1,720,969 research outputs found
A lexicographic approach to constrained MDP admission control
No full textThis 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
Full text linkThis 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
No full textThis 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
Full text linkThe 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
No full textThe 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
A resource allocation algorithm of multi-cloud resources based on Markov Decision Process
No full textCloud technologies can nowadays be considered as commodities. The possibility of getting access to storage, computing and networking virtual resources empowers any business that needs dynamic IT capabilities. The Cloud Management Broker (CMB) plays a crucial role to handle heterogeneous virtualized cloud resources in order to offer a unique set of interfaces to the cloud users. Moreover, the CMB is in charge of optimizing the usage of the cloud resources, satisfying the requirements declared by the users. This paper proposes a novel multi-cloud resource allocation algorithm, based on a Markov Decision Process (MDP), capable of dynamically assigning the resources requests to a set of IT resources (storage or computing resources), with the aim of maximizing the expected CMB revenue. Simulation results show the feasibility and the higher performances obtained by the proposed algorithm, compared to a greedy approach. ?????? 2013 IEEE
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
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