A scheme of resource allocation and stability for peer–to–peer file–sharing networks

Li, Shiyong; Sun, Wei; Chengguo, E; Shi, Lei

doi:10.1515/amcs-2016-0049

Cited by 6 publications

(3 citation statements)

References 33 publications

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“…There is flourishing literature that relates theory to the pooling system of N servers and the inventory pooling system (Benjaafar, 1995;Buzacott, 1996;Mandelbaum and Reiman, 1998;Nagarajan and Sosic, 2008). Some of these works have focused on application of cooperation in production lines, call centers and networks (Hopp et al, 2004;Aksin et al, 2008;Mariano and Correia, 2015;Li et al, 2016). Gonzalez and Herrero (2004) deal with a cost allocation problem arising from sharing a medical service in the presence of queues and use a standard queuing theory model in the context of several medical procedures, a certain demand for treatment and a maximum average waiting-time guaranteed by the government.…”

Section: Introductionmentioning

confidence: 99%

The interval Shapley value of an M/M/1 service system

Chengguo

2017

International Journal of Applied Mathematics and Computer Science

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Service systems and their cooperation are one of the most important and hot topics in management and information sciences. To design a reasonable allocation mechanism of service systems is the key issue in the cooperation of service systems. In this paper, we systematically introduce the interval Shapley value as cost allocation of cooperative interval games 〈N,V〉 arising from cooperation in a multi-server service system, and provide an explicit expression for the interval Shapley value of cooperative interval games 〈N,V〉. We construct an interval game 〈N,W〉 of a service system which shares the same value for the grand coalition with the original interval game, by using the characteristic function which is dominated by the function of the original interval game. Finally, we prove that the interval game 〈N,W〉 is concave, which means that the interval Shapley value of the interval game 〈N,W〉 is in the interval core of this interval game, and illustrate this conclusion by using numerical examples.

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Section: Introductionmentioning

confidence: 99%

The interval Shapley value of an M/M/1 service system

Chengguo

2017

International Journal of Applied Mathematics and Computer Science

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“…However, optimization algorithms like gradient algorithm and quasi-Newton method can achieve good convergence effect in a short time, fewer researchers have come up with new precise algorithms. Li et al [45] proposed a pricing scheme to realize reasonable resource allocation in a P2P file-sharing network, and verified its stability with Lyapunov theory. Zhan et al [46] applied a policy gradient DRL-based approach to solving resource scheduling problems regarded as the Markov decision process.…”

Section: Related Researchmentioning

confidence: 99%

A Price-based D2D Resource Allocation Scheme in Smart Manufacturing Scenarios

Li,

Zhang,

Liu

et al. 2023

Preprint

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Smart Manufacturing has become a popular topic due to the growth of Internet of Things (IoT) and Cloud Manufacturing, which is driving traditional manufacturing companies to migrate towards digitization and intelligence. Device to Device (D2D) facilitates communication between terminal devices and improves output and operational efficiency. Firstly, by using the utility theory of microeconomics, this paper constructs a novel utility-oriented resource allocation model and addresses the resource allocation problem for D2D communication. This paper also proposes an interesting price-based resource allocation scheme along with an improved gradient descent method and proves the stability of the price-based resource allocation technique based on the Lyapunov stability theory. The simulation results demonstrate that the two proposed algorithms can achieve the equilibrium which is also the optimum of the resource allocation model, and that the gradient descent method is outperformed by the price-based mechanism method in terms of stability and convergence speed.

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“…Recently, Li and Sun (2016) as well as Li et al (2016a) applied the first-order Lagrangian idea and low-pass filtering method, and proposed a novel price-based bandwidth allocation mechanism. Thus a service customer receives its bandwidth allocation according to its offered price, which realizes the goal of fair resource allocation.…”

Section: Introductionmentioning

confidence: 99%

Utility optimization–based bandwidth allocation for elastic and inelastic services in peer–to–peer networks

Zhang

Wang

et al. 2019

International Journal of Applied Mathematics and Computer Science

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This paper considers reasonable bandwidth allocation for multiclass services in peer-to-peer (P2P) networks, measures the satisfaction of each peer as a customer by a utility function when acquiring one service, and develops an optimization model for bandwidth allocation with the objective of utility maximization. Elastic services with concave utilities are first considered and the exact expression of optimal bandwidth allocation for each peer is deduced. In order to obtain an optimum in distributed P2P networks, we develop a gradient-based bandwidth allocation scheme and illustrate the performance with numerical examples. Then we investigate bandwidth allocation for inelastic services with sigmoidal utilities, which is a nonconvex optimization problem. In order to solve it, we analyze provider capacity provisioning for bandwidth allocation of inelastic services and modify the update rule for prices that service customers should pay. Numerical examples are finally given to illustrate that the improved scheme can also efficiently converge to the global optimum.

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A scheme of resource allocation and stability for peer–to–peer file–sharing networks

Cited by 6 publications

References 33 publications

The interval Shapley value of an M/M/1 service system

The interval Shapley value of an M/M/1 service system

A Price-based D2D Resource Allocation Scheme in Smart Manufacturing Scenarios

Utility optimization–based bandwidth allocation for elastic and inelastic services in peer–to–peer networks

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