The Experts below are selected from a list of 250173 Experts worldwide ranked by ideXlab platform
Ananthram Swami - One of the best experts on this subject based on the ideXlab platform.
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a game Theoretic Framework for resource sharing in clouds
2019 12th IFIP Wireless and Mobile Networking Conference (WMNC), 2019Co-Authors: Faheem Zafari, Kin K Leung, Don Towsley, Prithwish Basu, Ananthram SwamiAbstract:Providing resources to different users or applications is fundamental to cloud computing. This is a challenging problem as a cloud service provider may have insufficient resources to satisfy all user requests. Furthermore, allocating available resources optimally to different applications is also challenging. Resource sharing among different cloud service providers can improve resource availability and resource utilization as certain cloud service providers may have free resources available that can be “rented” by other service providers. However, different cloud service providers can have different objectives or utilities. Therefore, there is a need for a Framework that can share and allocate resources in an efficient and effective way, while taking into account the objectives of various service providers that results in a multi-objective optimization problem. In this paper, we present a Cooperative Game Theory (CGT) based Framework for resource sharing and allocation among different service providers with varying objectives that form a coalition. We show that the resource sharing problem can be modeled as an N–player canonical cooperative game with non-transferable utility (NTU) and prove that the game is convex for monotonic non-decreasing utilities. We propose an $\mathcal{O}\left( N \right)$ algorithm that provides an allocation from the core, hence guaranteeing Pareto optimality. We evaluate the performance of our proposed resource sharing Framework in a number of simulation settings and show that our proposed Framework improves user satisfaction and utility of service providers.
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a game Theoretic Framework for resource sharing in clouds
arXiv: Multiagent Systems, 2019Co-Authors: Faheem Zafari, Kin K Leung, Don Towsley, Prithwish Basu, Ananthram SwamiAbstract:Providing resources to different users or applications is fundamental to cloud computing. This is a challenging problem as a cloud service provider may have insufficient resources to satisfy all user requests. Furthermore, allocating available resources optimally to different applications is also challenging. Resource sharing among different cloud service providers can improve resource availability and resource utilization as certain cloud service providers may have free resources available that can be ``rented'' by other service providers. However, different cloud service providers can have different objectives or \emph{utilities}. Therefore, there is a need for a Framework that can share and allocate resources in an efficient and effective way, while taking into account the objectives of various service providers that results in a \emph{multi-objective optimization} problem. In this paper, we present a \emph{Cooperative Game Theory} (CGT) based Framework for resource sharing and allocation among different service providers with varying objectives that form a coalition. We show that the resource sharing problem can be modeled as an $N-$player \emph{canonical} cooperative game with \emph{non-transferable utility} (NTU) and prove that the game is convex for monotonic non-decreasing utilities. We propose an $\mathcal{O}({N})$ algorithm that provides an allocation from the \emph{core}, hence guaranteeing \emph{Pareto optimality}. We evaluate the performance of our proposed resource sharing Framework in a number of simulation settings and show that our proposed Framework improves user satisfaction and utility of service providers.
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A Decision-Theoretic Framework for Opportunistic Spectrum Access
IEEE Wireless Communications, 2007Co-Authors: Qing Zhao, Ananthram SwamiAbstract:Built on a hierarchical access structure with primary and secondary users, opportunistic spectrum access improves spectrum efficiency while maintaining compatibility with legacy wireless systems. The basic idea is to allow secondary users to exploit instantaneous spectrum availability while limiting the interference to primary users. In this article, we identify basic components, fundamental trade-offs, and practical constraints in opportunistic spectrum access. We introduce a decision-Theoretic Framework based on the theory of partially observable Markov decision processes. This Framework allows us to systematically tackle the optimal integrated design and quantitatively characterize the interaction between signal processing for opportunity identification and networking for opportunity exploitation. A discussion of open problems, potential applications, and recent developments is also provided.
Volkan Cevher - One of the best experts on this subject based on the ideXlab platform.
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Limits on Support Recovery With Probabilistic Models: An Information-Theoretic Framework
IEEE Transactions on Information Theory, 2017Co-Authors: Jonathan Scarlett, Volkan CevherAbstract:The support recovery problem consists of determining a sparse subset of a set of variables that is relevant in generating a set of observations, and arises in a diverse range of settings, such as compressive sensing, subset selection in regression, and group testing. In this paper, we take a unified approach to support recovery problems, considering general probabilistic models relating a sparse data vector to an observation vector. We study the information-Theoretic limits of both exact and partial support recovery, taking a novel approach motivated by thresholding techniques in channel coding. We provide general achievability and converse bounds characterizing the trade-off between the error probability and number of measurements, and we specialize these to the linear, 1-bit, and group testing models. In several cases, our bounds not only provide matching scaling laws in the necessary and sufficient number of measurements, but also sharp thresholds with matching constant factors. Our approach has several advantages over previous approaches. For the achievability part, we obtain sharp thresholds under broader scalings of the sparsity level and other parameters (e.g., signal-to-noise ratio) compared with several previous works, and for the converse part, we not only provide conditions under which the error probability fails to vanish, but also conditions under which it tends to one.
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limits on support recovery with probabilistic models an information Theoretic Framework
International Symposium on Information Theory, 2015Co-Authors: Jonathan Scarlett, Volkan CevherAbstract:The support recovery problem consists of determining a sparse subset of a set of variables that is relevant in generating a set of observations, and arises in a diverse range of settings such as group testing, compressive sensing, and subset selection in regression. In this paper, we provide a unified approach to support recovery problems, considering general probabilistic observation models relating a sparse data vector to an observation vector. We study the information-Theoretic limits for both exact and partial support recovery, taking a novel approach motivated by thresholding techniques in channel coding. We provide general achievability and converse bounds characterizing the trade-off between the error probability and number of measurements, and we specialize these bounds the linear and 1-bit compressive sensing models. Our conditions not only provide scaling laws, but also explicit matching or near-matching constant factors. Moreover, our converse results not only provide conditions under which the error probability fails to vanish, but also conditions under which it tends to one.
Ren Ping Liu - One of the best experts on this subject based on the ideXlab platform.
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an evolutionary game Theoretic Framework for femtocell radio resource management
IEEE Transactions on Wireless Communications, 2015Co-Authors: Shangjing Lin, Hui Tian, Ren Ping LiuAbstract:Plug-and-play femtocells will be an integrating part of future cellular networks. Resource management and interference mitigation become challenging, suffering from severely delayed network control in large-scale deployments. We propose a new game Theoretic Framework, where fast interference suppression is decoupled from the relatively slow frequency allocation process to tolerate the delayed control. The key idea is to cast femtocell clustering as an outer-loop evolutionary game coupled with bankruptcy channel allocation, which drives the cells to spontaneously switch to less interfered clusters. Within each cluster, we design an inner-loop non-cooperative power control game, such that the requirement of prompt control is eliminated. The two loops interact recursively with analytically confirmed stability. Simulations show that our Framework can improve the throughput by 13.2% in a network of 200 cells, compared to the prior art. The gain grows further with the network size.
Jonathan Scarlett - One of the best experts on this subject based on the ideXlab platform.
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Limits on Support Recovery With Probabilistic Models: An Information-Theoretic Framework
IEEE Transactions on Information Theory, 2017Co-Authors: Jonathan Scarlett, Volkan CevherAbstract:The support recovery problem consists of determining a sparse subset of a set of variables that is relevant in generating a set of observations, and arises in a diverse range of settings, such as compressive sensing, subset selection in regression, and group testing. In this paper, we take a unified approach to support recovery problems, considering general probabilistic models relating a sparse data vector to an observation vector. We study the information-Theoretic limits of both exact and partial support recovery, taking a novel approach motivated by thresholding techniques in channel coding. We provide general achievability and converse bounds characterizing the trade-off between the error probability and number of measurements, and we specialize these to the linear, 1-bit, and group testing models. In several cases, our bounds not only provide matching scaling laws in the necessary and sufficient number of measurements, but also sharp thresholds with matching constant factors. Our approach has several advantages over previous approaches. For the achievability part, we obtain sharp thresholds under broader scalings of the sparsity level and other parameters (e.g., signal-to-noise ratio) compared with several previous works, and for the converse part, we not only provide conditions under which the error probability fails to vanish, but also conditions under which it tends to one.
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limits on support recovery with probabilistic models an information Theoretic Framework
International Symposium on Information Theory, 2015Co-Authors: Jonathan Scarlett, Volkan CevherAbstract:The support recovery problem consists of determining a sparse subset of a set of variables that is relevant in generating a set of observations, and arises in a diverse range of settings such as group testing, compressive sensing, and subset selection in regression. In this paper, we provide a unified approach to support recovery problems, considering general probabilistic observation models relating a sparse data vector to an observation vector. We study the information-Theoretic limits for both exact and partial support recovery, taking a novel approach motivated by thresholding techniques in channel coding. We provide general achievability and converse bounds characterizing the trade-off between the error probability and number of measurements, and we specialize these bounds the linear and 1-bit compressive sensing models. Our conditions not only provide scaling laws, but also explicit matching or near-matching constant factors. Moreover, our converse results not only provide conditions under which the error probability fails to vanish, but also conditions under which it tends to one.
Ekram Hossain - One of the best experts on this subject based on the ideXlab platform.
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a noncooperative game Theoretic Framework for radio resource management in 4g heterogeneous wireless access networks
IEEE Transactions on Mobile Computing, 2008Co-Authors: Dusit Niyato, Ekram HossainAbstract:Fourth generation (4G) wireless networks will provide high-bandwidth connectivity with quality-of-service (QoS) support to mobile users in a seamless manner. In such a scenario, a mobile user will be able to connect to different wireless access networks such as a wireless metropolitan area network (WMAN), a cellular network, and a wireless local area network (WLAN) simultaneously. We present a game-Theoretic Framework for radio resource management (that is, bandwidth allocation and admission control) in such a heterogeneous wireless access environment. First, a noncooperative game is used to obtain the bandwidth allocations to a service area from the different access networks available in that service area (on a long-term basis). The Nash equilibrium for this game gives the optimal allocation which maximizes the utilities of all the connections in the network (that is, in all of the service areas). Second, based on the obtained bandwidth allocation, to prioritize vertical and horizontal handoff connections over new connections, a bargaining game is formulated to obtain the capacity reservation thresholds so that the connection-level QoS requirements can be satisfied for the different types of connections (on a long-term basis). Third, we formulate a noncooperative game to obtain the amount of bandwidth allocated to an arriving connection (in a service area) by the different access networks (on a short-term basis). Based on the allocated bandwidth and the capacity reservation thresholds, an admission control is used to limit the number of ongoing connections so that the QoS performances are maintained at the target level for the different types of connections.
