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International audience; The integration of RIS elements in upcoming 6G networks appears a major breakthrough to extend network coverage and capacity. This paper introduces a new link-layer scheme for RIS-enabled communications building on existing models for the physical layer of RIS technologies. The scheme is able to integrate the selection of precoders/beams in a codebook and scheduling of UEs at once. Furthermore, elements for the integration of the proposed scheme in current 3GPP-5G specifications are addressed. The scheduler combines a slow mechanism operating at the downlink OFDMA frame scale with a standard proportional fair scheduler operating at the OFDMA slot scale and accommodating both LOS and non-LOS UEs. We introduce an optimization framework for the proposed scheduler whose performance is hence simulated in a reference scenario. The spectral efficiency figures in our tests confirm a large gain of the scheme against a baseline direct path scheme. Finally, the scheduler optimization permits to achieve a further improvement of 15-20% for non line-of-sight users.

The average coflow completion time (CCT) is the standard performance metric in coflow scheduling. However, standard CCT minimization may introduce unfairness between the data transfer phase of different computing jobs. Thus, while progress guarantees have been introduced in the literature to mitigate this fairness issue, the trade-off between fairness and efficiency of data transfer is hard to control. This paper introduces a fairness framework for coflow scheduling based on the concept of slowdown, i.e., the performance loss of a coflow compared to isolation. By controlling the slowdown it is possible to enforce a target coflow progress while minimizing the average CCT. In the proposed framework, the minimum slowdown for a batch of coflows can be determined in polynomial time. By showing the equivalence with Gaussian elimination, slowdown constraints are introduced into primal-dual iterations of the CoFair algorithm. The algorithm extends the class of the sigma-order schedulers to solve the fair coflow scheduling problem in polynomial time. It provides a 4-approximation of the average CCT w.r.t. an optimal scheduler. Extensive numerical results demonstrate that this approach can trade off average CCT for slowdown more efficiently than existing state of the art schedulers.

Many cloud service providers (CSPs) offer an on-demand service with a small delay. Motivated by the reality of cloud ecosystems, we study non-interruptible services and consider a differentiated service model to complement the existing market by offering multiple service level agreements (SLAs) to satisfy users with different delay-tolerance. The model itself is incentive compatible by construction. Two typical architectures are considered to fulfill SLAs: (i) non-preemptive priority queues, and (ii) multiple independent groups of servers. We leverage queueing theory to establish guidelines for the resultant market: (a) under the first architecture, the service model can only improve the revenue marginally over the pure on-demand service model; (b) under the second architecture, we give a closed-form expression of the revenue improvement when a CSP offers two SLAs and derive a condition under which the market is viable. Additionally, under the second architecture, we give an exhaustive search procedure to find the optimal SLA delays and prices when a CSP generally offers multiple SLAs. Numerical results show that the achieved revenue improvement can be significant even if two SLAs are offered. Our results can help CSPs design optimal delay-differentiated services and choose appropriate serving architectures.

A fundamental goal in the design of IaaS service is to enable both user-friendly and cost-effective service access, while attaining high resource efficiency for revenue maximization. QoS differentiation is an important lens to achieve this design goal. In this paper, we propose the first analytical QoS-differentiated resource management and pricing architecture in the cloud computing context; here, a cloud service provider (CSP) offers a portfolio of SLAs. In order to maximize the CSP's revenue, we address two technical questions: (1) how to set the SLA prices so as to direct users to the SLAs best fitting their needs, and, (2) determining how many servers should be assigned to each SLA, and which users and how many of their jobs are admitted to be served. We propose optimal schemes to jointly determine SLA-based prices and perform capacity planning in polynomial time. Our pricing model retains high usability at the customer's end. Compared with standard usage-based pricing schemes, numerical results show that the proposed scheme can improve the revenue by up to a five-fold increase.

The main task of HTTP Adaptive Streaming is to adapt video quality dynamically under variable network conditions. This is a key feature for multimedia delivery especially when quality of service cannot be granted network-wide and, e.g., throughput may suffer short term fluctuations. Hence, robust bitrate adaptation schemes become crucial in order to improve video quality. The objective, in this context, is to control the filling level of the playback buffer and maximize the quality of the video, while avoiding unnecessary video quality variations. In this paper we study bitrate adaptation algorithms based on Backward-Shifted Coding (BSC), a scalable video coding scheme able to greatly improve video quality. We design bitrate adaptation algorithms that balance video rate smoothness and high network capacity utilization, leveraging both on throughput-based and buffer-based adaptation mechanisms. Extensive simulations using synthetic and real-world video traffic traces show that the proposed scheme performs remarkably well even under challenging network conditions.

In this paper we design an incentive mechanism for heterogeneous Delay Tolerant Networks (DTNs). The proposed mechanism tackles a core problem of such systems: how to induce coordination of DTN relays in order to achieve a target performance figure, e.g., delivery probability or end-to-end delay, under a given constraint in term of network resources, e.g., number of active nodes or energy consumption. Also, we account for the realistic case when the cost for taking part in the forwarding process varies with the devices' technology or the users' habits. Finally, the scheme is truly applicable to DTNs since it works with no need for end-to-end connectivity. In this context, we first introduce the basic coordination mechanism leveraging the notion of a Minority Game. In this game, relays compete to be in the population minority and their utility is defined in combination with a rewarding mechanism. The rewards in turn configure as a control by which the network operator controls the desired operating point for the DTN. To this aim, we provide a full characterization of the equilibria of the game in the case of heterogeneous DTNs. Finally, a learning algorithm based on stochastic approximations provably drives the system to the equilibrium solution without requiring perfect state information at relay nodes or at the source node and without using end-to-end communications to implement the rewarding scheme. We provide extensive numerical results to validate the proposed scheme., Comment: Technical report

Publication in the conference proceedings of EUSIPCO, Florence, Italy, 2006

In this paper, we apply the concepts of Markov decision evolutionary games to non-cooperative forwarding con- trol of Delay Tolerant Networks (DTN). Specifically, we rely on the design of mechanisms at the source node to study forwarding probability of the message in a DTN using the two- hop routing. We study the forwarding probability as a function of the competition within a large population of mobiles which need occasionally to make some action. In particular, for each message generated by a source, each mobile may take a decision that concerns the strategy by which the mobile participates to the relaying of the message from source to destination. A mobile that participates receives a unit of reward if it is the first to deliver a copy of the packet to the destination. The action taken by a mobile determine not only the immediate reward but also the transition probability to its next battery energy state. We characterize the Evolutionary Stable Strategies (ESS) for these games and propose a method to compute them. We also propose a mechanism design at the source in order to maximize the message delivery probability to the destination, given the equilibrium behavior (called Evolutionary Stable Strategy - ESS).

In this paper, we apply evolutionary games to non-cooperative forwarding control of Delay Tolerant Networks (DTN). We focus our study on the probability to deliver a message from source to destination in a DTN using two-hop routing. We derive the success probability as a function of the competition within a large population of mobiles. In particular, for each message generated by a source, each mobile may take a decision that concerns the strategy by which the mobile participates to the relaying. A mobile that participates receives a unit of reward if it is the first to deliver a copy of the packet to the destination. A utility function is introduced as the difference between a reward unit and the energy cost. We show how the evolution dynamics and the equilibrium behavior (called Evolutionary Stable Strategy - ESS) are influenced by the characteristics of inter contact time, energy expenditure and pricing characteristics. We specialize our analysis also to mechanisms that the source may introduce in order to have the message delivered to the destination with high probability within a given deadline.

Telecommunication systems are becoming more and more complex and dynamic. As an example, on e-commerce platforms, on content provision platforms or cloud computing platforms, a variety of dierent agents interact and inuence each other. In data-centers, large numbers of dierent ows need to be served meeting a given quality of service, and the routing decisions taken for one ow can easily inuence the performance of some other ows. These examples are characterized by complexity and dynamism. These features emerge form the interactions among agents and the environment where they operate, and from the fact that agents can choose their actions within a spectrum of possible strategies. As a result, given the structural constraints of a system, it is usually challenging to understand what the emerging properties and the operating points of such system are. In the present work we analyze the dynamics of such systems, with the aim of devising strategies to optimize their operating points. Given their variety, we resort to dierent techniques to analyze each of them. In the second and third chapters we will resort to a game theoretical framework, as we will need to model an edge caching system as a multi-agent system where the decisions made by one of the agents determine the behavior of all the others. In the fourth chapter we will tackle a ow segmentation problem. In this context we will need to model the behavior of an agent seeking to maximize a private utility from the interactions with a stochastic environment, therefore we will use the theory of stochastic optimization. In the fth chapter we tackle the problem of determining an optimal trunk-reservation policy for a queue control problem, in absence of knowledge of the statistical properties of the queue. In this case, we will use reinforcement learning tools to devise an algorithm based on policy learning that converges to an optimal policy.

The Network Neutrality issue has been at the center of debate worldwide lately. Some countries have established laws so that principles of network neutrality are respected. Among the questions that have been discussed in these debates there is whether to allow agreements between service and content providers, i.e. to allow some preferential treatment by an operator to traffic from some providers (identity-based discrimination).

In this thesis we analyze the relationship between epidemiology and network theory, starting from the observation that the viral propagation between interacting agents is determined by intrinsic characteristics of the population contact network. We aim to investigate how a particular network structure can impact on the long-term behavior of epidemics. This field is way too large to be fully discussed; we limit ourselves to consider networks that are partitioned into local communities, in order to incorporate realistic contact structures into the model. The gross structure of hierarchical networks of this kind can be described by a quotient graph. The rationale of this approach is that individuals infect those belonging to the same community with higher probability than individuals in other communities. We describe the epidemic process as a continuous-time individual-based susceptible–infected–susceptible (SIS) model using a first-order mean-field approximation, both in homogeneous and in heterogeneous setting. For this mean-field model we show that the spectral radius of the smaller quotient graph, in connection with the infecting and curing rates, is related to the epidemic threshold, and it gives conditions in order to decide whether the overall healthy-state defines a globally asymptotically stable or an unstable equilibrium. Moreover we show that above the threshold another steady-state exists that can be computed using a lower-dimensional dynamical system associated with the evolution of the process on the quotient graph. Our investigations are based on the graph-theoretical notion of equitable partition and of its recent and rather flexible generalization, that of almost equitable partition. We also consider the important issue related to the control of the infectious disease. Taking into account the connectivity of the network, we provide a cost-optimal distribution of resources to prevent the disease from persisting indefinitely in the population; for a particular case of two-level immunization problem we report on the construction of a polynomial time complexity algorithm. In the second part of the thesis we include stochasticity in the model, considering the infection rates in the form of independent stochastic processes. This allows us to get stochastic differential equation for the probability of infection in each node. We report on the existence of the solution for all times. Moreover we show that there exist two regions, given in terms of the coefficients of the model, one where the system goes to extinction almost surely, and the other where it is stochastic permanent.