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"... Abstract—In this paper, we present a theoretical framework and a distributed mechanism for fair bandwidth allocation on a network with various bottleneck links. In our model, a user is guaranteed a minimum bandwidth and charged a price for a bandwidth capacity request. We defined a utility function ..."
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Abstract—In this paper, we present a theoretical framework and a distributed mechanism for fair bandwidth allocation on a network with various bottleneck links. In our model, a user is guaranteed a minimum bandwidth and charged a price for a bandwidth capacity request. We defined a utility function that reflects user's bandwidth demand when the user requests the bandwidth capacity. We then present a non-cooperative game with social welfare function to resolve users ' conflicting bandwidth capacity requests at bottleneck links. We also show that our proposed game-theoretic solution guarantees fair bandwidth allocation as defined in our residual capacity fairness. In order to guarantee the minimum bandwidth requirement, we integrate an admission control in our solution. However, global optimal admission conditions are not easy to implement for large networks. We therefore propose a distributed admission scheme. As a result, the paper presents fair and practical distributed algorithms for bandwidth allocation and admission control in enterprise networks. Our simulation and evaluation study show that the distributed approach is sufficiently close to the global optimal solution. Index Terms — bandwidth allocation, fairness, pricing, bandwidth capacity, admission control.
Sampling Techniques for Zero-sum, Discounted Markov Games
"... In this paper, we first present a key approximation result for zero-sum, discounted Markov games, providing bounds on the state-wise loss and the loss in the sup norm resulting from using approximate Q-functions. Then we extend the policy rollout technique for MDPs to Markov games. Using our key app ..."
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In this paper, we first present a key approximation result for zero-sum, discounted Markov games, providing bounds on the state-wise loss and the loss in the sup norm resulting from using approximate Q-functions. Then we extend the policy rollout technique for MDPs to Markov games. Using our key approximation result, we prove that, under certain conditions, the rollout technique gives rise to a policy that is closer to the Nash equilibrium than the base policy. We also use our key result to provide an alternative analysis of a second sampling approach to Markov games known as sparse sampling. Our analysis implies the (already known) result that, under certain conditions, the policy generated by the sparse-sampling algorithm is close to the Nash equilibrium. We prove that the amount of sampling that guarantees these results is independent of the state-space--size of the Markov game.
Approximation Results On Sampling Techniques For Zero-Sum Discounted Markov Games
"... In this paper, we first present a key approximation result for zero-sum, discounted Markov games, providing bounds on the state-wise loss and the loss in the sup norm resulting from using approximate Q-functions (e.g., Q-functions estimated by sampling). Then, we extend the policy rollout sampling t ..."
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In this paper, we first present a key approximation result for zero-sum, discounted Markov games, providing bounds on the state-wise loss and the loss in the sup norm resulting from using approximate Q-functions (e.g., Q-functions estimated by sampling). Then, we extend the policy rollout sampling technique for MDPs to Markov games. Using our key approximation result, we prove that under certain conditions, the resulting rollout technique for games gives rise to a policy that is closer to the Nash equilibrium than the base policy, using an amount of sampling completely independent of the state space size. We also use our key result to provide an alternative to a published analysis of a second sampling approach to Markov games known as "sparse sampling". Thus, our theorem implies the (already known) result that, under certain conditions, the policy generated by the sparse-sampling algorithm is close to the Nash equilibrium. Again, the amount of sampling that guarantees the result is independent of the size of the state space of the Markov game. We also provide simulation results to demonstrate the practicality of our extension of the rollout technique.
Fair Bandwidth Allocation Under User Capacity Constraints
"... Abstract—This paper presents a theoretic framework and evaluation for centralized and distributed schemes of fair bandwidth allocation on a network with various bottleneck links. In our model, a network user is charged a price for requiring a certain bandwidth capacity with a guaranteed minimum band ..."
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Abstract—This paper presents a theoretic framework and evaluation for centralized and distributed schemes of fair bandwidth allocation on a network with various bottleneck links. In our model, a network user is charged a price for requiring a certain bandwidth capacity with a guaranteed minimum bandwidth. We define a utility function to capture user's bandwidth demand with the user capacity requirements. A noncooperative game with social welfare function is proposed to solve users ' conflicting bandwidth demands at the bottleneck links. We propose residual capacity fairness and developed a gametheoretic solution to achieve fair bandwidth allocation that satisfies the residual capacity fairness criterion. In order to satisfy the guaranteed minimum bandwidth requirements of flows, admission control is integrated in our solution to the game. The global admission conditions are not easy to implement for large networks and therefore distributed admission scheme is proposed. The paper presents distributed algorithms that are fair and practical for bandwidth allocation with admission control. Our simulation results show that our distributed approach suffici ently close to the centralized solution.
Fair Admission Control to Achieve Guaranteed Bandwidth Allocation
"... In this paper, we present a theoretic framework for admission controls and bandwidth allocations at network links to achieve guaranteed bandwidth allocations, which guarantee to allocate admitted flows data rates that are above their required minimum bandwidths. The admission control and bandwidth a ..."
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In this paper, we present a theoretic framework for admission controls and bandwidth allocations at network links to achieve guaranteed bandwidth allocations, which guarantee to allocate admitted flows data rates that are above their required minimum bandwidths. The admission control and bandwidth allocation are devised from the optimal solution of maximizing aggregate social welfare of the network. We define a utility function to capture network user’s demand for guaranteed bandwidth requirements with a price charged by a network service provider at the edge of the network. A fairness criterion is introduced for network links to allocate bandwidths. We first consider global admission conditions, which are deduced from the social welfare maximization problem, and then present distributed admission conditions, which can be used by each network link to make admission decisions locally. The bandwidth allocation resulted from the distributed admission conditions is asymptotically optimal with respect to the bandwidth allocation resulted from the global admission conditions. We show that the admission control framework can provide guidance for network service providers to charge users that require guaranteed bandwidths for data transmissions. 1.
