The Experts below are selected from a list of 3945 Experts worldwide ranked by ideXlab platform

Chunming Qiao - One of the best experts on this subject based on the ideXlab platform.

  • Virtual network mapping for multicast services with max-min fairness of reliability
    IEEE OSA Journal of Optical Communications and Networking, 2015
    Co-Authors: Zilong Ye, Weida Zhong, Yangming Zhao, Hongfang Yu, Chunming Qiao
    Abstract:

    Network function virtualization (NFV) provides an effective way to reduce the network provider's cost by allowing multiple virtual networks (VNs) to share the underlying physical infrastructure. In the NFV environment, especially when supporting multicast services over the VNs, reliability is a critical requirement since the failure of one virtual node can cause the malfunction of multiple nodes that receive multicasting data from it. In this paper, we study for the first time to the best of our knowledge how to efficiently map VNs for multicast services over both general IP networks and orthogonal frequency division multiplexing (OFDM)-based elastic optical networks (EONs) while taking into consideration the max-min fairness in terms of reliability among distinct VNs. For general IP networks, we propose a mixed integer linear programming (MILP) model to determine the upper bound on the reliability with max-min fairness. In addition, an efficient heuristic, namely a reliability-aware genetic (RAG) algorithm, is developed to address reliable multicast VN mapping with a low computational complexity. By encoding multicast tree construction and link mapping into the process of path selection, taking into consideration the reliability with max-min fairness, and the networking reliability factors during mutation, RAG can globally optimize the reliability and fairness of all the multicast VN requests. For OFDM-based EONs, we extend the MILP (RAG) to optical-MILP [(O-MILP) optical RAG (O-RAG)] by considering the most efficient modulation format selection strategy, spectrum continuity, and conflict constraints. Through extensive simulations, we demonstrate that RAG (O-RAG) achieves close to the optimal reliability fairness with a much lower time complexity than the MILP (O-MILP) model. In particular, the path reliability-based mutation strategy in RAG (O-RAG) yields a significant performance improvement over other heuristic solutions in terms of reliability fairness, bandwidth (spectrum) consumption, and transmission delay.

  • Virtual Network Mapping for Reliable Multicast Services with max-min fairness
    2015 IEEE Global Communications Conference (GLOBECOM), 2015
    Co-Authors: Weida Zhong, Zilong Ye, Yangming Zhao, Hongfang Yu, Chunming Qiao
    Abstract:

    Network Function Virtualization (NFV) provides an effective way to reduce the network provider's cost by allowing multiple Virtual Networks (VNs) to share the underlying physical infrastructure. In the NFV environment, especially when supporting multicast service over the VNs, reliability is a critical requirement in the process of VN mapping since the failure of one virtual node can cause the malfunction of all the subsequent nodes that receive multicasting data from it. In this paper, for the first time, we study how to efficiently map VNs for reliable multicast services, while taking into consideration the max-min fairness of the reliability among distinct VNs. We propose a Mixed Integer Linear Programming (MILP) model to determine the upper bound on the max-min fairness reliability. In addition, an efficient heuristic, namely Uniform Reliability Mutation based Genetic (URMG) algorithm, is developed to address reliable multicast VN mapping with a low computational complexity. By encoding multicast tree construction and link mapping into path selection, taking into consideration the max-min reliability fairness goal, and the networking reliability factors during mutation, URMG can globally optimize the reliability and its fairness of all the multicast VN requests. Through extensive simulations, we demonstrate that URMG achieves close to the optimal reliability fairness with a much lower time complexity than the MILP and yields a significant performance improvement in terms of reliability fairness, bandwidth consumption and transmission delay comparing with other heuristic solutions.

Yuhao Wang - One of the best experts on this subject based on the ideXlab platform.

  • Robust Max–Min fairness Resource Allocation in Sensing-Based Wideband Cognitive Radio With SWIPT: Imperfect Channel Sensing
    IEEE Systems Journal, 2018
    Co-Authors: Fuhui Zhou, Julian Cheng, Norman C. Beaulieu, Yuhao Wang
    Abstract:

    fairness among different users and energy utilization are key issues in the future communication network design. Robust max-min fairness resource allocation in sensing-based wideband cognitive radio with simultaneous wireless information and power transfer is studied when spectrum sensing and channel state information are imperfect. A worst-case throughput is maximized by jointly optimizing the sensing time, transmit power, and subchannel allocation under the worst-case channel state information error model, subject to constraints on energy harvesting, interference power, and transmit power. Two operation paradigms for cognitive radio are considered, namely, opportunistic spectrum access and sensing-based spectrum sharing. The formulated robust max-min fairness resource allocation problems are mixed-integer and nonconvex programming with infinite inequality constraints. An efficient one-dimensional search algorithm is designed based on the proposed transmit power and subchannel allocation scheme. Simulation results show that the secondary user under sensing-based spectrum sharing can obtain a performance gain compared with that under opportunistic spectrum access at the cost of implementation complexity. Design tradeoffs are identified and discussed.

  • GLOBECOM - Resource Allocation in Wideband Cognitive Radio with SWIPT: max-min fairness Guarantees
    2016 IEEE Global Communications Conference (GLOBECOM), 2016
    Co-Authors: Fuhui Zhou, Zan Li, Norman C. Beaulieuz, Julian Cheng, Yuhao Wang
    Abstract:

    Abstract-A max-min fairness resource allocation is studied for wideband cognitive radio under sensing-based spectrum sharing with simultaneous wireless information and power transfer. Specifically, the throughput of the worse-case secondary user is maximized by jointly optimizing the sensing time, transmit power and subchannel allocation, subject to constraints on energy harvesting, interference power and transmit power. The formulated max-min fairness resource allocation problem is a mixed integer non-convex programming. An efficient one-dimensional search algorithm based on the proposed transmit power and subchannel allocation scheme is designed to solve the formulated problem. Several tradeoffs are found, such as a tradeoff between the sensing performance and the throughput of the secondary user under a max-min fairness criterion.

  • Resource Allocation in Wideband Cognitive Radio with SWIPT: max-min fairness Guarantees
    2016 IEEE Global Communications Conference (GLOBECOM), 2016
    Co-Authors: Fuhui Zhou, Zan Li, Norman C. Beaulieuz, Julian Cheng, Yuhao Wang
    Abstract:

    A max-min fairness resource allocation is studied for wideband cognitive radio under sensing-based spectrum sharing with simultaneous wireless information and power transfer. Specifically, the throughput of the worse-case secondary user is maximized by jointly optimizing the sensing time, transmit power and subchannel allocation, subject to constraints on energy harvesting, interference power and transmit power. The formulated max-min fairness resource allocation problem is a mixed integer non-convex programming. An efficient one-dimensional search algorithm based on the proposed transmit power and subchannel allocation scheme is designed to solve the formulated problem. Several tradeoffs are found, such as a tradeoff between the sensing performance and the throughput of the secondary user under a max-min fairness criterion.

P. Marbach - One of the best experts on this subject based on the ideXlab platform.

  • Priority service and max-min fairness
    IEEE ACM Transactions on Networking, 2003
    Co-Authors: P. Marbach
    Abstract:

    We study a priority service where users are free to choose the priority of their traffic, but are charged accordingly by the network. We assume that each user chooses priorities to maximize its own net benefit, and model the resulting interaction among users as a noncooperative game. We show that there exists an unique equilibrium for this game and that in equilibrium the bandwidth allocation is weighted max-min fair.

  • INFOCOM - Priority service and max-min fairness
    Proceedings.Twenty-First Annual Joint Conference of the IEEE Computer and Communications Societies, 2002
    Co-Authors: P. Marbach
    Abstract:

    We study a pricing scheme for networks which use priorities to provide differentiated quality of service. We consider the situation where users are free to choose the priority of their traffic, but are charged accordingly. We model this situation as a non-cooperative game, where users behave in a selfish manner and choose an allocation of priorities to packets to optimize their own net benefit. We show that there exists an unique equilibrium for this game and the bandwidth allocation in equilibrium is weighted max-min fair.

  • Priority service and max-min fairness
    Proceedings.Twenty-First Annual Joint Conference of the IEEE Computer and Communications Societies, 2002
    Co-Authors: P. Marbach
    Abstract:

    We study a pricing scheme for networks which use priorities to provide differentiated quality of service. We consider the situation where users are free to choose the priority of their traffic, but are charged accordingly. We model this situation as a non-cooperative game, where users behave in a selfish manner and choose an allocation of priorities to packets to optimize their own net benefit. We show that there exists an unique equilibrium for this game and the bandwidth allocation in equilibrium is weighted max-min fair.

Fuhui Zhou - One of the best experts on this subject based on the ideXlab platform.

  • Robust Max–Min fairness Resource Allocation in Sensing-Based Wideband Cognitive Radio With SWIPT: Imperfect Channel Sensing
    IEEE Systems Journal, 2018
    Co-Authors: Fuhui Zhou, Julian Cheng, Norman C. Beaulieu, Yuhao Wang
    Abstract:

    fairness among different users and energy utilization are key issues in the future communication network design. Robust max-min fairness resource allocation in sensing-based wideband cognitive radio with simultaneous wireless information and power transfer is studied when spectrum sensing and channel state information are imperfect. A worst-case throughput is maximized by jointly optimizing the sensing time, transmit power, and subchannel allocation under the worst-case channel state information error model, subject to constraints on energy harvesting, interference power, and transmit power. Two operation paradigms for cognitive radio are considered, namely, opportunistic spectrum access and sensing-based spectrum sharing. The formulated robust max-min fairness resource allocation problems are mixed-integer and nonconvex programming with infinite inequality constraints. An efficient one-dimensional search algorithm is designed based on the proposed transmit power and subchannel allocation scheme. Simulation results show that the secondary user under sensing-based spectrum sharing can obtain a performance gain compared with that under opportunistic spectrum access at the cost of implementation complexity. Design tradeoffs are identified and discussed.

  • GLOBECOM - Resource Allocation in Wideband Cognitive Radio with SWIPT: max-min fairness Guarantees
    2016 IEEE Global Communications Conference (GLOBECOM), 2016
    Co-Authors: Fuhui Zhou, Zan Li, Norman C. Beaulieuz, Julian Cheng, Yuhao Wang
    Abstract:

    Abstract-A max-min fairness resource allocation is studied for wideband cognitive radio under sensing-based spectrum sharing with simultaneous wireless information and power transfer. Specifically, the throughput of the worse-case secondary user is maximized by jointly optimizing the sensing time, transmit power and subchannel allocation, subject to constraints on energy harvesting, interference power and transmit power. The formulated max-min fairness resource allocation problem is a mixed integer non-convex programming. An efficient one-dimensional search algorithm based on the proposed transmit power and subchannel allocation scheme is designed to solve the formulated problem. Several tradeoffs are found, such as a tradeoff between the sensing performance and the throughput of the secondary user under a max-min fairness criterion.

  • Robust max-min fairness Energy Harvesting in Secure MISO Cognitive Radio With SWIPT
    arXiv: Information Theory, 2016
    Co-Authors: Fuhui Zhou, Zan Li, Julian Cheng, Qunwei Li, Jiangbo Si
    Abstract:

    A multiple-input single-output cognitive radio downlink network is studied with simultaneous wireless information and power transfer. In this network, a secondary user coexists with multiple primary users and multiple energy harvesting receivers. In order to guarantee secure communication and energy harvesting, the problem of robust secure artificial noise-aided beamforming and power splitting design is investigated under imperfect channel state information. Specifically, the max-min fairness energy harvesting problem is formulated under the bounded channel state information error model. A one-dimensional search algorithm based on ${\cal S}\text{-Procedure} $ is proposed to solve the problem. It is shown that the optimal robust secure beamforming can be achieved. A tradeoff is elucidated between the secrecy rate of the secondary user receiver and the energy harvested by the energy harvesting receivers under a max-min fairness criterion.

  • Resource Allocation in Wideband Cognitive Radio with SWIPT: max-min fairness Guarantees
    2016 IEEE Global Communications Conference (GLOBECOM), 2016
    Co-Authors: Fuhui Zhou, Zan Li, Norman C. Beaulieuz, Julian Cheng, Yuhao Wang
    Abstract:

    A max-min fairness resource allocation is studied for wideband cognitive radio under sensing-based spectrum sharing with simultaneous wireless information and power transfer. Specifically, the throughput of the worse-case secondary user is maximized by jointly optimizing the sensing time, transmit power and subchannel allocation, subject to constraints on energy harvesting, interference power and transmit power. The formulated max-min fairness resource allocation problem is a mixed integer non-convex programming. An efficient one-dimensional search algorithm based on the proposed transmit power and subchannel allocation scheme is designed to solve the formulated problem. Several tradeoffs are found, such as a tradeoff between the sensing performance and the throughput of the secondary user under a max-min fairness criterion.

Michal Segalov - One of the best experts on this subject based on the ideXlab platform.

  • Upward max-min fairness
    Journal of the ACM, 2017
    Co-Authors: Emilie Danna, Avinatan Hassidim, Haim Kaplan, Alok Kumar, Yishay Mansour, Michal Segalov
    Abstract:

    Often one would like to allocate shared resources in a fair way. A common and well-studied notion of fairness is max-min fairness, where we first maximize the smallest allocation, and subject to that the second smallest, and so on. We consider a networking application where multiple commodities compete over the capacity of a network. In our setting, each commodity has multiple possible paths to route its demand (for example, a network using Multiprotocol Label Switching (MPLS) tunneling). In this setting, the only known way of finding a max-min fair allocation requires an iterative solution of multiple linear programs. Such an approach, although polynomial time, scales badly with the size of the network, the number of demands, and the number of paths, and is hard to implement in a distributed environment. More importantly, a network operator has limited control and understanding of the inner working of the algorithm. In this article we introduce Upward max-min fairness, a novel relaxation of max-min fairness, and present a family of simple dynamics that converge to it. These dynamics can be implemented in a distributed manner. Moreover, we present an efficient combinatorial algorithm for finding an upward max-min fair allocation. This algorithm is a natural extension of the well-known Water Filling Algorithm for a multiple path setting. We test the expected behavior of this new algorithm and show that on realistic networks upward max-min fair allocations are comparable to the max-min fair allocations both in fairness and in network utilization.

  • Upward Max Min fairness
    2012 Proceedings IEEE INFOCOM, 2012
    Co-Authors: Emilie Danna, Avinatan Hassidim, Haim Kaplan, Alok Kumar, Yishay Mansour, Michal Segalov
    Abstract:

    Often one would like to allocate shared resources in a fair way. A common and well studied notion of fairness is max-min fairness, where we first maximize the smallest allocation, and subject to that the second smallest, and so on. We consider a networking application where multiple commodities compete over the capacity of a network. In our setting each commodity has multiple possible paths to route its demand (for example, a network using MPLS tunneling). In this setting, the only known way of finding a max-min fair allocation requires an iterative solution of multiple linear programs. Such an approach, although polynomial time, scales badly with the size of the network, the number of demands, and the number of paths. More importantly, a network operator has limited control and understanding of the inner working of the algorithm. Finally, this approach is inherently centralized and cannot be implemented via a distributed protocol. In this paper we introduce Upward max-min fairness, a novel relaxation of max-min fairness and present a family of simple dynamics that converge to it. These dynamics can be implemented in a distributed manner. Moreover, we present an efficient combinatorial algorithm for finding an upward max-min fair allocation, which is a natural extension of the well known Water Filling Algorithm for a multiple path setting. We test the expected behavior of this new algorithm and show that on realistic networks upward max-min fair allocations are comparable to the max-min fair allocations both in fairness and in network utilization.

  • INFOCOM - Upward Max Min fairness
    2012 Proceedings IEEE INFOCOM, 2012
    Co-Authors: Emilie Danna, Avinatan Hassidim, Haim Kaplan, Alok Kumar, Yishay Mansour, Michal Segalov
    Abstract:

    Often one would like to allocate shared resources in a fair way. A common and well studied notion of fairness is max-min fairness, where we first maximize the smallest allocation, and subject to that the second smallest, and so on. We consider a networking application where multiple commodities compete over the capacity of a network. In our setting each commodity has multiple possible paths to route its demand (for example, a network using MPLS tunneling). In this setting, the only known way of finding a max-min fair allocation requires an iterative solution of multiple linear programs. Such an approach, although polynomial time, scales badly with the size of the network, the number of demands, and the number of paths. More importantly, a network operator has limited control and understanding of the inner working of the algorithm. Finally, this approach is inherently centralized and cannot be implemented via a distributed protocol.