Distributed workload control for federated service discovery

The diffusion of the internet paradigm in each aspect of human life continuously fosters the widespread of new technologies and related services. In the Future Internet scenario, where 5G telecommunication facilities will interact with the internet of things world, analyzing in real time big amounts of data to feed a potential infinite set of services belonging to different administrative domains, the role of a federated service discovery will become crucial. In this paper the authors propose a distributed workload control algorithm to handle efficiently the service discovery requests, with the aim of minimizing the overall latencies experienced by the requesting user agents. The authors propose an algorithm based on the Wardrop equilibrium, which is a gametheoretical concept, applied to the federated service discovery domain. The proposed solution has been implemented and its performance has been assessed adopting different network topologies and metrics. An open source simulation environment has been created allowing other researchers to test the proposed solution

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

Correlation energy and spin polarization in the 2D electron gas

The ground state energy of the two--dimensional uniform electron gas has been calculated with fixed--node diffusion Monte Carlo, including backflow correlations, for a wide range of electron densities as a function of spin polarization. We give a simple analytic representation of the correlation energy which fits the density and polarization dependence of the simulation data and includes several known high- and low-density limits. This parametrization provides a reliable local spin density energy functional for two-dimensional systems and an estimate for the spin susceptibility. Within the proposed model for the correlation energy, a weakly first--order polarization transition occurs shortly before Wigner crystallization as the density is lowered.Comment: Minor typos corrected, see erratum: Phys. Rev. Lett. 91, 109902(E) (2003

Testing of two-dimensional local approximations in the current-spin and spin-density-functional theories

We study a model quantum dot system in an external magnetic field by using both the spin-density-functional theory and the current-spin-density-functional theory. The theories are used with local approximations for the spin-density and the vorticity. The reliabilities of different parametrizations for the exchange-correlation functionals are tested by comparing the ensuing energetics with quantum Monte Carlo results. The limit where the vorticity dependence should be used in the exchange-correlation functionals is discussed.Comment: 18 pages, 3 figures, 5 eps-figure file

An islet population model of pancreatic insulin production

Glucose-induced pancreatic insulin release is the fundamental mechanism responsible for glucose homeostasis, its failure determining the clinical picture of Diabetes Mellitus. The details of the feedback loop controlling glycemia through insulin secretion have been an important subject of investigation and modeling for decades. In this note, a recently published population model is considered, whose purpose is to replicate in silico different observed phenomena such as low frequency glycemia-insulinemia oscillations, as well as concordant induction of high-frequency insulin oscillations. The basic idea underlying this model is that the pancreas behaves like a population of independent controllers (each consisting of a fundamental secreting unit, a pancreatic islet), all reacting to the same glucose stimulus, but with varying perfomance characteristics. This idea has been supported by a relatively wide range of simulations, aiming to replicate most important in vivo experiments concerning pancreatic insulin release. It will be shown in this note that the same mathematical structure can also replicate a set of in vitro experiments, provided that the model context is adapted to the structure of the different experiments to be simulated. More in details, the model will be shown to reproduce the double phase of insulin release during a prolonged glucose stimulus: A first phase of impulsive insulin release, immediately upon glucose administration, and a second phase of more gradual release, dependent on the potentiation effect of the secretory units. ©2013 IEEE

Future internet architecture: Control-based perspectives related to Quality of Experience (QoE) management

This paper presents the Future Internet Architecture developed in the framework of the Platino project, focusing on the Quality of Experience (QoE) Management; the presented architecture is fully compliant with the concepts developed in the framework of the FP7 PPP Future Internet initiative and in the projects related to Software Defined Networks (SDNs) and Network Function Virtualization (NFV). The paper highlights the proposed underlying innovations of the architecture related to the Orchestration functionalities; in particular, the developments related to such innovations offer a plenty of opportunities for applying control based, operation research and optimization techniques

A future internet interface to control programmable networks

current internet infrastructure is still configured and managed manually or adopting a limited level of automation. The Future Internet aims to provide the network resources as a service to ease the process of automatic designing, controlling and supervising the telecommunication infrastructure. A key enabler of the Future Internet is the virtualization of the available resources and of the related functionalities. The widespread of cloud computing, Software Defined Network (SDN) and Network Function Virtualization (NFV) technologies opened the way for a total control of programmable networks. Many open and commercial implementations have adopted this paradigm, but they expose a fragmented set of dissimilar interfaces that often offer similar or even overlapping functionalities. The result is that uncontrolled, open-loop routines and procedures still require a manual intervention. In this paper, we describe an open interface and its reference implementation, to control programmable networks adopting a novel, closed-loop approach based on end-users feedbacks. The proposed interface has been implemented as a Future Internet Generic Enabler named OFNIC

A Q-Learning based approach to Quality of Experience control in cognitive Future Internet networks

The paper describes an innovative and fully cognitive approach which offers the opportunity to cope with some key limitations of the present telecommunication networks by means of the introduction of a novel architecture design in the perspective of the emerging Future Internet framework. Within this architecture, the Quality of Experience (QoE) Management functionalities are aimed at approaching the desired QoE level of the applications by dynamically selecting the most appropriate Class of Service supported by the network. In the present work, this selection is driven by an optimal and adaptive control strategy based on the renowned Q-Learning algorithm. The proposed dynamic approach differs from the traffic classification approaches found in the literature, where a static assignment of Classes of Service to applications is performed

A multi-agent reinforcement learning based approach to quality of experience control in future internet networks

In the perspective of the emerging Future Internet framework, the Quality of Experience (QoE) Control functionalities are aimed at approaching the desired QoE level of the applications by dynamically selecting the most appropriate Classes of Service supported by the network. In the present work, this selection is driven by Multi-Agent Reinforcement Learning, namely by the Friend-Q learning algorithm. The proposed dynamic approach differs from the traffic classification approaches found in the literature, where a static assignment of Classes of Service to application instances is performed. All these improvements are aimed at adding a cognition loop to telecommunication networks, by making use of Multi-Agent Reinforcement Learning, and at fostering the intelligent connectivity between applications and networks