Optimal resource allocation for multiclass services in peer-to-peer networks via successive approximation (original) (raw)

Abstract

Peer-to-peer (P2P) networks support a wide variety of network services including elastic services such as file-sharing and downloading and inelastic services such as real-time multiparty conferencing. Each peer who acquires a service will receive a certain level of satisfaction if the service is provided with a certain amount of resource. The utility function is used to describe the satisfaction of a peer when acquiring a service. In this paper we consider optimal resource allocation for elastic and inelastic services and formulate a utility maximization model which is an intractable and difficult non-convex optimization problem. In order to resolve it, we apply the successive approximation method and approximate the non-convex problem to a serial of equivalent convex optimization problems. Then we develop a gradient-based resource allocation scheme to achieve the optimal solutions of the approximations. After a serial of approximations, the proposed scheme can finally converge to an optimal solution of the primal utility maximization model for resource allocation which satisfies the Karush–Kuhn–Tucker conditions.

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Acknowledgements

The authors would like to thank the anonymous reviewers and Associate Editor for very detailed and helpful comments and suggestions to improve this work, and the support from the National Natural Science Foundation of China (Nos. 71671159 and 71971188), the Humanity and Social Science Foundation of Ministry of Education of China (No. 16YJC630106), and the Natural Science Foundation of Hebei Province (Nos. G2018203302, G2020203005).

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Authors and Affiliations

  1. School of Economics and Management, Yanshan University, Qinhuangdao, 066004, China
    Shiyong Li, Wei Sun & Huan Liu

Authors

  1. Shiyong Li
  2. Wei Sun
  3. Huan Liu

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Correspondence toWei Sun.

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Li, S., Sun, W. & Liu, H. Optimal resource allocation for multiclass services in peer-to-peer networks via successive approximation.Oper Res Int J 22, 2605–2630 (2022). https://doi.org/10.1007/s12351-021-00622-9

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