analysis of a well-known model for resource sharing, the share-constrained proportional allocation mechanism, to realize network slicing. This mechanism enables tenants to reap the performance benefits of sharing, while retaining the ability to customize their own users' allocation. This results in a network slicing game in which each tenant reacts to the user allocations of the other tenants so as to maximize its own utility. We show that, for elastic traffic, the game associated with such strategic behavior converges to a Nash equilibrium. At the Nash equilibrium, a tenant always achieves the same or better performance than that of a static partitioning of resources, thus providing the same level of protection as static partitioning. We further analyze the efficiency and fairness of the resulting allocations, providing tight bounds for the price of anarchy and envy-freeness. Our analysis and extensive simulation results confirm that the mechanism provides a comprehensive practical solution to realize network slicing. Our theoretical results also fills a gap in the analysis of this resource allocation model under strategic players.

Network slicing is considered one of the main pillars of the upcoming 5G networks. Indeed, the ability to slice a mobile network and tailor each slice to the needs of the corresponding tenant is envisioned as a key enabler for the design of future networks. However, this novel paradigm opens up to new challenges, such as isolation between network slices, the allocation of resources across them, and the admission of resource requests by network slice tenants. In this paper, we address this problem by designing the following building blocks for supporting network slicing: i) traffic and user mobility analysis, ii) a learning and forecasting scheme per slice, iii) optimal admission control decisions based on spatial and traffic information, and iv) a reinforcement process to drive the system towards optimal states. In our framework, namely RL-NSB, infrastructure providers perform admission control considering the service level agreements (SLA) of the different tenants as well as their traffic usage and user distribution, and enhance the overall process by the means of learning and the reinforcement techniques that consider heterogeneous mobility and traffic models among diverse slices. Our results show that by relying on appropriately tuned forecasting schemes, our approach provides very substantial potential gains in terms of system utilization while meeting the tenants' SLAs.

Enterprise Networking has a strong set of requirements in terms of resiliency, reliability and resources usage. With current approaches being based on monolithic and expensive infrastructures using dedicated overlay links, providers are moving to more economical hybrid solutions that encompass private dedicated links with public/regular Internet connections. However, these usually rely on complex, hardware-dependent and/or proprietary Traffic Engineering (TE) solutions, which are computationally costly, in particular for the forwarding nodes. In this paper, we propose SEMPER: a lightweight TE solution based on MP-TCP that, in contrast to other TE solutions, moves the complexity to the endpoints of the connection, and relieves the forwarding elements from complex operations or even maintaining state. As our evaluation shows, SEMPER efficiently makes use of all available paths between the endpoints while maintaining fairness, and properly adapts to variations on the available capacity.

By providing especially tailored instances of a virtual network,network slicing allows for a strong specialization of the offered services on the same shared infrastructure. Network slicing has profound implications on resource management, as it entails an inherent trade-off between: (i) the need for fully dedicated resources to support service customization, and (ii) the dynamic resource sharing among services to increase resource efficiency and cost-effectiveness of the system. In this paper, we provide a first investigation of this trade-off via an empirical study of resource management efficiency in network slicing. Building on substantial measurement data collected in an operational mobile network (i) we quantify the efficiency gap introduced by non-reconfigurable allocation strategies of different kinds of resources, from radio access to the core of the network, and (ii) we quantify the advantages of their dynamic orchestration at different timescales. Our results provide insights on the achievable efficiency of network slicing architectures, their dimensioning, and their interplay with resource management algorithms.

Wireless networked embedded systems are increasingly used for safety-critical applications such as smart production or networked cars, where failures may have severe impact. Therefore, strict dependability requirements have to be met. This is difficult to achieve, however, as these applications often operate in harsh environments or are exposed to attacks. In this talk we present recent research results obtained in the Dependable Things research center at TU Graz which aims at increasing the dependability of the IoT for safety-critical applications. Specifically, we present a single-anchor approach to robust and accurate localization using UWB, a method to analyze software for potential side-channel leakage, and an approach to automatically learn models of protocols used in the IoT in order to formally verify their correct implementation and interoperability. There is consensus among the relevant industry and standardization communities that a key element in 5G mobile networks will be network slicing. The idea is to allow the mobile infrastructure to be “sliced” into logical networks, where each slice is a collection of resources and functions that includes software modules running at different locations as well as the nodes' computational and communication resources. The intention is to tailor each slice to support a specific service, providing only what is necessary for the service while avoiding unnecessary overheads and complexity. This provides a basis for efficient infrastructure sharing among diverse entities, ranging from classical or virtual mobile network operators to new players that simply view connectivity as a service, where each of these entities may be running one or more slices. This talk will focus on the key enablers for network slicing and the research challenges involved in realizing this technology. Current standardization activities will be reviewed along with the contributions of major research projects, such as the H2020 5G-NORMA and 5G-MoNArch projects. A key problem underlying network slicing is enabling efficient sharing of mobile network resources. Various approaches considered in 3GPP will be analysed, ranging from per-reservation based schemes (where network slices reserve the required resources in advance) to others based on network shares (where resources are allocated based on predetermined shares). The performance and behavior of the various approaches will be studied based on analytical tools including optimization, game theory and machine learning. Buildig on these analyses, we will provide some insights on the stability, peformance, optimality and level of customization enabled by the various approaches.

Next-generation wireless architectures are expected to enable slices of shared wireless infrastructure, which are customized to specific mobile operators/services. Given infrastructure costs and the stochastic nature of mobile services' spatial loads, it is highly desirable to achieve efficient statistical multiplexing among such slices. We study a simple dynamic resource sharing policy, which allocates a “share” of a pool of (distributed) resources to each slice-share constrained proportionally fair (SCPF). We give a characterization of SCPF's performance gains over static slicing and general processor sharing. We show that higher gains are obtained when a slice's spatial load is more “imbalanced” than, and/or “orthogonal” to, the aggregate network load, and that the overall gain across slices is positive. We then address the associated dimensioning problem. Under SCPF, traditional network dimensioning translates to a coupled share dimensioning problem, which characterizes the existence of a feasible share allocation, given slices' expected loads and performance requirements. We provide a solution to robust share dimensioning for SCPF-based network slicing. Slices may wish to unilaterally manage their users' performance via admission control, which maximizes their carried loads subject to performance requirements. We show that this can be modeled as a “traffic shaping” game with an achievable Nash equilibrium. Under high loads, the equilibrium is explicitly characterized, as are the gains in the carried load under SCPF versus static slicing. Detailed simulations of a wireless infrastructure supporting multiple slices with heterogeneous mobile loads show the fidelity of our models and the range of validity of our high-load equilibrium analysis.

In a virtualized radio access network (RAN), baseband processing is performed by software running in cloud-computing platforms. However, current protocol stacks were not designed to run in this kind of environment; the high variability on the computational resources consumed by RAN functions may lead to eventual computational outages (where frames are not decoded on time), severely degrading the resulting performance. In this paper, we address this issue by re-designing two key functions of the protocol stack: 1) scheduling, to select the transmission of those frames that do not result in computational outages, and 2) modulation and coding scheme (MCS) selection, to downgrade the selected MCS in case no sufficient computational resources are available. We formulate the resulting problem as a joint optimization and compute the (asymptotically) optimal solution to this problem. We further show that this solution involves solving an NP-hard problem, and propose an algorithm to obtain an approximate solution that is computationally efficient while providing bounded performance over the optimal. We thoroughly evaluate the proposed approach via simulation, showing that it can provide savings as high as 80% of the computational resources while paying a small price in performance.

Technologies that enable network slicing are expected to be a key component of next generation mobile networks. Their promise lies in enabling tenants (such as mobile operators and/or services) to reap the cost and performance benefits of sharing resources while retaining the ability to customize their own allocations. When employing dynamic sharing mechanisms, tenants may exhibit strategic behavior, optimizing their choices in response to those of other tenants. This paper analyzes dynamic sharing in network slicing when tenants support inelastic users with minimum rate requirements. We propose a NEtwork Slicing (NES) framework combining: 1) admission control; 2) resource allocation; and 3) user dropping. We model the network slicing system with admitted users as a NES game; this is a new class of game where the inelastic nature of the traffic may lead to dropping users whose requirements cannot be met. We show that, as long as admission control guarantees that slices can satisfy the rate requirements of all their users, this game possesses a Nash equilibrium. Admission control policies (a conservative and an aggressive one) are considered, along with a resource allocation scheme and a user dropping algorithm, geared at maintaining the system in Nash equilibria. We analyze our NES framework's performance in equilibrium, showing that it achieves the same or better utility than static resource partitioning, and bound the difference between NES and the socially optimal performance. Simulation results confirm the effectiveness of the proposed approach.

We investigate how individual mobile services are consumed at a national scale, by studying data collected in a 3G/4G mobile network deployed over a major European country. Through correlation and clustering analyses, our study unveils a strong heterogeneity in the demand for different mobile services, both in time and space. In particular, we show that: (i) somehow surprisingly, almost all considered services exhibit quite different temporal usage patterns; (ii) in contrast to such temporal behavior, spatial patterns are fairly uniform across all services; (iii) when looking at usage patterns at different locations, the average traffic volume per user is dependent on the urbanization level, yet its temporal dynamics are not. Our findings do not only have sociological implications, but are also relevant to the orchestration of network resources.

In addition to providing substantial performance enhancements, future 5G networks will also change the mobile network ecosystem. Building on the network slicing concept, 5G allows to “slice” the network infrastructure into separate logical networks that may be operated independently and targeted at specific services. This opens the market to new players: the infrastructure provider, which is the owner of the infrastructure, and the tenants, which may acquire a network slice from the infrastructure provider to deliver a specific service to their customers. In this new context, we need new algorithms for the allocation of network resources that consider these new players. In this paper, we address this issue by designing an algorithm for the admission and allocation of network slices requests that (i) maximises the infrastructure provider's revenue and (ii) ensures that the service guarantees provided to tenants are satisfied. Our key contributions include: (i) an analytical model for the admissibility region of a network slicing-capable 5G Network, (ii) the analysis of the system (modelled as a Semi-Markov Decision Process) and the optimisation of the infrastructure provider's revenue, and (iii) the design of an adaptive algorithm (based on Q-learning) that achieves close to optimal performance.

Network slicing to enable resource sharing among multiple tenants-network operators and/or services-is considered a key functionality for next generation mobile networks. This paper provides an analysis of a well-known model for resource sharing, the `share-constrained proportional allocation' mechanism, to realize network slicing. This mechanism enables tenants to reap the performance benefits of sharing, while retaining the ability to customize their own users' allocation. This results in a network slicing game in which each tenant reacts to the user allocations of the other tenants so as to maximize its own utility. We show that, under appropriate conditions, the game associated with such strategic behavior converges to a Nash equilibrium. At the Nash equilibrium, a tenant always achieves the same, or better, performance than under a static partitioning of resources, hence providing the same level of protection as such static partitioning. We further analyze the efficiency and fairness of the resulting allocations, providing tight bounds for the price of anarchy and envy-freeness. Our analysis and extensive simulation results confirm that the mechanism provides a comprehensive practical solution to realize network slicing. Our theoretical results also fill a gap in the literature regarding the analysis of this resource allocation model under strategic players.

Mobile network operators can track subscribers via passive or active monitoring of device locations. The recorded trajectories offer an unprecedented outlook on the activities of large user populations, which enables developing new networking solutions and services, and scaling up studies across research disciplines. Yet, the disclosure of individual trajectories raises significant privacy concerns: thus, these data are often protected by restrictive non-disclosure agreements that limit their availability and impede potential usages. In this paper, we contribute to the development of technical solutions to the problem of privacy-preserving publishing of spatiotemporal trajectories of mobile subscribers. We propose an algorithm that generalizes the data so that they satisfy-anonymity, an original privacy criterion that thwarts attacks on trajectories. Evaluations with real-world datasets demonstrate that our algorithm attains its objective while retaining a substantial level of accuracy in the data. Our work is a step forward in the direction of open, privacy-preserving datasets of spatiotemporal trajectories.

The emerging network slicing paradigm for 5G provides new business opportunities by enabling multi-tenancy support. At the same time, new technical challenges are introduced, as novel resource allocation algorithms are required to accommodate different business models. In particular, infrastructure providers need to implement radically new admission control policies to decide on network slices requests depending on their Service Level Agreements (SLA). When implementing such admission control policies, infrastructure providers may apply forecasting techniques in order to adjust the allocated slice resources so as to optimize the network utilization while meeting network slices' SLAs. This paper focuses on the design of three key network slicing building blocks responsible for (i) traffic analysis and prediction per network slice, (ii) admission control decisions for network slice requests, and (iii) adaptive correction of the forecasted load based on measured deviations. Our results show very substantial potential gains in terms of system utilization as well as a trade-off between conservative forecasting configurations versus more aggressive ones (higher gains, SLA risk).

Next generation wireless architectures are expected to enable slices of shared wireless infrastructure which are customized to specific mobile operators/services. Given infrastructure costs and the stochastic nature of mobile services' spatial loads, it is highly desirable to achieve efficient statistical multiplexing amongst network slices. We study a simple dynamic resource sharing policy which allocates a `share' of a pool of (distributed) resources to each slice-Share Constrained Proportionally Fair (SCPF). We give a characterization of the achievable performance gains over static slicing, showing higher gains when a slice's spatial load is more `imbalanced' than, and/or `orthogonal' to, the aggregate network load. Under SCPF, traditional network dimensioning translates to a coupled share dimensioning problem, addressing the existence of a feasible share allocation given slices' expected loads and performance requirements. We provide a solution to robust share dimensioning for SCPF-based network slicing. Slices may wish to unilaterally manage their users' performance via admission control which maximizes their carried loads subject to performance requirements. We show this can be modeled as a "traffic shaping" game with an achievable Nash equilibrium. Under high loads the equilibrium is explicitly characterized, as are the gains in the carried load under SCPF vs. static slicing. Detailed simulations of a wireless infrastructure supporting multiple slices with heterogeneous mobile loads show the fidelity of our models and range of validity of our high load equilibrium analysis.

This paper addresses the slicing of radio access network resources by multiple tenants, e.g., virtual wireless operators and service providers. We consider a criterion for dynamic resource allocation amongst tenants, based on a weighted proportionally fair objective, which achieves desirable fairness/protection across the network slices of the different tenants and their associated users. Several key properties are established, including: the Pareto-optimality of user association to base stations, the fair allocation of base stations' resources, and the gains resulting from dynamic resource sharing across slices, both in terms of utility gains and capacity savings. We then address algorithmic and practical challenges in realizing the proposed criterion. We show that the objective is NP-hard, making an exact solution impractical, and design a distributed semi-online algorithm, which meets performance guarantees in equilibrium and can be shown to quickly converge to a region around the equilibrium point. Building on this algorithm, we devise a practical approach with limited computational information and handoff overheads. We use detailed simulations to show that our approach is indeed near-optimal and provides substantial gains both to tenants (in terms of capacity savings) and end users (in terms of improved performance).

Power-line communications (PLC) are becoming a key component in home networking, because they provide easy and high-throughput connectivity. The dominant MAC protocol for high data-rate PLC, the IEEE 1901, employs a CSMA/CA mechanism similar to the backoff process of 802.11. Existing performance evaluation studies of this protocol assume that the backoff processes of the stations are independent (the so-called decoupling assumption). However, in contrast to 802.11, 1901 stations can change their state after sensing the medium busy, which is regulated by the so-called deferral counter. This mechanism introduces strong coupling between the stations and, as a result, makes existing analyses inaccurate. In this paper, we propose a performance model for 1901, which does not rely on the decoupling assumption. We prove that our model admits a unique solution for a wide range of configurations and confirm the accuracy of the model using simulations. Our results show that we outperform current models based on the decoupling assumption. In addition to evaluating the performance in steady state, we further study the transient dynamics of 1901, which is also affected by the deferral counter.

Rate adaptation in 802.11 WLANs has received a lot of attention from the research community, with most of the proposals aiming at maximising throughput based on network conditions. Considering energy consumption, an implicit assumption is that optimality in throughput implies optimality in energy efficiency, but this assumption has been recently put into question. In this paper, we address via analysis and experimentation the relation between throughput performance and energy efficiency in multi-rate 802.11 scenarios. We demonstrate the trade-off between these performance figures, confirming that they may not be simultaneously optimised, and analyse their sensitivity towards the energy consumption parameters of the device. Our results provide the means to design novel rate adaptation schemes that takes energy consumption into account.

While substantial effort has been devoted to understand fraudulent activity in traditional online advertising (search and banner), more recent forms such as video ads have received little attention. The understanding and identification of fraudulent activity (i.e., fake views) in video ads for advertisers, is complicated as they rely exclusively on the detection mechanisms deployed by video hosting portals. In this context, the development of independent tools able to monitor and audit the fidelity of these systems are missing today and needed by both industry and regulators.

Wireless mesh networks are being deployed all around the world both to provide ubiquitous connection to the Internet and to carry data generated by several services (video surveillance, smart grids, earthquake early warning systems, etc.). In those cases where fixed power connections are not available, mesh nodes operate by harvesting ambient energy (e.g., solar or wind power) and hence they can count on a limited and time-varying amount of power to accomplish their functions. Since we consider mesh nodes equipped with multiple radios, power savings and network performance can be maximized by properly routing flows, assigning channels to radios and identifying nodes/radios that can be turned off. Thus, the problem we address is a joint channel assignment and routing problem with additional constraints on the node power consumption, which is NP-complete. In this paper, we propose a heuristic, named minimum power channel assignment and routing algorithm (MP-CARA), which is guaranteed to return a local optimum for this problem. Based on a theoretical analysis that we present in the paper, which gives an upper bound on the outage probability as a function of the constraint on power consumption, we can guarantee that the probability that a node runs out of power with MP-CARA falls below a desired threshold. The performance of MP-CARA is assessed by means of an extensive simulation study aiming to compare the solutions returned by MP-CARA to those found by other heuristics proposed in the literature.

Increasing the density of access points is one of the most effective mechanisms to cope with the growing traffic demand in wireless networks. To prevent energy wastage at low loads, a resource-on-demand (RoD) scheme is required to opportunistically (de)activate access points as network traffic varies. While previous publications have analytically modeled these schemes in the past, they have assumed that resources are immediately available when activated, an assumption that leads to inaccurate results and might result in inappropriate configurations of the RoD scheme. In this paper, we analyze a general RoD scenario with N access points and non-zero start-up times. We first present an exact analytical model that accurately predicts performance but has a high computational complexity, and then derive a simplified analysis that sacrifices some accuracy in exchange for a much lower computational cost. To illustrate the practicality of this model, we present the design of a simple configuration algorithm for RoD. Simulation results confirm the validity of the analyses, and the effectiveness of the configuration algorithm.

Offloading traffic through opportunistic communications has been recently proposed as a way to relieve the current overload of cellular networks. Opportunistic communication can occur when mobile device users are (temporarily) in each other's proximity, such that the devices can establish a local peer-to-peer connection (e.g., via WLAN or Bluetooth). Since opportunistic communication is based on the spontaneous mobility of the participants, it is inherently unreliable. This poses a serious challenge to the design of any cellular offloading solutions, that must meet the applications' requirements. In this paper, we address this challenge from an optimization analysis perspective, in contrast to the existing heuristic solutions. We first model the dissemination of content (injected through the cellular interface) in an opportunistic network with heterogeneous node mobility. Then, based on this model, we derive the optimal content injection strategy, which minimizes the load of the cellular network while meeting the applications' constraints. Finally, we propose an adaptive algorithm based on control theory that implements this optimal strategy without requiring any data on the mobility patterns or the mobile nodes' contact rates. The proposed approach is extensively evaluated with both a heterogeneous mobility model as well as real-world contact traces, showing that it substantially outperforms previous approaches proposed in the literature.

Power-line communications are employed in home networking to provide easy and high-throughput connectivity. The IEEE 1901, the MAC protocol for power-line networks, employs a CSMA/CA protocol similar to that of 802.11, but is substantially more complex, which probably explains why little is known about its performance. One of the key differences between the two protocols is that whereas 802.11 only reacts upon collisions, 1901 also reacts upon several consecutive transmissions and thus can potentially achieve better performance by avoiding unnecessary collisions. In this paper, we propose a model for the 1901 MAC. Our analysis reveals that the default configuration of 1901 does not fully exploit its potential and that its performance degrades with the number of stations. Based on analytical reasoning, we derive a configuration for the parameters of 1901 that drastically improves throughput and achieves optimal performance without requiring the knowledge of the number of stations in the network. In contrast, 802.11 requires knowing the number of contending stations to provide a similar performance, which is unfeasible for realistic traffic patterns. We confirm our results and enhancement with testbed measurements, by implementing the 1901 MAC protocol on WiFi hardware.

The 802.11e standard enables user configuration of several MAC parameters, making WLANs vulnerable to users that selfishly configure these parameters to gain throughput. In this paper, we propose a novel distributed algorithm to thwart such selfish behavior. The key idea of the algorithm is for stations to react, upon detecting a misbehavior, by using a more aggressive configuration that penalizes the misbehaving station. We show that the proposed algorithm guarantees global stability while providing good response times. By conducting an analysis of the effectiveness of the algorithm against selfish behaviors, we also show that a misbehaving station cannot obtain any gain by deviating from the algorithm. Simulation results confirm that the proposed algorithm optimizes throughput performance while discouraging selfish behavior. We also present an experimental prototype of the proposed algorithm demonstrating that it can be implemented on commodity hardware.

Opportunistic traffic offloading has been proposed to tackle overload problems in cellular networks. However, existing proposals only address device-to-device-based offloading techniques with deadline-based data propagation, and neglect content injection procedures. In contrast, we tackle the offloading issue from another perspective: the base station interference coordination problem during content injection. In particular, we focus on dissemination of contents, and aim at the minimization of the total transmission time spent by base stations to inject the contents into the network. We leverage the almost blank sub-frame technique to keep under control the intercell interference in such a process. We formulate an optimization problem, prove that it is NP-hard and NP-complete, and propose a near-optimal heuristic to solve it. Our algorithm substantially outperforms classical intercell interference approaches, as we evaluate through the simulation of LTE-A networks.

It has recently been shown that "flexible channelization", whereby wireless stations adapt their spectrum bands on a per-frame basis, is feasible in practice. In this paper, we propose TF-CSMA/CA, an algorithm for flexible channelization that schedules packets in time and frequency domains. TF-CSMA/CA is a simple extension of the CSMA/CA protocol used by IEEE 802.11. Contrary to existing channelization schemes, it is entirely distributed and it reacts only to packet collisions, successful transmissions and carrier sensing. With TF-CSMA/CA, when a station is involved in a collision, it performs backoff in both time and frequency domains. Backing off also in the frequency domain allows the transmitters to be much more efficient and aggressive in the time domain, which significantly reduces the severe overheads present with recent 802.11 PHY layers. The main challenge, however, is that the stations need some level of self-organization in order to find spectrum bands of variable widths that minimize interference, while still efficiently using the available spectrum. Using analysis and simulations, we show that such an extension of CSMA/CA to the frequency domain drastically improves both throughput and fairness. Notably, it enables the stations to find interference-free spectrum bands of appropriate size using no communication -- relying only on collisions and successes as implicit signals.

Inter-Cell Interference Coordination (ICIC) has been identified for LTE as the main instrument for interference control. With ICIC, quality requirements can be guaranteed while avoiding the complexity of coordinated baseband processing approaches. However, most ICIC schemes proposed so far rely on centralized multi-cell scheduling algorithms that involve very heavy signaling overhead and, as a result, cannot be used for dense cellular layouts. In this paper, we propose H2(IC)2, a novel ICIC scheme that, in contrast to previous approaches, incurs very low overhead and is practical for dense deployments. H2(IC)2is based on the Almost Blank SubFrame (ABSF) approach specified by 3GPP, which controls interference by avoiding data transmission in some subframes. Our scheme follows a two-tier approach, consisting of (i) the local schedulers, which perform the scheduling decisions locally and compute ABSF patterns, and (ii) a central coordinator, which supervises ABSF decisions. As a result of such a two-tier design, the scheme requires very light signaling to drive the local schedulers to globally efficient operating points. We analyze the convergence of distributed ABSF/scheduling decisions by using game theoretical tools and show that H2(IC)2performs fairly close to the benchmark provided by a centralized omniscient scheduler.

This paper proposes a conceptually novel, adaptive and future-proof 5G mobile network architecture. The proposed architecture enables unprecedented levels of network customisability, ensuring stringent performance, security, cost and energy requirements to be met; as well as providing an API-driven architectural openness, fuelling economic growth through over-the-top innovation. Not following the 'one system fits all services' paradigm of current architectures, the architecture allows for adapting the mechanisms executed for a given service to the specific service requirements, resulting in a novel service- and context-dependent adaptation of network functions paradigm. The technical approach is based on the innovative concept of adaptive (de)composition and allocation of mobile network functions, which flexibly decomposes the mobile network functions and places the resulting functions in the most appropriate location. By doing so, access and core functions no longer (necessarily) reside in different locations, which is exploited to jointly optimize their operation when possible. The adaptability of the architecture is further strengthened by the innovative software-defined mobile network control and mobile multi-tenancy concepts.

The traditional model of single ownership of the mobile network infrastructure is being challenged by the forecasted mobile data tsunami and the resulting CAPEX and OPEX costs. In this context, the sharing of network infrastructure among operators has emerged as a way to ensure operators' future cost competitiveness. While the elementary concepts related to passive network sharing are already being exploited today, active network sharing is raising in importance to enable further reduction of network expenses in a substantial and sustainable way. The work presented in this paper addresses this challenge by designing a RAN Multi- tenant cell Slicing Controller (RMSC) that allows to flexibly share the RAN resources among multiple virtual operators (tenants). Three different possible designs for the RMSC controller are proposed, ranging from a fully distributed system with no inter-base station communication to a fully centralized solution with all information available. The proposed solutions are benchmarked against a distributed static slicing solution and a centralized load balancing solution, considering realistic scenarios of uneven user location distribution per tenant. The performance results obtained indicate that the proposed RMSC schemes can significantly outperform traditional solutions for multi-tenant mobile networks.

This paper focuses on the practical implementation of a Cloud-RAN architecture in the context of future 5G systems, with particular emphasis on different aspects of the functional split between the cloud platform and the radio access points. First, we provide a comprehensive overview of implementation aspects and how different hardware options impact the implementation of RAN functionality. We further discuss a virtualized infrastructure which may have a significant impact on how algorithms are implemented, how they interact with each other, and how they can be scaled within the RAN. We also analyze implementation constraints to be considered to provide backwards compatibility with 3GPP LTE; such constraints on the computing platforms result from the RAN requirements in terms of latency and throughput. Finally, the level of flexibility achievable by the proposed architecture is described from a practical point of view.

This paper provides an in-depth understanding of the per-frame energy consumption behavior in 802.11 wireless LAN devices. Extensive measurements are performed for seven devices of different types (wireless routers, smartphones, tablets, and embedded devices) and for both UDP and TCP traffic. Experimental results unveil that a substantial fraction of energy consumption, hereafter descriptively named cross-factor, may be ascribed to each individual frame while it crosses the protocol stack (OS, driver, NIC) and is independent of the frame size. Our findings, summarized in a convenient energy consumption model, contrast traditional models that (implicitly) amortize such energy cost component in a fixed baseline cost or in a toll proportional to the frame size and raise the alert that, in some cases, conclusions drawn using traditional energy models may be fallacious.

Multilayer video streaming allows to provide different video qualities to a group of multicast receivers with heterogeneous receive rates. The number of layers received (and thus the receive rate) determines the quality of the decoded video stream. For such layered multicast streaming, network coding provides higher capacity than multicast routing. Network coding can be performed within a layer or across layers, and in general, interlayer coding outperforms intralayer coding. An optimal solution to a network-coded layered multicast problem may require decoding of the network code at interior nodes to extract information to be forwarded. However, decoding consumes resources and introduces delay, which is particularly undesirable at interior nodes (the routers) of the network. In this paper, we thus focus on the interlayer network coding problem without decoding at interior nodes. We show that the problem is NP-hard and propose a heuristic algorithm for rate allocation and coding based on the Edmonds-Karp maximum flow algorithm. We prove that our algorithm ensures decodability of the information received and provides some fairness properties. Finally, we perform extensive simulations and show that our algorithm may even outperform other heuristics that do require decoding at interior nodes.

Distributed opportunistic scheduling (DOS) techniques have been recently proposed for improving the throughput performance of wireless networks. With DOS, each station contends for the channel with a certain access probability. If a contention is successful, the station measures the channel conditions and transmits in case the channel quality is above a certain threshold. Otherwise, the station does not use the transmission opportunity, allowing all stations to recontend. A key challenge with DOS is to design a distributed algorithm that optimally adjusts the access probability and the threshold of each station. To address this challenge, in this paper, we first compute the configuration of these two parameters that jointly optimizes throughput performance in terms of proportional fairness. Then, we propose an adaptive algorithm based on control theory that converges to the desired point of operation. Finally, we conduct a control theoretic analysis of the algorithm to find a setting for its parameters that provides a good tradeoff between stability and speed of convergence. Simulation results validate the design of our mechanism and confirm its advantages over previous works.

Power-line communications are employed in home networking to provide easy and high-throughput connectivity. IEEE 1901, the MAC protocol for power-line networks, employs a CSMA/CA protocol similar to that of 802.11, but is substantially more complex, which probably explains why little is known about its performance. One of the key differences between the two protocols is that whereas 802.11 only reacts upon collisions, 1901 also reacts upon several consecutive transmissions and thus can potentially achieve better performance by avoiding unnecessary collisions.

Power-line communications are becoming a key component in home networking. The dominant MAC protocol for high data-rate power-line communications, IEEE 1901, employs a CSMA/CA mechanism similar to the back off process of 802.11. Existing performance evaluation studies of this protocol assume that the back off processes of the stations are independent (the so-called decoupling assumption). However, in contrast to 802.11, 1901 stations can change their state after sensing the medium busy, which introduces strong coupling between the stations and, as a result, makes existing analyses inaccurate. In this paper, we propose a new performance model for 1901, which does not rely on the decoupling assumption. We prove that our model admits a unique solution. We confirm the accuracy of our model using both test bed experiments and simulations, and we show that it surpasses current models based on the decoupling assumption. Furthermore, we study the trade off between delay and throughput existing with 1901. We show that this protocol can be configured to accommodate different throughput and jitter requirements, and we give systematic guidelines for its configuration.

Opportunistic traffic offloading has been proposed to tackle overload problems in cellular networks. However, they only address the problem of deadline-based content propagation in the cellular system, given wireless environment characterization. In contrast, we cope with the traffic offloading issue from another perspective: the base station interference coordination problem. In particular, we aim at the minimization of the total transmission time spent by the base stations in order to inject contents into the network, and we leverage the recently proposed ABSF technique to keep under control intercell interference. We formulate an optimization problem, prove that it is NP-Complete, and propose a near-optimal heuristic. Our proposed algorithm substantially outperforms classical intercell interference approaches proposed in the literature, as we evaluate through the simulation of dense LTE-A network scenarios.

We investigate the IEEE 1901 MAC protocol, the dominant protocol for high data rate power-line communications. 1901 employs a CSMA/CA mechanism similar to - but much more complex than - the backoff mechanism of 802.11. Because of this extra complexity, and although this mechanism is the only widely used MAC layer for power-line networks, there are few analytical results on its performance. We propose a model for the 1901 MAC that comes in the form of a single fixed-point equation for the collision probability. We prove that this equation admits a unique solution, and we evaluate the accuracy of our model by using simulations.

Handling voice traffic in existing WLANs is extremely inefficient, due to the large overhead of the protocol operation as well as the time spent in contention. In this paper, we propose a simple scheme (VoIPiggy) to improve the efficiency of WLANs with voice traffic. The key idea of the mechanism is to piggyback voice frames onto the MAC layer acknowledgments, which reduces both the frame overhead and the time wasted in contention. To quantify the gains of our proposal, we first study its performance by means of a capacity and delay analysis of a WLAN operating under the VoIPiggy mechanism. Then, we present an implementation of the mechanism using commercial off-the-shelf devices, which involves programming at the driver and firmware levels. The performance of the proposed scheme is evaluated in a large-scale testbed consisting of 30 devices. Our extensive measurements, which are comprised of different network conditions in terms of number of active nodes, traffic load and transmission rates, confirm that the experimental results match the analytical ones, and show a dramatic performance improvement for both “voice only” and “voice and data” scenarios.

In this paper, we propose to provide throughput guarantees in heterogeneous wireless mesh networks by jointly optimizing routing and Medium Access Control configuration. Our solution is based on the notion of linearized capacity region, which provides a technology-independent way of representing the capacity of a wireless link thereby hiding the technology specifics to the upper layers. From the available capacity of the underlying links as given by the linearized capacity region, we propose two routing algorithms based on multipath and single path that find optimal paths for all the flows in the network given their throughput requirements. The throughput allocation resulting from routing is then provided to each link, which uses this information to optimize its technology-specific Medium Access Control parameters. The proposed approach is evaluated in a heterogeneous scenario comprising Wireless Local Area Networks WLAN and Worldwide Interoperability for Microwave Access WiMAX technologies and is shown to outperform previous solutions by at least a factor of 2.Copyright © 2012 John Wiley & Sons, Ltd.

Video traffic is foreseen to account for the majority of the Internet traffic in the near future. While the demand of video transmission keeps growing, the vast majority of wireless equipment deployed in the home environment, based on IEEE 802.11, cannot satisfy the amount of bandwidth that the video applications require. In order to cope with the increasing demand of multimedia traffic, the IEEE 802.11aa Task Group has recently standardized new mechanisms to allow efficient and robust transmission of multicast flows in Wireless LAN. However, the standard leaves open the choice of which one to use for a given scenario. In this paper, we explore the new mechanisms introduced by the 802.11aa Task Group, providing insights of the new choices for handling group addressed frames, by carrying out extensive simulations. Our results highlight the various trade-offs each mechanism has in terms of robustness, resource consumption and complexity, and provide a set of recommended guidelines for their use.

The increasing demand for higher data rates in cellular network results in increasing network density. As a consequence, inter-cell interference is becoming the most serious obstacle towards spectral efficiency. Therefore, considering that radio resources are limited and expensive, new techniques are required for efficient radio resource allocation in next generation cellular networks. In this paper, we propose a pure frequency reuse 1 scheme based on base station scheduling rather than the commonly adopted user scheduling. In particular, we formulate a base station scheduling problem to determine which base stations can be scheduled to simultaneously transmit, without causing excessive interference to any user of any of the scheduled base stations. We show that finding the optimal base station scheduling is NP-hard, and formulate the BASICS (BAse Station Inter-Cell Scheduling) algorithm, a novel heuristic to approximate the optimal solution at low complexity cost. The proposed algorithm is in line with the ABSF (almost blank sub-frame) technique recently standardized at the 3GPP. By means of numerical and packet-level simulations, we prove the effectiveness and superiority of BASICS as compared to the state of the art of inter-cell interference mitigation schemes.

Distributed opportunistic scheduling (DOS) is inherently more difficult than conventional opportunistic scheduling due to the absence of a central entity that knows the channel state of all stations. With DOS, stations use random access to contend for the channel and, upon winning a contention, they measure the channel conditions. After measuring the channel conditions, a station only transmits if the channel quality is good; otherwise, it gives up the transmission opportunity. The distributed nature of DOS makes it vulnerable to selfish users: By deviating from the protocol and using more transmission opportunities, a selfish user can gain a greater share of wireless resources at the expense of “well-behaved” users. In this paper, we address the problem of selfishness in DOS from a game-theoretic standpoint. We propose an algorithm that satisfies the following properties: 1) When all stations implement the algorithm, the wireless network is driven to the optimal point of operation; and 2) one or more selfish stations cannot obtain any gain by deviating from the algorithm. The key idea of the algorithm is to react to a selfish station by using a more aggressive configuration that (indirectly) punishes this station. We build on multivariable control theory to design a mechanism for punishment that is sufficiently severe to prevent selfish behavior, yet not so severe as to render the system unstable. We conduct a game-theoretic analysis based on repeated games to show the algorithm's effectiveness against selfish stations. These results are confirmed by extensive simulations.

A thorough understanding of the power consumption behavior of real world wireless devices is of paramount importance to ground energy-efficient protocols and optimizations on realistic and accurate energy models. This paper provides an in-depth experimental investigation of the per-frame energy consumption components in 802.11 Wireless LAN devices. To the best of our knowledge, our measurements are the first to unveil that a substantial fraction of energy consumption, hereafter descriptively named cross-factor, may be ascribed to each individual frame while it crosses the protocol/implementation stack (OS, driver, NIC). Our findings, summarized in a convenient new energy consumption model, contrast traditional models which either neglect or amortize such energy cost component in a fixed baseline cost, and raise the alert that, in some cases, conclusions drawn using traditional energy models may be fallacious.

Distributed Opportunistic Scheduling (DOS) techniques have been recently proposed to improve the throughput performance of wireless networks. With DOS, each station contends for the channel with a certain access probability. If a contention is successful, the station measures the channel conditions and transmits in case the channel quality is above a certain threshold. Otherwise, the station does not use the transmission opportunity, allowing all stations to recontend. A key challenge with DOS is to design a distributed algorithm that optimally adjusts the access probability and the threshold of each station. To address this challenge, in this paper we first compute the configuration of these two parameters that jointly optimizes throughput performance in terms of proportional fairness. Then, we propose an adaptive algorithm based on control theory that converges to the desired point of operation. Finally, we conduct a control theoretic analysis of the algorithm to find a setting for its parameters that provides a good tradeoff between stability and speed of convergence. Simulation results validate the design of the proposed algorithm and confirm its advantages over previous proposals.

Supporting voice traffic in existing WLANs results extremely inefficient, given the large overheads of the protocol operation and the need to prioritize this traffic over, e.g., bulky transfers. In this paper we propose a simple scheme to improve the efficiency of WLANs when voice traffic is present. The mechanism is based on piggybacking voice frames over the acknowledgments, which reduces both frame overheads and time spent in contentions. We evaluate its performance in a large-scale testbed consisting on 33 commercial off-the-shelf devices. The experimental results show dramatic performance improvements in both voice-only and mixed voice-and-data scenarios.

Multi-layer video streaming allows to provide different video qualities to a group of multicast receivers with heterogeneous receive rates. The number of layers received determines the quality of the decoded video stream. For such layered multicast streaming, network coding provides higher capacity than multicast routing. Network coding can be performed within a layer (intra-layer) or across layers (inter-layer), and in general inter-layer coding outperforms intra-layer coding. An optimal solution to a network coded layered multicast problem may require decoding of the network code at interior nodes to extract information to be forwarded. However, decoding consumes resources and introduces delay, which is particularly undesirable at interior nodes (the routers) of the network. In this paper, we thus focus on the inter-layer network coding problem without decoding at interior nodes. We propose a heuristic algorithm for rate allocation and code assignment based on the Edmonds-Karp maximum flow algorithm and perform simulations that show that our algorithm may even outperform other heuristics that do require decoding at interior nodes.

It is our great pleasure to welcome you to the Thirteenth IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks, WoWMoM 2012. Over the years, WoWMoM has emerged to be a flagship forum that brings together researchers from academia, industry, and government laboratories who are involved in various aspects of mobile multimedia networking technologies, ranging from communications platforms to services and applications.

The EDCA mechanism of the IEEE 802.11 standard has been designed to support, among others, video traffic. This mechanism relies on a number of parameters whose configuration is left open by the standard. Although there are some recommended values for these parameters, they are fixed independent of the WLAN conditions, which results in suboptimal performance. Following this observation, a number of approaches in the literature have been devised to set the EDCA parameters based on an estimation of the WLAN conditions. However, these previous approaches are based on heuristics and hence do not guarantee optimized performance. In this article we propose a novel algorithm to adjust the EDCA parameters to carry video traffic which, in contrast to previous approaches, is sustained on mathematical foundations that guarantee optimal performance. In particular, our approach builds upon (i) an analytical model of the WLAN performance under video traffic, used to derive the optimal point of operation of EDCA, and (ii) a control theoretic designed mechanism which drives the WLAN to this point of operation. Via extensive simulations, we show that the proposed approach performs optimally and substantially outperforms the standard recommended configuration as well as previous adaptive proposals.

It is our pleasure to introduce the WoWMoM 2011's workshops program. The Workshops associated with WoWMoM feature a large range of topics beyond the main conference, to better cover and stimulate discussion on the emerging research areas in the exciting field of wireless, mobile and multimedia networks. As the integral part of the main conference, the purpose of the workshops is to provide a less formal and more focused forum where novel and stimulating ideas and experiences are shared among researchers both from industry and academia, on all the topics of interest for the wireless, mobile and multimedia networking community. The number and quality of the workshops contribute to the relevance of WoWMoM as one of the reference forums for the research community working on these topics.

In this paper we investigate the case of IEEE 802.11-based WLANs and first show that, given the existing diversity of power consumption figures among mobile devices, performing a fair allocation of resources among devices is challenging. We then propose a criterion to objectively balance between the most energy-efficient configuration (where all resources are given to the single most energy efficient device) and the throughput-optimal allocation (where all devices evenly share the resources regardless of their power consumption). We derive a closed-form expression for the optimal configuration of the WLANs with respect to the energy-efficiency criterion. We validate our analysis through simulations, and show that our approach betters the prevalent allocation schemes discussed in literature in terms of energy efficiency, while maintaining the notion of fairness among competing devices.

The optimal configuration of the contention parameters of a WLAN depends on the network conditions in terms of number of stations and the traffic they generate. Following this observation, a considerable effort in the literature has been devoted to the design of distributed algorithms that optimally configure the WLAN parameters based on current conditions. In this paper, we propose a novel algorithm that, in contrast to previous proposals which are mostly based on heuristics, is sustained by mathematical foundations from multivariable control theory. A key advantage of the algorithm over existing approaches is that it is compliant with the 802.11 standard and can be implemented with current wireless cards without introducing any changes into the hardware or firmware. We study the performance of our proposal by means of theoretical analysis, simulations, and a real implementation. Results show that the algorithm substantially outperforms previous approaches in terms of throughput and delay.

Video transmission is expected to be the next big thing in personal communication systems. While text messaging applications have already experienced an explosive growth, the exchange of multimedia content is still lacking architectural solutions which enable it to become the next killer application. The EU project MEDIEVAL (MultimEDia transport for mobIlE Video AppLications) aims at filling this gap. In this paper we describe the approaches envisioned by the project for what concerns the data link layer, with special emphasis on wireless access and its related cross-layer issues. After presenting some general project aspects, we will review the state of the art and enumerate several open issues, concerning the identification of the most suitable radio technologies for video support, as well as their integration and required enhancements to enable efficient video transport.

Although the EDCA access mechanism of the 802.11e standard supports legacy DCF stations, the presence of DCF stations in the WLAN jeopardizes the provisioning of the service guarantees committed to the EDCA stations. The reason is that DCF stations compete with Contention Windows (CWs) that are predefined and cannot be modified, and as a result, the impact of the DCF stations on the service received by the EDCA stations cannot be controlled. In this paper, we address the problem of providing throughput guarantees to EDCA stations in a WLAN in which EDCA and DCF stations coexist. To this aim, we propose a technique that, implemented at the Access Point (AP), mitigates the impact of DCF stations on EDCA by skipping with a certain probability the Ack reply to a frame from a DCF station. When missing the Ack, the DCF station increases its CW, and thus, our technique allows us to have some control over the CWs of the legacy DCF stations. In our approach, the probability of skipping an Ack frame is dynamically adjusted by means of an adaptive algorithm. This algorithm is based on a widely used controller from classical control theory, namely a Proportional Controller. In order to find an adequate configuration of the controller, we conduct a control-theoretic analysis of the system. Simulation results show that the proposed approach is effective in providing throughput guarantees to EDCA stations in presence of DCF stations.

WLAN devices based on CSMA/CA access schemes have become a fundamental component of network deployments. In such wireless scenarios, traditional networking applications, tools, and protocols, with their built-in measurement techniques, are usually run unchanged. However, their actual interaction with the dynamics of underlying wireless systems is not yet fully understood. A relevant example of such built-in techniques is bandwidth measurement. When considering WLAN environments, various preliminary studies have shown that the application of results obtained in wired setups is not straightforward. Indeed, the contention for medium sharing among multiple users inherent to CSMA/CA access schemes has remarkable consequences on the behavior of and results obtained by bandwidth measurement techniques. In this paper, we focus on evaluating the effect of CSMA/CA-based contention on active bandwidth measurement techniques. As a result, it presents the rate response curve in steady state of a system with both FIFO and CSMA/CA-based contending cross-traffic. We also find out that the distribution of access delay shows a transient regime before reaching a stationary state. The duration of such transient regime is characterized and bounded. We also show how dispersion-based measurements that use a short number of probing packets are biased measurements of the achievable throughput, the origin of this bias lying on the transient detected in the access delay of probing packets. Overall, the results presented in this paper have several consequences that are expected to influence the design of bandwidth measurement tools as well as to better understand the results obtained with them in CSMA/CA links.

In Peer-to-Peer (P2P) systems where stored objects are small, routing dominates the cost of publishing and retrieving an object. In such systems, the issue of fairly balancing the routing load among all nodes becomes critical. In this paper we address this issue for Chord-based P2P systems. We first present an analytical model to evaluate the routing fairness of Chord based on the well accepted Jain's Fairness Index (FI). Our model shows that Chord performs poorly, with a FI around 0.6, mainly due to the different sizes of the zones between nodes. Following this observation, we propose a simple enhancement to the Chord finger selection algorithm with the goal of mitigating this effect. The key advantage of our proposal as compared to previous approaches is that it does not add any overhead to the basic Chord algorithm. The proposed approach is evaluated analytically showing a very substantial improvement over Chord, with a FI around 0.9. We conduct an extensive large-scale simulation study to evaluate our proposal and validate the analysis. The simulation study includes, among other aspects, churn conditions, heterogeneous nodes and Zipf-like object popularity.

When there is no wired connectivity, wireless mesh networks (WMNs) can provide Internet access with lower cost and greater flexibility than traditional approaches. This has motivated the design of new protocols and algorithms for WMNs, and recently the deployment of experimental prototypes. In this paper we add to these previous works with the performance evaluation of a first CARMEN deployment, with the following distinguishing features: i) it is an indoor deployment, ii) it is used by real users to connect to the Internet, and iii) it is built using off-the-shelf hardware. The results show that mesh technology can provide users with a satisfactory Internet experience, and motives further research along this line.

Current Internet use is evolving, users are becoming mobile and are expecting data services on the go. This fact presents big challenges and opportunities to operators, which see the increase in data services as a big market still to be exploited. However, current cellular technologies cannot accommodate the demand that will arise when the true Mobile Internet evolves. Addressing these challenges, we present the CARMEN project, the vision of which is to extend operators’ infrastructure by providing carrier grade services through a heterogeneous wireless mesh. The CARMEN architecture will provide enough bandwidth to cope with users’ expectations at a reduced cost, thereby generating major benefits to operators and users.

In this paper we first present a model to analyze the average and standard deviation of the delay in a 802.11e EDCA WLAN under voice traffic. Next, based on this model, we compute the optimal configuration of the EDCA parameters. Specifically, with our optimal configuration we aim at admitting as many voice calls as possible while meeting a given quality criterion for the average delay and its typical deviation. Simulation results confirm the effectiveness of our optimal configuration.

The distribution of the delay is a very important measure to determine the performance of a WLAN. Indeed, real-time applications typically require that their packets reach their destination within a certain delay with a given probability, and to guarantee this it is necessary to perform the computation of the delay distribution. In this paper we develop a novel method to compute the delay distribution of IEEE 802.11 distributed coordination function (DCF) for a WLAN where the stations transmit packets of variable length. We develop an analytical model that applies to basic, RTS/CTS, and hybrid (coexistence of basic and RTS/CTS) access modes. A simulation model is used to validate the analysis.

A simple and accurate packet delay distribution analysis is presented for the RTS/CTS access scheme of the IEEE 802.11 distributed coordination function (DCF). The packet delay distribution is effectively studied by developing an analytical model that calculates the probability that a packet is successfully transmitted after experiencing a delay time equal to a given value. The model uses simple mathematical relations and provides accurate delay distribution curves. The accuracy of the analytical model is verified by simulations

A simple, effective and accurate packet delay distribution analysis is presented for the IEEE 802.11 distributed coordination function (DCF). The packet delay distribution is effectively studied by developing an analytical model that calculates the probability that a packet is successfully transmitted after experiencing a delay time equal to a given value. The model uses simple mathematical relations and provides fast and accurate delay distribution curves. The accuracy of the analytical model is verified by simulations

Abstract — To this date, many works have been conducted to study the throughput and delay performance of WLANs under saturated conditions and to obtain the configuration that provides optimal performance under these conditions. From these previous works, however, it remains unclear whether this configuration is also appropriate for a WLAN operating under nonsaturation conditions. In this paper we present solid argu-ments which demonstrate that the optimal configuration resulting from saturation is also appropriate for a WLAN operating under nonsaturation conditions. Specifically, we show (via analysis and simulation) that a WLAN configured differently suffers from a number of performance anomalies when operating with finite sending rates. This is an important result for the configuration of WLAN parameters. I.

The service quality experienced by a user with the EDCA mechanism of the upcoming 802.11e standard depends on a number of configurable parameters, namely CWmin, CWmax, AIFS and TXOP_limit. WLAN stations are supposed to use the parameter configurations that the access point (AP) of the WLAN distributes with the beacon frames. However, a user can maliciously configure the parameters of his WLAN station in order to obtain a better service. In this paper, we address the issue of detecting malicious parameter configurations of EDCA. The AIFS and TXOP_limit parameters are relatively easy to control because of their deterministic nature. Given the capture effect, the CWmaxparameter may be unused by some users. Therefore, the key challenge lies in detecting if the CWminparameter of a station is well configured. The main contribution of this paper is the proposal of an algorithm to detect malicious CWminconfigurations. We show that, for an optimally configured WLAN, our algorithm is effective in avoiding that a user can substantially benefit from maliciously configuring his WLAN station without being detected

The substitution of ATM transport by IP in future UMTS Radio Access Networks (UTRAN) introduces several performance challenges that need to be addressed to guarantee the feasibility of its deployment. The significant increase of the overhead requires of header compression and multiplexing methods to achieve a usage of the UTRAN resources similar to the ATM one. Additionally, the specific UTRAN transport needs require the adaptation of standard packet scheduling mechanisms to efficiently use the network resources while providing the required QoS. Our results show that, applying header compression plus multiplexing techniques and taking into account the specific UTRAN synchronization requirements for QoS scheduling, very significant performance improvements can be obtained.

With DiffServ assured forwarding, a customer contracts a certain bandwidth, which is shared among the flows sent by him. If the customer sends too many flows, the realized throughput for each will be very low, and the corresponding transfer is very likely to be interrupted by its user due to its prolonged completion time. Since the work done in transferring an incomplete document is generally wasted, interrupted transfers constitute a significant source of inefficiency in the utilization of the contracted bandwidth. In order to avoid this inefficiency, we propose the use of admission control to limit the number of flows of a customer to ensure that each receives a minimal acceptable throughput.

With weighted fair queuing, the link's bandwidth is distributed among competing flows proportionally to their weights. In this paper we propose an extension of the DCF function of IEEE 802.11 to provide weighted fair queuing in wireless LAN. Simulation results show that the proposed scheme is able to provide the desired bandwidth distribution independent of the flows' aggressiveness and their willingness to transmit. Backwards compatibility is provided such that legacy IEEE 802.11 terminals receive a bandwidth corresponding to the default weight.

In the context of active queue management, intelligent dropping algorithms have been proposed to achieve a better link utilization with TCP. We apply this research to the problem of achieving a better utilization of a customer's contracted throughput when it is sending a TCP traffic aggregate. We propose a scheme, random early marking (REM), that improves the throughput of a TCP aggregate by early marking some packets as out. The proper configuration of REM has been analyzed from a control theoretical standpoint. Simulation results show that REM leads to a significant improvement in a wide range of environments.

User fairness aims at fairly distributing the network resources among users, where a user is an entity that can possibly send different flows through different paths. In this paper we first propose a criterion for user fairness based on political economics fairness theory: the user maxmin fairness criterion. Then we propose an architecture, user fair queuing (UFQ), that provides user maxmin fairness without keeping per-user state in the core nodes. UFQ requires neither admission control nor signaling. We present simulations and analysis on the performance of the proposed architecture.

In this paper, we propose ARME (Assured Rate MAC Extension), an extension of the IEEE 802.11 MAC protocol to provide throughput guarantees. The proposed extension relies on the distributed coordination function (DCF) with a modified algorithm for the computation of the contention window (CW). Best effort service (with no throughput guarantee) is supported by the functionality of the current 802.11 standard in such a way that legacy IEEE 802.11 terminals behave as best effort terminals in ARME. The performance of the proposed extension has been extensively evaluated through simulation; simulation results show that IEEE 802.11 devices using ARME behave well for different types of traffic and different source rates.

Active networks allow protocol processing code to be loaded dynamically into network nodes at run-time. This code can perform tasks specific to a stream of packets or even a single packet. In this paper we compare two active network architectures: the active node transfer system (ANTS) and the messenger system (M0). We have implemented a robust audio multicast protocol and a layered video multicast protocol with both active network systems. We discuss the differences of the two systems, evaluate architectural strengths and weaknesses, compare the runtime performance, and report practical experience and lessons learned.