The Experts below are selected from a list of 13287 Experts worldwide ranked by ideXlab platform
Xavier Costa-pérez - One of the best experts on this subject based on the ideXlab platform.
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Network Slicing Games: Enabling Customization in Multi-Tenant Mobile Networks
IEEE ACM Transactions on Networking, 2019Co-Authors: Pablo Caballero, Gustavo De Veciana, Albert Banchs, Xavier Costa-pérezAbstract:Network slicing to enable resource sharing among multiple tenants-network operators and/or services-is considered as a key functionality for next generation mobile networks. This paper provides an analysis of a well-known model for resource sharing, the share-constrained Proportional Allocation mechanism, to realize network slicing. This mechanism enables tenants to reap the performance benefits of sharing, while retaining the ability to customize their own users' Allocation. This results in a network slicing game in which each tenant reacts to the user Allocations of the other tenants so as to maximize its own utility. We show that, for elastic traffic, the game associated with such strategic behavior converges to a Nash equilibrium. At the Nash equilibrium, a tenant always achieves the same or better performance than that of a static partitioning of resources, thus providing the same level of protection as static partitioning. We further analyze the efficiency and fairness of the resulting Allocations, providing tight bounds for the price of anarchy and envy-freeness. Our analysis and extensive simulation results confirm that the mechanism provides a comprehensive practical solution to realize network slicing. Our theoretical results also fills a gap in the analysis of this resource Allocation model under strategic players.
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Network slicing games: Enabling customization in multi-tenant networks
IEEE INFOCOM 2017 - IEEE Conference on Computer Communications, 2017Co-Authors: Pablo Caballero, Gustavo De Veciana, Albert Banchs, Xavier Costa-pérezAbstract:Network slicing to enable resource sharing among multiple tenants-network operators and/or services-is considered a key functionality for next generation mobile networks. This paper provides an analysis of a well-known model for resource sharing, the `share-constrained Proportional Allocation' mechanism, to realize network slicing. This mechanism enables tenants to reap the performance benefits of sharing, while retaining the ability to customize their own users' Allocation. This results in a network slicing game in which each tenant reacts to the user Allocations of the other tenants so as to maximize its own utility. We show that, under appropriate conditions, the game associated with such strategic behavior converges to a Nash equilibrium. At the Nash equilibrium, a tenant always achieves the same, or better, performance than under a static partitioning of resources, hence providing the same level of protection as such static partitioning. We further analyze the efficiency and fairness of the resulting Allocations, providing tight bounds for the price of anarchy and envy-freeness. Our analysis and extensive simulation results confirm that the mechanism provides a comprehensive practical solution to realize network slicing. Our theoretical results also fill a gap in the literature regarding the analysis of this resource Allocation model under strategic players.
Jiakuan Chen - One of the best experts on this subject based on the ideXlab platform.
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sex specific plasticity of reproductive Allocation in response to water depth in a clonal dioecious macrophyte
American Journal of Botany, 2019Co-Authors: Spencer C H Barrett, Zhiping Song, Jiakuan ChenAbstract:PREMISE OF THE STUDY Sex-specific differences in reproductive investment contribute to sexual dimorphism in dioecious plants. Along environmental gradients, males and females may plastically adjust reproductive Allocation differently because of contrasting reproductive costs. In dioecious macrophytes, variation in water depth is likely to influence reproductive Allocation but has not been investigated in detail. METHODS Vallisneria spinulosa was grown in aquatic mesocosms at water depths of 50, 100 and 150 cm for 14 weeks. Plasticity in Allocation was measured to investigate whether sexual dimorphism in reproductive Allocation and vegetative growth changed in response to varying water depths. KEY RESULTS Females invested a higher fraction of resources to sexual reproduction than males across all water depths and decreased Proportional Allocation to sexual structures in shallow and deep water compared to intermediate water depth. In contrast, males maintained similar sexual Allocation across all water depths. Females displayed larger vegetative size than males, despite greater sexual investment, but decreased vegetative biomass more than males in shallow or deep water. The sexes invested similarly in clonal propagation by tubers at all water depths, but a trade-off with sexual reproduction was only evident in females. CONCLUSIONS Our results suggest that females of V. spinulosa have mechanisms to compensate for the costs of sexual reproduction in heterogeneous environments. Compared to males, females expressed greater plasticity in biomass allocated to sexual reproduction and vegetative growth in response to water depth variation. Environmental variation in underwater light availability probably caused the sex-specific Allocation strategies found in V. spinulosa.
Pablo Caballero - One of the best experts on this subject based on the ideXlab platform.
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Network Slicing Games: Enabling Customization in Multi-Tenant Mobile Networks
IEEE ACM Transactions on Networking, 2019Co-Authors: Pablo Caballero, Gustavo De Veciana, Albert Banchs, Xavier Costa-pérezAbstract:Network slicing to enable resource sharing among multiple tenants-network operators and/or services-is considered as a key functionality for next generation mobile networks. This paper provides an analysis of a well-known model for resource sharing, the share-constrained Proportional Allocation mechanism, to realize network slicing. This mechanism enables tenants to reap the performance benefits of sharing, while retaining the ability to customize their own users' Allocation. This results in a network slicing game in which each tenant reacts to the user Allocations of the other tenants so as to maximize its own utility. We show that, for elastic traffic, the game associated with such strategic behavior converges to a Nash equilibrium. At the Nash equilibrium, a tenant always achieves the same or better performance than that of a static partitioning of resources, thus providing the same level of protection as static partitioning. We further analyze the efficiency and fairness of the resulting Allocations, providing tight bounds for the price of anarchy and envy-freeness. Our analysis and extensive simulation results confirm that the mechanism provides a comprehensive practical solution to realize network slicing. Our theoretical results also fills a gap in the analysis of this resource Allocation model under strategic players.
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Network slicing games: Enabling customization in multi-tenant networks
IEEE INFOCOM 2017 - IEEE Conference on Computer Communications, 2017Co-Authors: Pablo Caballero, Gustavo De Veciana, Albert Banchs, Xavier Costa-pérezAbstract:Network slicing to enable resource sharing among multiple tenants-network operators and/or services-is considered a key functionality for next generation mobile networks. This paper provides an analysis of a well-known model for resource sharing, the `share-constrained Proportional Allocation' mechanism, to realize network slicing. This mechanism enables tenants to reap the performance benefits of sharing, while retaining the ability to customize their own users' Allocation. This results in a network slicing game in which each tenant reacts to the user Allocations of the other tenants so as to maximize its own utility. We show that, under appropriate conditions, the game associated with such strategic behavior converges to a Nash equilibrium. At the Nash equilibrium, a tenant always achieves the same, or better, performance than under a static partitioning of resources, hence providing the same level of protection as such static partitioning. We further analyze the efficiency and fairness of the resulting Allocations, providing tight bounds for the price of anarchy and envy-freeness. Our analysis and extensive simulation results confirm that the mechanism provides a comprehensive practical solution to realize network slicing. Our theoretical results also fill a gap in the literature regarding the analysis of this resource Allocation model under strategic players.
Giacomo Como - One of the best experts on this subject based on the ideXlab platform.
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generalized Proportional Allocation policies for robust control of dynamical flow networks
IEEE Transactions on Automatic Control, 2020Co-Authors: Gustav Nilsson, Giacomo ComoAbstract:We study a robust control problem for dynamical flow networks. In the considered framework, traffic flows along the links of a transportation network —modeled as a capacited multigraph— and queues up at the nodes, whereby control policies determine which incoming queues are to be allocated service simultaneously, within some predetermined scheduling constraints. We first prove fundamental performance limitations on the system performance by showing that for a dynamical flow network to be stabilizable by some control policy it is necessary that the exogenous inflows belong to a certain stability region, that is determined by the network topology, the link capacities, and the scheduling constraints. Then, we introduce a family of distributed controls, referred to as Generalized Proportional Allocation (GPA) policies, and prove that they stabilize a dynamical transportation network whenever the exogenous inflows belong to such stability region. The proposed GPA control policies are decentralized and fully scalable as they rely on local feedback information only. Differently from previously studied maximally stabilizing control strategies, the GPA control policies do not require any global information about the network topology, the exogenous inflows, or the routing, which makes them robust to unpredicted network load variations and changes in the link capacities or the routing decisions. Moreover, the proposed GPA control policies also take into account the overhead time while switching between services. Our theoretical results find one application in the control of urban traffic networks with signalized intersections, where vehicles have to queue up at junctions and the traffic signal controls determine the green light Allocation to the different incoming lanes.
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On Generalized Proportional Allocation Policies for Traffic Signal Control
IFAC-PapersOnLine, 2017Co-Authors: Gustav Nilsson, Giacomo ComoAbstract:The fast-increasing demand and relatively slow growth of infrastructure capacity are providing a strong motivation for research in real-time urban traffic controls that make the best use of novel sensing in order to increase efficiency and resilience of the transportation system. In our contribution, we focus on a class of dynamic feedback traffic signal control policies that are based on a generalized Proportional Allocation rule. The proposed traffic signal controls are decentralized (they make use of local information only), scalable (they are independent of the network size and topology), and universal (they do not rely on any information about external inflows or turning ratios). In spite of their fully distributed nature, we prove that such control policies achieve a global objective, maximum throughput, in that they stabilize the urban traffic network whenever possible under the given capacity constraints. The traffic model we consider consists in a network of interconnected vertical queues with deterministic dynamics driven by physical laws (conservation of mass and preservation of non-negativity of the traffic volumes) as well as scheduling constraints (described as a set of phases, each phase consisting in a subset of lanes that can be be given green light simultaneously). This results in a differential inclusion for which we prove existence and, in the special case of orthogonal phases, uniqueness of continuous solutions via a generalization of the reflection principle. Stability is then proved by interpreting the generalized Proportional Allocation controllers as minimizers of a certain entropy-like function that is then used as a Lyapunov function for the closed-loop system. (Less)
Tamer Başar - One of the best experts on this subject based on the ideXlab platform.
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Efficient signal Proportional Allocation (ESPA) mechanisms: decentralized social welfare maximization for divisible resources
IEEE Journal on Selected Areas in Communications, 2006Co-Authors: R. Maheswaran, Tamer BaşarAbstract:We address the problem of devising efficient decentralized Allocation mechanisms for a divisible resource, which is critical to many technological domains such as traffic management on the Internet and bandwidth Allocation to agents in ad hoc wireless networks. We introduce a class of efficient signal Proportional Allocation (ESPA) mechanisms that yields an Allocation which maximizes social welfare with minimal signaling and computational requirements for the resource. Revenue limits for this class are obtained and a sequence of schemes that approach these limits arbitrarily closely are given. We also present a locally stable negotiation scheme applicable to the entire class and illustrate efficiency and revenue properties through simulation.
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On Revenue Generation When Auctioning Network Resources
Proceedings of the 44th IEEE Conference on Decision and Control, 2005Co-Authors: Rajiv Maheswaran, Tamer BaşarAbstract:While efficiency of mechanisms for control of communication networks has been extensively investigated, little attention has been paid to the critical metric of revenue generation. In this paper, we pursue such an investigation within a class of Allocation schemes with the minimal signaling and computation costs necessary in communication network domains. We show that, within this space, linear cost rules for Proportional Allocation mechanisms are optimal for symmetric agent populations and reserving a portion of the resource can increase revenue even though less of the resource is being sold. While nonlinear cost functions can be better for asymmetric populations, intelligent agents can undermine this via signal splitting. We show how a resource can counter this phenomenon by declaring a linear cost.
