Recent advancements in quantum computing have demonstrated significant potential for solving combinatorial optimization problems, like the quadratic knapsack problem, a constrained binary optimization problem. However, current quantum and quantum-inspired algorithms often require transforming these constrained problems into an unconstrained form, known as Quadratic Unconstrained Binary Optimization (QUBO). Such transformations can significantly impact the algorithms’ speed and efficiency. In this study, we evaluate five existing transformation methods and propose four novel approaches. We assess all nine methods using Simulated Annealing and find that three of our approaches outperform existing methods in terms of execution time and the quality and quantity of feasible solutions found. Additionally, we tested these transformations on quantum annealers, which were unable to solve even small problem instances, due to limitations in connectivity and error rates. However, our results highlight the advantages of the new approaches, which reduce the total number of variables in the QUBO representation. This is a critical factor for enhanced performance on emerging quantum hardware, since it also reduces the required number of qubits and the embedding chain lengths.

This paper studies the performance of Floating Gossip, a novel decentralized approach for Gossip Learning at the network edge. Floating Gossip utilizes Floating Content to facilitate location-based probabilistic evolution of Machine Learning models, without external infrastructure support. We investigate dynamic scenarios requiring continuous learning, leveraging a mean field approach to analyze Floating Gossip's performance boundaries. Our focus is on the quantity of data that users can integrate into their models, as a function of key system parameters. Unlike previous studies that separately optimize communication or computational aspects of Gossip Learning, our methodology considers their combined effect. We validate our analysis through comprehensive simulations, demonstrating the high accuracy of our analytical model. Our methodology reveals Floating Gossip's effectiveness in training and updating Machine Learning models collaboratively, leveraging opportunistic exchanges between mobile users, while flexibly adapting to different user characteristics and mobility patterns. This research highlights Floating Gossip's potential for continuous, cooperative model training in dynamic, infrastructure-less environments, offering insight into its performance patterns and its potential in practical applications.

In this paper, we consider a relay-enabled wireless network and optimize the weighted proportional fair (wPF) bandwidth allocation by means of exact algorithms with linear complexity on the number of users and relays. Complex architectures typical of relay-enabled systems pose the need of developing efficient techniques that account for the intertwined nature of all the network agents, as resources must be split not only between users, but also between relays and relay-served users, altogether constrained by backhaul capacities and the traffic bottleneck present at the wired base stations. Here, traditional schemes for bandwidth allocation cannot be applied, as resources from one point of the network cannot be allocated regardless the allocation performed at other entities of the same network. Hence, we develop a compact weighted proportional fair resource management with very lightweight complexity, able to jointly allocate access and backhaul resources optimally in real time. We benchmark the results on network capacity and fairness with state-of-art proposals and show that the wPF exact algorithms proposed yield the best trade-off between capacity and fairness in relay networks.

We investigate the minimization of the age of information (AoI) of an AI-powered application that requires timely processing of data generated by a multitude of users. We consider that sequences of inference tasks generated at individual terminals can either be processed locally with a tiny machine learning (ML) model or be offloaded to a more powerful ML model residing on an edge computing facility shared by all users. Since the local ML model is less powerful, its inferences may have low confidence. When this happens, the user is forced to repeat the inference with the more powerful edge ML model. The choice between local processing or offloading follows a randomized-alpha policy, where the local ML model, while less powerful, offers the advantage to alleviate congestion of the edge server. The AoI model follows the frameworks presented in the literature for multiple sources sharing the same queue. Local processing instead works as a single-server dedicated queue, but we account for the imperfections of the tiny ML model by including a failure probability in the local server. Tasks that are processed locally but eventually fail to achieve a minimum confidence level are offloaded to the edge server, resulting in a longer overall processing time. We derive a queueing model of the entire system based on some bounds from the literature. Our results show the trade-offs between processing latency, inference accuracy, and system congestion, highlighting the importance of optimizing task allocation strategies.

Services rely more and more on cloud platforms to offer their products to end users. This implies that being able to estimate the latency required to reach those cloud platforms is of growing importance. To shed light on this crucial aspect, we perform a three-month measurement campaign involving traceroute measurements every 30 minutes over 256 pairs of source-destination probes, where the vantage points are located in different Cloud Service Providers (CSPs) and the destination probes belong to one of the main Infrastructure Operators (IOs) of Spain. We provide interesting insights obtained from analyzing the data resulting from this campaign. Among them, we observe that, as expected, distance is the unavoidable factor impacting cloud latency. Yet, other results are less anticipated, such as the great stability of the network, or the lack of performance difference when comparing standard and premium network service tiers. We also analyze the potential of forecasting the cloud latency both for future samples but also for unobserved connections.

In this paper we investigate the limit performance of Floating Gossip, a new, fully distributed Gossip Learning scheme which relies on Floating Content to implement location-based probabilistic evolution of machine learning models in an infrastructure-less manner.We consider dynamic scenarios where continuous learning is necessary, and we adopt a mean field approach to investigate the limit performance of Floating Gossip in terms of amount of data that users can incorporate into their models, as a function of the main system parameters. Different from existing approaches in which either communication or computing aspects of Gossip Learning are analyzed and optimized, our approach accounts for the compound impact of both aspects. We validate our results through detailed simulations, proving good accuracy. Our model shows that Floating Gossip can be very effective in implementing continuous training and update of machine learning models in a cooperative manner, based on opportunistic exchanges among moving users.

The development of novel use cases in beyond-5G and 6G networks will rely, among other aspects, on the availability of computing resources at the edge, therefore enabling the realization of applications that are both computationally demanding and latency constrained, such as Mobile Augmented Reality (MAR). Indeed, due to end devices' intrinsic constraints on computation capabilities and battery, newer MAR applications require offloading their most demanding tasks. However, the constrained nature of edge resources implies that these tasks should be carefully allocated at the edge network in order to guarantee satisfactory Quality of Experience to end-users. In this context, we analyze the edge operator's resource allocation to support the energy-aware offloading of MAR tasks at the edge of the cellular network with the goal of not only maximizing service acceptance (i.e., revenue), but also optimizing the operator's business utility, which depends on its carbon footprint and the profit of operating the service. We leverage Deep Reinforcement Learning to propose an efficient model to operate the edge resource allocation that can adapt to different utilities.

We consider a portion of a RAN where end-users access services that imply the issue of a request through their associated base station (BS), followed by a computation on one of the available in-network computing facilities, and finally by the return of the result of the computation to the end-user who issued the request. The result must be returned within a specified latency deadline in order to be useful. Since not all BSs are equipped with a computing facility, some end-users may be disadvantaged, because they are associated with a BS from which the delay for a service request to reach a computing facility and for the results of the computation to come back is longer. Aiming at uniform end-user satisfaction, network operators should strive to on the one hand reduce differences in achieved end-user performance, while on the other obtain an efficient use of network resources. With simple analytical models we investigate the effectiveness of light network management algorithms, consisting in carefully choosing the routing probabilities of service requests toward one of the available computing facilities. We argue that at least some of such light network management algorithms should be compatible with the very stringent European Network Neutrality rules, and we show that they allow a good trade-off between overall resource utilization and equal performance experienced by end-users.

Aiding the ground cellular network with aerial base stations carried by drones has experienced an intensive raise of interest in the past years. Reconfigurable air-to-ground channels enable aerial stations to enhance users’ access links by means of seeking good line-of-sight connectivity while hovering in the air. In this article, we propose an analytical framework for the 3D placement of a fleet of coordinated drone relays. This framework optimizes network performance in terms of user throughput fairness, expressed through the α-fairness metric. The optimization problem is formulated as a mixed-integer non-convex program, which is intractable. Hence, we propose an extremal-optimization-based algorithm, Parallelized Alpha-fair Drone Deployment (PADD), which solves the problem online, in low-degree polynomial time. We evaluate our proposal by means of numerical simulations over the real topology of a dense city. We discuss the advantages of integrating drone relay stations in current networks and test several resource scheduling approaches in both static and dynamic scenarios, including with progressively larger and denser crowds.

With the current advances in unmanned aerial vehicle (UAV) technologies, aerial vehicles are becoming very attractive for many purposes. However, currently the bottleneck in their adoption is no longer due to architectural and protocol challenges and constraints, but rather to the limited energy that they can rely on. In this article, we design two power resupply schemes under the assumption of a fleet of homogeneous UAVs. Such schemes are designed to minimize the size of the fleet to be devoted to apersistentservice (i.e., carried out at all times) of a set of aerial locations. First, we consider the case where the aerial locations to be served are equidistant from an energy supply station. In that scenario, we design a simple scheduling scheme, that we name homogeneous rotating resupply (HoRR), which we prove to be feasible and exact in the sense that it uses the minimum possible number of UAVs to guarantee the permanent coverage of the aerial service locations. Then, we extend that work for the case of nonevenly distributed aerial locations. In this new scenario, we demonstrate that the problem becomes NP-hard, and design a lightweight scheduling scheme, partitioned heterogeneous rotating resupply (PHeRR), which extends the operation ofHoRRto the heterogeneous case. Through numerical analysis, we show thatPHeRRprovides near-exact resupply schedules.

We consider a radio access network slice serving mobile users whose requests imply computing requirements. Service is virtualized over either a powerful but distant cloud infrastructure or an edge computing host. The latter provides less computing and storage capacity with respect to the cloud, but can be reached with much lower delay. A tradeoff thus naturally arises between computing capacity and data transfer latency. We investigate the performance of this service model, discussing how service requests should be routed to edge or cloud servers. We look at the performance of various classes of online algorithms based on different levels of information about the system state. Our investigation is based on analytical models, simulations in OMNeT++, and a prototype implementation over operational cellular networks. First of all, we observe that distributing the load of service requests over edge and cloud is in general beneficial for performance, and simple to implement with a stateless online server selection policy that can be easily configured with near-optimal performance. Second, we shed light on the limited improvements that stateful polices can offer, notwithstanding they base their decisions on the knowledge of server congestion levels or round-trip latency conditions. Third, we unveil that stateful policies are dangerously prone to errors, which may make stateless policies preferable.

5G and beyond 5G networks will allow novel use cases by placing constrained computing nodes at the edge, following the Multi-access Edge Computing (MEC) paradigm. Edge nodes could be partially powered by intermittent renewable energies, leading to the possibility of having time-varying computing capacities. In this scenario, we tackle the problem of how to support gaming at the edge of the cellular network. Moving cloud-based games to the edge could be a premium service for end-users, thanks to reduced latency and higher bandwidth. The goal of our paper is to design a scheme that maximizes the utility of a service/infrastructure provider in a MEC scenario, with time-varying MEC nodes capacities powered by intermittent renewable energies. We formulate a multi-dimensional integer linear programming problem, proving that it is NP Hard in the strong sense. We prove that our problem is sub-modular and propose an efficient heuristic, GREENING, which considers the allocation of gaming sessionse and their migration. Through simulations, we show that our heuristic achieves performance close to what achievable by a solver, except with extremely lower complexity, and performs near-optimally, 20% better than state-of-the-art algorithms in terms of system utility. We also show that our scheme is compliant with currently adopted standards by ETSI and meant to support novel networking principles like network slicing.

Floating Content (FC) is a paradigm for localized infrastructure-less content dissemination, that aims at sharing information among nodes within a restricted geographical area by relying only on opportunistic content exchanges. FC provides the basis for the probabilistic spatial storage of shared information in a completely decentralized fashion, usually without support from dedicated infrastructure. One of the key open issues in FC is the characterization of its performance limits as functions of the system parameters, accounting for its reliance on volatile wireless exchanges and on limited user resources. This paper takes a first step towards tackling this issue, by elaborating a model for the storage capacity of FC, i.e., for the maximum amount of information that can be stored through the FC paradigm. The storage capacity of FC, and of similar probabilistic content dissemination systems, is evaluated with a powerful information theoretical approach, based on a mean field model of opportunistic information exchange. In addition, an extremely simple explicit approximate expression for storage capacity is derived. The numerical results generated by our analytical models are compared to the predictions of realistic simulations under different setups, proving the accuracy of our analytical approaches, and characterizing the properties of the FC storage capacity.

The adoption and integration of drones in commu-nication networks is becoming reality thanks to the deployment of advanced solutions for IoT and cellular communication relay schemes. However, using drones introduces new energy con-straints and scheduling issues in the dynamic management of the network topology, due to the need to call back and recharge, or substitute, drones that run out of energy. In this paper, we describe the design of a drone recharging scheme for realisti-cally limited flight time of drones, and leverage the presence of recharging stations. Indeed, drones need to be recharged periodically, and maximizing the operational time of drones is paramount to minimize the size of the fleet of drones to be devoted to a drone mission, hence its cost. We design Homogeneous Rotating Recharge (HRR), an optimal drone recharging scheduling that extends the coverage of a cellular network. HRR minimizes the number of back-up drones needed to guarantee a fixed number of operational drones, so as to support the operation of an underlying cellular network. Results show that operating a network of drones with our scheme provides reliable and stable performance over time.

The advent of multi access edge computing~(MEC) will enable latency-critical applications such as cooperative adaptive cruise control (also known as platooning) to be hosted at the edge of the network. MEC-based platooning will leverage the coverage of the cellular infrastructure to enable inter-vehicular communications, potentially overcoming crucial problems of vehicular ad-hoc networks~(VANETs) such as non-trivial packet loss rates. However, MEC-based platooning will require the controller to be migrated to the most suitable positions at the network edge, in order to maintain low-latency connections as the platoon moves. In this paper, we propose a context-awareQ -Learning algorithm that carries out such migrations only as often as is necessary, and thereby reduces the additional delays implicit in application migration across MEC hosts. When compared to the state-of-the-art approach named FollowME, our scheme exhibits better compliance of vehicle speed and spacing values to preset targets, as well as a reduced statistical dispersion.

We consider a slice of a radio access network where human and machine users access services with either high throughput or low latency requirements. The slice offers both eMBB and URLLC service categories to serve HTC (Human-Type Communication) and MTC (Machine-Type Communication) traffic. We propose to use eMBB for both HTC and MTC, transferring machine traffic to URLLC only when eMBB is not able to meet the low latency requirements of MTC. We show that by so doing the slice is capable of providing very good performance to about one hundred MTC users under high HTC traffic conditions. Instead, running time-critical MTC over only eMBB is not doable at all, whereas using URLLC suffices for at most a few tens of devices. Therefore, our approach improves the number of users served by the slice by one order of magnitude, without requiring extra resources or compromising performance. To study system performance we develop a novel analytical model of uplink packet transmissions, which covers both legacy eMBB-or URLLC-based MTC, as well as our compound approach. Our model allows to tune slice parameters so as to achieve the desired balance between HTC and MTC service guarantees. We validate the model against detailed simulations using as an example an autonomous driving scenario.

Leveraging machine learning (ML) for the detection of network problems dates back to handling call-dropping issues in telephony. However, troubleshooting cellular networks is still a manual task, assigned to experts who monitor the network around the clock. We present here TTrees (from Troubleshooting Trees), a practical and interpretable ML software tool that implements a methodology we have designed to automate the identification of the causes of performance anomalies in a cellular network. This methodology is unsupervised and combines multiple ML algorithms (e.g., decision trees and clustering). TTrees requires small volumes of data and is quick at training. Our experiments using real data from operational commercial mobile networks show that TTrees can automatically identify and accurately classify network anomalies - e.g., cases for which a network low performance is not apparently justified by op-erational conditions - training with just a few hundreds of data samples, hence enabling precise troubleshooting actions.

In this paper we develop a simple, yet accurate, performance model to understand if and how evolutions of standard cellular network protocols can be exploited to allow large numbers of machine type devices to access transmission resources with short latency, and we apply our model to the performance analysis of smart factory radio access networks. The model results shed light on the problems resulting from the application of evolved standard access procedures and help understand how many devices can be served per base station with specified latency targets. In addition, considering the simultaneous presence of different traffic classes, we investigate the effectiveness of prioritised access, exploiting access class barring techniques. Our model shows that, even with the sub-millisecond time slots foreseen in LTE Advanced Pro and 5G, a base station can accommodate at most few thousand devices to guarantee access latency below 100 ms with high transmission success probability. Lower access latency, of the order of 10 ms, can be achieved only with base stations serving an unrealistically small numbers of devices. This calls for a rethinking of wireless access strategies to avoid excessive latency in ultra-dense cell deployments within smart factory's infrastructures.

As a growing number of service and application providers choose cloud networks to deliver their services on a software-as-a-service (SaaS) basis, cloud providers need to make their provisioning systems agile enough to meet service level agreements (SLAs). At the same time, they should guard against over-provisioning, which limits their capacity to accommodate more tenants. To this end, we propose Shortterm memory Q-Learning pRovisioning (SQLR, pronounced as “scaler”), a system employing a customized variant of the modelfree reinforcement learning algorithm. It can reuse contextual knowledge learned from one workload to optimize the number of virtual machines (resources) allocated to serve other workload patterns. With minimal overhead, SQLR achieves comparable results to systems where resources are unconstrained. Our experiments show that we can reduce the amount of provisioned resources by about 20% with less than 1% overall service unavailability (due to blocking), while delivering similar response times to those of an over-provisioned system.

In this article, we develop a modeling framework to describe the uplink behavior of radio access in a sliced cell, including most features of the standard 3GPP multiple access procedures. Our model allows evaluating throughput and latency of each slice, as a function of cell parameters, when resources are in part dedicated to individual slices and in part shared. The availability of an accurate model is extremely important for the automated run time management of the cell and for the correct setting of its parameters. Indeed, our model considers most details of the behavior of sliced 5G cells, including Access Class Barring (ACB) and Random Access CHannel (RACH) procedures, preamble decoding, Random Access Response (RAR), and Radio Resource Control (RRC) procedures. To cope with a number of slices devoted to serve various co-deployed tenants, we derive a multi-class queueing model of the network processor. We then present (i) an accurate and computationally efficient technique to derive the performance measures of interest using continuous-time Markov chains, which scales up to a few slices only, and (ii) tight performance bounds, which are useful to tackle the case of more than a fistful of slices. We prove the accuracy of the model by comparison against a detailed simulator. Eventually, with our performance evaluation study, we show that our model is very effective in providing insight and guidelines for allocation and management of resources in cells hosting slices for services with different characteristics and performance requirements, such as machine type communications and human type communications.

Platooning of cars or trucks is one of the most relevant applications of autonomous driving, since it has the potential to greatly improve efficiency in road utilization and fuel consumption. Traditional proposals of vehicle platooning were based on distributed architectures with computation on board platoon vehicles and direct vehicle-to-vehicle (V2V) communications (or Dedicated Short Range Communication - DSRC), possibly with the support of roadside units. However, with the introduction of the 5G technology and of computing elements at the edge of the network, according to the multi-access edge computing (MEC) paradigm, the possibility emerges of a centralized control of platoons through MEC, with several significant advantages with respect to the V2V approach. For this reason, in this paper we investigate the feasibility of vehicle platooning in a centralized scenario where the control of vehicle speed and acceleration is managed by the network through its MEC facilities, possibly with a platooning-as-a-service (PaaS) paradigm. Using a detailed simulator, we show that, with realistic values of latency and packet loss probability, large platoons can be effectively controlled by MEC hosts.

Floating Content (FC) is a paradigmatic example of opportunistic infrastructure-less content sharing system where information is spread upon mobile node encounters within an area which is called the replication zone. FC allows the probabilistic spatial storage of information, even in the case of unreliable communications, with no support from dedicated servers. Given the large amount of communication and storage resources typically required to guarantee content persistence despite node mobility, a major open issue for the practical viability of FC and of similar distributed storage systems is the characterization of their storage capacity, i.e., of the maximum amount of information which can be stored for a given set of system parameters. In this paper, we propose a simple yet powerful information theoretical model of the storage capacity of probabilistic distributed storage systems such as FC, based on a mean field model of opportunistic information exchange. We evaluate numerically our results, and validate the model by means of realistic simulations, showing the accuracy of our mean field approach and characterizing the properties of the FC storage capacity versus the main system parameters.

Millimeter wave (mmWave) communication not only provides ultra-high speed radio access but is also ideally suited for efficient and flexible wireless backhauling. Specifically for dense deployments, a mmWave macro base station (MBS) that serves a large number of mmWave micro base stations (μBSs) is much more cost effective than legacy cellular architectures which connect μBSs to the core network through fibers. In addition, μBSs can cooperate with each other by acting as relay nodes. The directional nature of mmWave communication allows for spatial reuse, even in the presence of interference, which can be exploited to optimize mmWave wireless backhaul performance. The optimization opportunistically prioritizes the use of good connections at the MBS and further leverages compact and concurrent transmissions between μBS. Relays and directional antennas speed up communication, but increase the complexity of the scheduling problem. In this work, we study the mmWave backhaul scheduling problem and derive an MILP formulation for it as well as upper and lower bounds. We prove that the problem is NP-hard and can be approximated, but only if interference is negligible. By means of numerical simulations, we compare theoretical results with heuristics in small system sizes. Results validate the analysis and demonstrate the high performance of our heuristics in realistic cellular settings.

The integration of aerial base stations carried by drones in cellular networks offers promising opportunities to enhance the connectivity enjoyed by ground users. In this paper, we propose an optimization framework for the 3-D placement and repositioning of a fleet of drones with a realistic inter-drone interference model and drone connectivity constraints. We show how to maximize network coverage by means of an extremal-optimization algorithm. The design of our algorithm is based on a mixed-integer non-convex program formulation for a coverage problem that is NP-Complete, as we prove in the paper. We not only optimize drone positions in a 3-D space in polynomial time, but also assign flight routes solving an assignment problem and using a strong geometrical tool, namely Bézier curves, which are extremely useful for non-uniform and realistic topologies. Specifically, we propose to fly drones following Bézier curves to seek the chance of approaching to clusters of ground users. This enhances coverage over time while users and drones move. We assess the performance of our proposal for synthetic scenarios as well as realistic maps extracted from the topology of a capital city. We demonstrate that our framework is near-optimal and using Bézier curves increases coverage up to 47 percent while drones move.

In this paper we investigate the allocation of resources to slices on the radio interface of one cell. In particular, we develop a detailed stochastic model of the behaviour of the sliced cell radio access, including most features of the standard access procedures. Our model allows the computation of the throughput achieved by each slice, as well as the distribution of delays for each slice. The availability of a model capable of accurately predicting the performance achieved by services using different slices as a function of the cell parameters is extremely important for the automated run time management of the cell and for the correct setting of its parameters.Specifically, while our model can cope with a number of slices, we focus on the case of one cell comprising one slice for human type communications and one slice for machine type communications, and we discuss relevant emerging behaviours in the slices performance, as functions of the cell parameters.We validate the analytical predictions by comparison against the estimates of a detailed simulator, proving the accuracy of the model. Our model turns out to be very effective in providing insight and guidelines for allocation and management of resources in cells hosting slices carrying traffic derived from services with different characteristics and performance requirements.

In this paper, we study the capacity (i.e., the maximum achievable throughput) of radio access networks that exploit mobile small cell base stations carried by vehicles for adaptive densification in urban areas. While traditional approaches for radio access network densification with fixed small cell base stations are proving ineffective and extremely costly, mobile small cell base stations carried by vehicles can provide adaptive densification while achieving higher efficiency and lower cost. As a matter of fact, the existence of correlations between the number of mobile network subscribers and the number of vehicles in a given area allows for the spontaneous creation of temporary dense small cell deployments where and when needed. Ultimately, this approach to Radio Access Network densification increases efficiency, hence reducing costs for the operator. In this context, we first present an approach for the computation of the maximum throughput that can be obtained in an area served by traditional fixed base stations and mobile small cell base stations. We then provide initial estimates for the throughput improvements with respect to traditional deployments that rely on fixed base stations only. Evaluations in the realistic case study of the main railway station area in Milan, Italy, reveal that the use of mobile base stations achieves throughout gains up to 120% over legacy fixed access infrastructures, while granting higher fairness among subscribers.

Measuring and characterizing web page performance is a challenging task. When it comes to the mobile world, the highly varying technology characteristics coupled with the opaque network configuration make it even more difficult. Aiming at reproducibility, we present a large scale empirical study of web page performance collected in eleven commercial mobile networks spanning four countries. By digging into measurement from nearly two million web browsing sessions, we shed light on the impact of different web protocols, browsers, and mobile technologies on the web performance. We find that the impact of mobile broadband access is sizeable. For example, the median page load time using mobile broadband increases by a third compared to wired access. Mobility clearly stresses the system, with handover causing the most evident performance penalties. Contrariwise, our measurements show that the adoption of HTTP/2 and QUIC has practically negligible impact. To understand the intertwining of all parameters, we adopt state-of-the-art statistical methods to identify the significance of different factors on the web performance. Our analysis confirms the importance of access technology and mobility context as well as webpage composition and browser. Our work highlights the importance of large-scale measurements. Even with our controlled setup, the complexity of the mobile web ecosystem is challenging to untangle. For this, we are releasing the dataset as open data for validation and further research.

In this paper we study the queuing system that describes the operations of data services in cellular networks, e.g., UMTS, LTE/LTE-A, and most likely the forthcoming 5G standard. The main characteristic of all these systems is that after service access, resources remain allocated to the end user for some time before release, so that if the same user requests access to service again, before a system timeout, the same resources are still available. For the resulting queuing model, we express the blocking probability in closed form, and we also provide recursive expressions in the number of connections that can be handled by the base station. Closed form expressions are also derived for other useful performance metrics, i.e., throughput and network service time. Analytical results are validated against results of a detailed simulation model, and compared to traditional queueing models results, such as the Erlang B formula iteratively applied to the resources that are not blocked by potentially returning users. Our analysis complements the performance evaluation of the other key mechanism used to access data services in cellular networks, namely the random access, which precedes the resource allocation and utilization phase studied in this paper.

In this paper we develop a simple, yet accurate, performance model to understand if and how evolutions of traditional cellular network protocols can be exploited to allow large numbers of devices to gain control of transmission resources in smart factory radio access networks. The model results shed light on the applicability of evolved access procedures and help understand how many devices can be served per base station. In addition, considering the simultaneous presence of different traffic classes, we investigate the effectiveness of prioritised access, exploiting access class barring techniques. Our model shows that, even with the sub-millisecond time slots foreseen in LTE Advanced Pro and 5G, a base station can accommodate at most few thousand devices to guarantee access latencies below 100 ms with high transmission success probabilities. This calls for a rethinking of wireless access strategies to avoid ultra-dense cell deployments within smart factory infrastructures.

Video streaming is a very popular service among the end-users of Mobile Broadband (MBB) networks. DASH and WebRTC are two key technologies in the delivery of mobile video. In this work, we empirically assess the performance of video streaming with DASH and WebRTC in operational MBB networks, by using a large number of programmable network probes spread over several countries in the context of the MONROE project. We collect a large dataset from more than 300 video streaming experiments. Our dataset consists of network traces, performance indicators captured during the streaming sessions, and experiment metadata. The dataset captures the wide variability in video streaming performance, and unveils how mobile broadband is still not offering consistent quality guarantees across different countries and networks, especially for users on the move. We open source our complete software toolset and provide the video dataset as open data.

Information sharing is becoming a relevant issue for mobile broadband operators, due to the increasing popularity of social networks, to the increasing volumes of shared information, and to the steady increase in the number and capabilities of mobile devices connected to the Internet. Offloading information sharing services from the cellular infrastructure to device-to-device (D2D) communications can offer a welcome reduction of traffic. This paper discusses experiments with a smartphone information sharing application that can be used on commercial-off-the-shelf devices, with no need to root the device's software. In order to avoid unrealistic assumptions on the behavior of D2D communications, this work includes and builds upon the implementation of an Android application that supports infras-tructureless distributed content sharing among wireless devices using Wi-Fi Direct. The collected experimental data permit a detailed analysis of the occurring events, and a careful assessment of the performance of pervasive information sharing services. Our experiments reveal that many assumptions commonly used in the literature do not hold in real settings. We conclude that delay-tolerant services can be supported, albeit we also show that high densities of devices can (somewhat counter-intuitively) impair performance.

Open experimentation with operational Mobile Broadband (MBB) networks in the wild is currently a fundamental requirement of the research community in its endeavor to address the need of innovative solutions for mobile communications. Even more, there is a strong need for objective data about stability and performance of MBB (e.g., 3G/4G) networks, and for tools that rigorously and scientifically assess their status. In this paper, we introduce the MONROE measurement platform: an open access and flexible hardware-based platform for measurements and custom experimentation on operational MBB networks. The MONROE platform enables accurate, realistic and meaningful assessment of the performance and reliability of 11 MBB networks in Europe. We report on our experience designing, implementing and testing the solution we propose for the platform. We detail the challenges we overcame while building and testing the MONROE testbed and argue our design and implementation choices accordingly. We describe and exemplify the capabilities of the platform and the wide variety of experiments that external users already perform using the system.

Device-to-Device (D2D) communications potentially allow users placed in a cell to establish direct connections among them using several connection modes. In this paper, we propose Multi-Path D2D (MPD2D), a mathematical optimisation framework that accounts for the availability of D2D modes under flow demand requirements. MPD2D selects the combination of cellular and D2D links that fits better for boosting the network benefit. We consider the Underlay and Overlay as Inband D2D modes over cellular technology (LTE) and the Outband D2D mode exploiting WLAN technology (WiFi). Throughput, energy consumption, interference, and flow requirements are managed in order to maximise a network utility function accounting for cell capacity and power efficiency. We also derive a user satisfaction metric that accounts for the history of users within the cell. Integrating such a metric is lightweight yet very effective to drive towards almost complete fairness for the system over time. Our optimisation scheme is formulated as a Binary Non-Linear Program which results in very high performance in terms of throughput gain in comparison to other benchmarks for the D2D mode selection problem. Finally, we propose two effective heuristics whose performance and complexity we compare with optimal results and that largely outperform state of the art solutions.

An experience common to smartphone users is the difficulty in accessing services in crowded scenarios, such as a rock concert or a football match. In these cases, to (partially) mitigate frustration, users generically claim that network congestion is occurring, and try again and again to access the network with their smartphones: the result is that user frustration and network congestion reinforce each other! This paper investigates the root causes of poor performance of cellular networks in crowded environments and shows that the commonly adopted random access procedure can prevent full utilization of wireless resources. We develop a simple yet accurate analytical model to analyze why attempting random access to wireless resources can become a problem even when access congestion avoidance is enforced, e.g., with the Access Class Barring technique. The model we propose suggests that cluster-based network access, leveraging device-to-device communications, significantly alleviates access problems. Moreover, it sheds light on scalability laws that govern network utilization and quality of experience, in terms of cell capacity, number of access channels, and cluster size.

We introduce a channel-opportunistic architecture that enhances the user experience in terms of throughput, fairness, and energy efficiency. Our proposed architecture leverages D2D communication and it is built on top of the forthcoming D2D features of 5G networks. In particular, we focus on outband D2D where cellular users are allowed to exploit both cellular (i.e., LTE-A) and WLAN (i.e., WiFi Direct) technologies to establish a D2D connection. In this architecture, cellular users form clusters, in which only the user with the best channel condition communicates with the base station on behalf of the entire cluster. Within the cluster, the unlicensed spectrum is utilized to relay traffic. In this article, we provide analytical models for the proposed system and study the impact of several payoff distribution methods commonly adopted in the literature on coalitional game theory. We then introduce an operator-controlled relay protocol based on the D2D features of LTE-A and WiFi Direct, and demonstrate the feasibility and the advantages of D2D-assisted cellular communication with our SDR prototype.

Device-to-Device (D2D) communications represent a paradigm shift in cellular networks. In particular, analytical results on D2D performance for offloading and relay are very promising, but no experimental evidence validates these results to date. This paper is the first to provide an experimental analysis of outband D2D relay schemes. Moreover, we design D2D opportunistic relay with QoS enforcement (DORE), a complete framework for handling channel opportunities offered by outband D2D relay nodes. DORE consists of resource allocation optimization tools and protocols suitable to integrate QoS-aware opportunistic D2D communications within the architecture of 3GPP Proximity-based Services. We implement DORE using an SDR framework to profile cellular network dynamics in the presence of opportunistic outband D2D communication schemes. Our experiments reveal that outband D2D communications are suitable for relaying in a large variety of delay-sensitive cellular applications, and that DORE enables notable gains even with a few active D2D relay nodes.

Device-to-Device communications represent a paradigm shift in cellular networks. Analytical results on D2D performance are very promising, but there is no experimental evidence that validates these results to date. This paper is the first to provide an experimental analysis of outband D2D schemes. Moreover, we design DORE, a complete framework for handling channel opportunities offered by outband D2D relay nodes. DORE consists of resource allocation optimization tools and protocols suitable to integrate QoS-aware opportunistic D2D communications within the architecture of 3GPP Proximity-based Services. We implement DORE using an SDR framework to profile cellular network dynamics in presence of opportunistic outband D2D communication schemes. Our experiments reveal that outband D2D communications are suitable for a large variety of delay-sensitive cellular applications, and that DORE enables notable gains even with a few active D2D relay nodes.

This paper tackles the multifaceted challenges of controllingit mixed access networks with base stations, access points and D2D relay stations. Mixed access networks are of uncompelling importance since the next generation cellular access networks are envisioned to use different access technologies at once. We propose a unified framework to model throughput, airtime and power consumption of mobile terminals under any workload conditions in SDN-controlled mixed access networks. With our analysis, we formulate an access selection problem to optimize energy efficiency at the terminal side, while providing fair throughput guarantees. We show that the problem is NP-hard and propose an online low-complexity heuristic that largely outperforms legacy access selection policies. Our results indicate that energy efficiency can be traded off for fairness and that D2D relay is key to increase it both energy efficiency and fairness.

Mobile broadband (MBB) networks underpin numerous vital operations of the society and are arguably becoming the most important piece of the communications infrastructure. In this demo paper, our goal is to showcase the potential of a novel multi-homed MBB platform for measuring, monitoring and assessing the performance of MBB services in an objective manner. Our platform, MONROE, is composed of hundreds of nodes scattered over four European countries and a backend system that collects the measurement results. Through a user-friendly web client, the experimenters can schedule and deploy their experiments. The platform further embeds traffic analysis tools for real-time traffic flow analysis and a powerful visualization tool.

Opportunistic schedulers such as MaxRate and Proportional Fair are known for trading off between throughput and fairness of users in cellular networks. In this paper, we propose a novel solution that integrates opportunistic scheduling design principles and cooperative D2D communication capabilities in order to maximize fairness without sacrificing throughput. Specifically, we develop a mathematical approach and design a smart tie-breaking scheme which maximizes the fairness achieved by the MaxRate scheduler. However, our approach could be applied to improve fairness of any scheduler. In addition, we show that users that cooperatively form D2D clusters benefit from both higher throughput and fairness. Our scheduling scheme is simple to implement, scales linearly with the number of clusters, and is able to double the throughput of Equal Time schedulers and to outperform by 20% or more Proportional Fair schedulers, while providing a user fairness index comparable to or better than Proportional Fair.

In this paper, we analytically model the behavior of gigabit Energy Efficient Ethernet (EEE) links with coalescing using M/G/1 queues with sleep and wake-up periods. The particularity of gigabit EEE links is that energy-saving operations are triggered only when links are inactive in both transmission directions. Our model approximates with a good accuracy both the energy saving and the average packet delay by using a few significant traffic descriptors. Furthermore, we use real traffic traces to investigate on the performance of static as well as dynamic coalescing schemes. Surprisingly, our evaluation shows that dynamic coalescing does not significantly outperform static coalescing.

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.

In this paper we present an analytic approach to performance analysis of ad hoc networks under non saturation conditions, which does not rely on any assumption on traffic statistics. Our approach assumes traffic to be constrained by leaky bucket arrival curves, and it relies on a coupled processors model to capture the dependencies between user achievable rates due to sharing of the wireless transmission medium. We derive sufficient conditions for stability of transmission queues in an ad hoc network, and we describe a method for the determination of a proportionally fair allocation of resources, which allows trading the fairness of the solution for computational complexity. We validate our results through simulations, showing how our approach allows deriving operating points which both increase the fairness of the allocation and the overall average utilization of network resources with respect to saturated models.

In this work, we propose a dynamic coalescing algorithm for IEEE 802.3az standard, which dynamically adapts coalescing operations based on the current load and on the delay experienced in the link. Our results show that our algorithm almost doubles the energy efficiency of EEE with static coalescing while keeping packet delay bounded.

This work presents the first experimental evaluation of the Floating Content (FC) communication paradigm in a campus/large office setting. By logging information transfer events we have characterized mobility patterns, and we have assessed the performance of services implemented using the FC paradigm. Our results unveil the key relevance of group dynamics in user movements for the FC performance. Surprisingly, in such an environment, our results show that a relatively low user density is enough to guarantee content persistence over time, contrarily to predictions from available models. Based on these experimental findings, we develop a novel simple analytical model that accounts for the peculiarities of the mobility patterns in such a setting, and that can accurately predict the effectiveness of FC for the implementation of services in a campus/large office setting.

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.

The significant growth in mobile data traffic and the ever- increasing user's demand for high-speed, always connected networks continue challenging network providers and lead research towards solutions to enable faster, scalable and more flexible networks. In this paper we present the CROWD approach, a networking framework providing mechanisms to tackle the high densification and heterogeneity of wireless networks. The goal of CROWD is to design protocols and algorithms for very dense and heterogeneous wireless networks, which we call DenseNets. The mechanisms we propose include energy efficiency, MAC enhancements, connectivity management and backhaul configuration to contribute to the next generation of networks considering density as a resource instead of as an obstacle.

In this paper, we propose Floating Band D2D, an adaptive framework to exploit the full potential of Device-to-Device (D2D) transmission modes. We show that inband and outband D2D modes exhibit different pros and cons in terms of complexity, interference, and spectral efficiency. Moreover, none of these modes is suitable as a one-size-fits-all solution for today's cellular networks, due to diverse network requirements and variable users' behavior. Therefore, we unveil the need for going beyond traditional single-band mode-selection schemes. Specifically, we model and formulate a general and adaptive multi-band mode selection problem, namely Floating Band D2D. The problem is NP-hard, so we propose simple yet effective heuristics. Our results show the superiority of the Floating Band D2D framework, which dramatically increases network utility and achieves near complete fairness.

In this work, we present an exhaustive empirical characterization of the power requirements of multiple components of data center servers. To do so, we devise different experiments to stress these components, taking into account the multiple available frequencies and the fact that we are working with multicore servers. In these experiments, we measure energy consumption of server components and identify their optimal operational points. Our study proves that the curve defining the minimal CPU power utilization, as a function of the load in active cycles per second, is neither concave nor purely convex. Instead, it definitively shows a super-linear dependence on the load. Similarly, we present results on how to improve the efficiency of network cards and disks. Finally, we validate the accuracy of the model derived from our characterization by comparing the real energy consumed by two Hadoop applications-PageRank and WordCount-with the estimation from our model, obtaining errors below 4.1% on average.

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.

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.

Opportunistic scheduling was initially proposed to exploit user channel diversity for network capacity enhancement. However, the achievable gain of opportunistic schedulers is generally restrained due to fairness considerations which impose a tradeoff between fairness and throughput. In this paper, we show via analysis and numerical simulations that opportunistic scheduling not only increases network throughput dramatically, but also increases energy efficiency and can be fair to the users when they cooperate, in particular by using D2D communications. We propose to leverage smartphone's dual-radio interface capabilities to form clusters among mobile users. We design simple, scalable and energy-efficient D2D-assisted opportunistic strategies, which would incentivize mobile users to form clusters. We use a coalitional game theory approach to analyze the cluster formation mechanism, and show that proportional fair-based intra-cluster payoff distribution brings significant incentive to all mobile users regardless of their channel quality.

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.

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.

In this paper, we analyze the power save and its impact on web traffic performance when customers adopt the continuous connectivity paradigm. To this aim, we provide a model for packet transmission and cost. We model each mobile user's traffic with a realistic web traffic profile, and study the aggregate behavior of the users attached to a base station by means of a processor-shared queueing system. In particular, we evaluate user access delay, download time and expected economy of energy in the cell. The model is validated through packet-level simulations. Our model shows that dramatic energy save can be achieved by both mobile users and base stations, e.g., as much as 70% of the energy cost due to packet transmission at the base station.

In this paper we propose an efficient heuristic for solving the Distance-1 Edge Coloring problem (D1EC) for the on-the-fly assignment of orthogonal wireless channels in wireless as soon as a topology change occurs. The coloring algorithm exploits the simulated annealing paradigm, i.e., a generalization of Monte Carlo methods for solving combinatorial problems. We show that the simulated annealing-based coloring converges fast to a sub optimal coloring scheme even for the case of dynamic channel allocation. However, a stateful implementation of the D1EC scheme is needed in order to speed-up the network coloring upon topology changes. In fact, a stateful D1EC reduces the algorithm’s convergence time by more than 60% in comparison to stateless algorithms.

IEEE 802.11-based mesh networks can yield a throughput distribution among nodes that is spatially biased, with traffic originating from nodes that directly communicate with the gateway obtaining higher throughput than all other upstream traffic. In particular, if single-hop nodes fully utilize the gateway's resources, all other nodes communicating with the same gateway will attain very little (if any) throughput. In this paper, we show that it is sufficient to rate limit the single-hop nodes in order to give transmission opportunities to all other nodes. Based on this observation, we develop a new rate limiting scheme for 802.11 mesh networks, which counters the spatial bias effect and does not require, in principle, any control overhead. Our rate control mechanism is based on three key techniques. First, we exploit the system's inherent priority nature and control the throughput of the spatially disadvantaged nodes by only controlling the transmission rate of the spatially advantaged nodes. Namely, the single-hop nodes collectively behave as a proxy controller for multi-hop nodes in order to achieve the desired bandwidth distribution. Second, we devise a rate limiting scheme that enforces a utilization threshold for advantaged single-hop traffic and guarantees a small portion of the gateway resources for the disadvantaged multi-hop traffic. We infer demand for multi-hop flow bandwidth whenever gateway resource usage exceeds this threshold, and subsequently reduce the rates of the spatially advantaged single-hop nodes. Third, since the more bandwidth the spatially disadvantaged nodes attain, the easier they can \emph{signal} their demands, we allow the bandwidth unavailable for the advantaged nodes to be elastic, i.e., the more the disadvantaged flows use the gateway resources, the higher the utilization threshold is. We develop an analytical model to study a system characterized by such priority, dynamic utilization thresholds, and control by proxy. Moreover, we use simulations to evaluate the proposed elastic rate limiting technique.

In this paper, we perform an extensive measurement study on a multi-tier mesh network serving 4,000 users. Such dense mesh deployments have high levels of interaction across heterogeneous wireless links. We find that this heterogeneous backhaul consisting of data-carrying (forwarding) linksandnon- data-carrying (non-forwarding) links creates two key effects on performance. First, we show that low-rate management and control packets can produce a disproportionally large degradation in data throughput. We define a metric for this effect called Wireless Overhead Multiplier and use it to quantify the impact of MAC and PHY mechanisms on the the throughput degradation. Surprisingly, we show that these multiplicative effects are primarily driven by the non-forwarding links where, in the worst case, data packets lose physical layer capture to the overhead, yielding disproportionate throughput degradation. Finally, we show that when data flows contend in this worst-case scenario, the loss-based autorate policy is unnecessarily triggered, causing throughput imbalance and poor network utilization.

Significant progress has been made in understanding the behavior of TCP and congestion-controlled traffic over multi- hop wireless networks. Despite these advances, however, no prior work identified severe throughput imbalances in the basic scenario of mesh networks, in which one-hop flows contend with two-hop flows for gateway access. In this paper, we demonstrate via real network measurements, test-bed experiments, and an analytical model that starvation exists in such a scenario, i.e., the one-hop flow receives most of the bandwidth while the two- hop flow starves. Our analytical model yields a solution consisting of a simple contention window policy that can be implemented via mechanisms in IEEE 802.11e. Despite its simplicity, we demonstrate through analysis, experiments, and simulations, that the policy has a powerful effect on network-wide behavior, shifting the network's queuing points, mitigating problematic MAC behavior, and ensuring that TCP flows obtain a fair share of the gateway bandwidth, irrespective of their spatial locations.

This paper presents a resource management mechanism aimed at improving the effectiveness of streaming services in vehicular networks. The scenario considered in this paper is that of a group of customers located into a same public vehicle, e.g. a moving train connected to the network via a satellite link, and requesting either video-on-demand-like services, as well as real-time diffusive (broadcast) streaming services. We show that a proxy server, devised to introduce an elastic buffer aimed at decoupling the information retrieval download speed on the outer network from the natural play-out speed used in the vehicular network, results to be an extremely effective approach in reducing the outage probability given by link failure in the outer network (e.g. tunnel crossing). A resource management mechanism, called A2M, is applied to both video-on-demand and diffusive services, and its performance effectiveness is evaluated through simulation.

We focus on an admission controlled traffic scenario. Flows, characterized by heavy-tailed on/off periods, are admitted to a network link according to a measurement based admission control algorithm. Our simulation results show that the long range dependence of the accepted traffic aggregate is marginal, particularly when compared with that resulting from a traffic aggregate accepted by a parameter-based admission control scheme. Our results appear to suggest that measurement based admission control is a value added tool to dramatically improve performance in the presence of self-similar traffic, rather than being a mere approximation for traditional (parameter-based) admission control schemes.