Approaches for Future Internet architecture design and Quality of Experience (QoE) Control

Researching a Future Internet capable of overcoming the current Internet limitations is a strategic investment. In this respect, this paper presents some concepts that can contribute to provide some guidelines to overcome the above-mentioned limitations. In the authors' vision, a key Future Internet target is to allow applications to transparently, efficiently and flexibly exploit the available network resources with the aim to match the users' expectations. Such expectations could be expressed in terms of a properly defined Quality of Experience (QoE). In this respect, this paper provides some approaches for coping with the QoE provision problem

Attack-Surface Metrics, OSSTMM and Common Criteria Based Approach to “Composable Security” in Complex Systems

In recent studies on Complex Systems and Systems-of-Systems theory, a huge effort has been put to cope with behavioral problems, i.e. the possibility of controlling a desired overall or end-to-end behavior by acting on the individual elements that constitute the system itself. This problem is particularly important in the “SMART” environments, where the huge number of devices, their significant computational capabilities as well as their tight interconnection produce a complex architecture for which it is difficult to predict (and control) a desired behavior; furthermore, if the scenario is allowed to dynamically evolve through the modification of both topology and subsystems composition, then the control problem becomes a real challenge. In this perspective, the purpose of this paper is to cope with a specific class of control problems in complex systems, the “composability of security functionalities”, recently introduced by the European Funded research through the pSHIELD and nSHIELD projects (ARTEMIS-JU programme). In a nutshell, the objective of this research is to define a control framework that, given a target security level for a specific application scenario, is able to i) discover the system elements, ii) quantify the security level of each element as well as its contribution to the security of the overall system, and iii) compute the control action to be applied on such elements to reach the security target. The main innovations proposed by the authors are: i) the definition of a comprehensive methodology to quantify the security of a generic system independently from the technology and the environment and ii) the integration of the derived metrics into a closed-loop scheme that allows real-time control of the system. The solution described in this work moves from the proof-of-concepts performed in the early phase of the pSHIELD research and enrich es it through an innovative metric with a sound foundation, able to potentially cope with any kind of pplication scenarios (railways, automotive, manufacturing, ...)

Control architecture to provide E2E security in interconnected systems: the (new) SHIELD approach

Modern Systems are usually obtained as incremental composition of proper (smaller and SMART) subsystems interacting through communication interfaces. Such flexible architecture allows the pervasive provisioning of a wide class of services, ranging from multimedia contents delivery, through monitoring data collection, to command and control functionalities. All these services requires that the adequate level of robustness and security is assured at End-to- End (E2E) level, according to user requirements that may vary depending on the specific context or the involved technologies. A flexible methodology to dynamically control the security level of the service being offered is then needed. In this perspective, the authors propose an innovative control architecture able to assure E2E security potentially in any application, by dynamically adapting to the underlying systems and using its resources to “build the security”. In particular, the main novelties of this solution are: i) the possibility of dynamically discovering and composing the available functionalities offered by the environment to satisfy the security needs and ii) the possibility of modelling and measuring the security through innovative technology-independent metrics. The results presented in this paper moves from the solutions identified in the pSHIELD project and enrich them with the innovative advances achieved through the nSHIELD research, still ongoing. Both projects have been funded by ARTEMIS-JU

Refinement of the diagnostic approach for the identification of children and adolescents affected by familial hypercholesterolemia: Evidence from the LIPIGEN study

Background and aims: We aimed to describe the limitations of familiar hypercholesterolemia (FH) diagnosis in childhood based on the presence of the typical features of FH, such as physical sings of cholesterol accumulation and personal or family history of premature cardiovascular disease or hypercholesterolemia, comparing their prevalence in the adult and paediatric FH population, and to illustrate how additional information can lead to a more effective diagnosis of FH at a younger age.Methods: From the Italian LIPIGEN cohort, we selected 1188 (>= 18 years) and 708 (<18 years) genetically-confirmed heterozygous FH, with no missing personal FH features. The prevalence of personal and familial FH features was compared between the two groups. For a sub-group of the paediatric cohort (N = 374), data about premature coronary heart disease (CHD) in second-degree family members were also included in the evaluation.Results: The lower prevalence of typical FH features in children/adolescents vs adults was confirmed: the prevalence of tendon xanthoma was 2.1% vs 13.1%, and arcus cornealis was present in 1.6% vs 11.2% of the cohorts, respectively. No children presented clinical history of premature CHD or cerebral/peripheral vascular disease compared to 8.8% and 5.6% of adults, respectively. The prevalence of premature CHD in first-degree relatives was significantly higher in adults compared to children/adolescents (38.9% vs 19.7%). In the sub-cohort analysis, a premature CHD event in parents was reported in 63 out of 374 subjects (16.8%), but the percentage increased to 54.0% extending the evaluation also to second-degree relatives.Conclusions: In children, the typical FH features are clearly less informative than in adults. A more thorough data collection, adding information about second-degree relatives, could improve the diagnosis of FH at younger age

A lexicographic approach to constrained MDP admission control

This paper proposes a Reinforcement Learningbased lexicographic approach to the Call Admission Control (CAC) problem in communication networks. The CAC problem is modeled as a multi-constrained Markov Decision Problem (MDP). To overcome the problems of the standard approaches to the solution of constrained MDP, a multiconstraint lexicographic approach is defined, and an on-line implementation based on Reinforcement Learning techniques is proposed. Simulations validate the proposed approach. © 2013 IEEE

A Lexicographic Approach to Constrained MDP Admission Control

This paper proposes a reinforcement learning-based lexicographic approach to the call admission control problem in communication networks. The admission control problem is modelled as a multiconstrained Markov decision process. To overcome the problems of the standard approaches to the solution of constrained Markov decision processes, based on the linear programming formulation or on a Lagrangian approach, a multi-constraint lexicographic approach is defined, and an online implementation based on reinforcement learning techniques is proposed. Simulations validate the proposed approach

Optimal planning and routing in medium voltage powerline communications networks

This paper deals with the problem of deploying a PowerLine Communication (PLC) network over a medium voltage (MV) power grid. The PLC network is used to connect the end nodes (ENs) of the MV grid to the service provider by means of PLC network nodes enabled as access points. In particular, a network planning problem is faced wherein we require to define the PLC network topology by deciding which MV network nodes are to be enabled as access points. An optimization problem is then formulated, which minimizes the cost of enabling the access points and maximizes the reliability of PLC network paths in a multi-objective optimization fashion. This work also considers resiliency (i.e., it guarantees the PLC network connectivity even in case of link faults) and capacity constraints (i.e., it checks that there are enough resources to transmit the estimated amount of traffic over the PLC network paths). As a byproduct, the optimization algorithm also returns the optimal routing. Simulations based on realistic MV network topologies validate the proposed approach. © 2010-2012 IEEE

An approximate dynamic programming approach to resource management in multi-cloud scenarios

The programmability and the virtualisation of network resources are crucial to deploy scalable Information and Communications Technology (ICT) services. The increasing demand of cloud services, mainly devoted to the storage and computing, requires a new functional element, the Cloud Management Broker (CMB), aimed at managing multiple cloud resources to meet the customers’ requirements and, simultaneously, to optimise their usage. This paper proposes a multi-cloud resource allocation algorithm that manages the resource requests with the aim of maximising the CMB revenue over time. The algorithm is based on Markov decision process modelling and relies on reinforcement learning techniques to find online an approximate solution

Resource management in multi-cloud scenarios via reinforcement learning

The concept of Virtualization of Network Resources, such as cloud storage and computing power, has become crucial to any business that needs dynamic IT resources. With virtualization, we refer to the migration of various tasks, usually performed by hardware infrastructures, to virtual IT resources. This approach allows resources to be rapidly deployed, scaled and dynamically reassigned. In the last few years, the demand of cloud resources has grown dramatically, and a new figure plays a key role: the Cloud Management Broker (CMB). The CMB purpose is to manage cloud resources to meet the user's requirements and, at the same time, to optimize their usage. This paper proposes two multi-cloud resource allocation algorithms that manage the resource requests with the aim of maximizing the CMB revenue over time. The algorithms, based on Reinforcement Learning techniques, are evaluated and compared by numerical simulations