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Distributed strategic learning and game theoretic formulation of network embedded coding

机译:网络嵌入式编码的分布式策略学习和博弈论表述

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摘要

Nowadays, there is a growing demand of middleware solutions for reliable event notification over the Internet due to several key industrial projects aiming at integrating existing legacy systems and enhancing their functionalities. Reliable event notification is mainly realized by means of retransmissions, with the consequent worsening of the performance and traffic load. Traditionally, the opposite approach of spatial redundancy is not used due to the tunability, scalability and flexibility issues of its implementation schemes. In this paper, we propose a game theoretic formulation of Forward Error Correction (one of the mean schemes to introduce spatial redundancy), in order to resolve these mentioned issues. Moreover, we introduce distributed strategic learning for the optimal formulation of the payoff functions in the game, and for the effective adaptivity in response to the possible variations in the experienced loss patterns. We prove the quality of this solution by using a series of simulations run on OMNET++. (C) 2017 Elsevier B.V. All rights reserved.
机译:如今,由于一些旨在集成现有遗留系统并增强其功能的关键工业项目,对通过Internet进行可靠事件通知的中间件解决方案的需求不断增长。可靠的事件通知主要是通过重传来实现的,从而导致性能和流量负载的恶化。传统上,由于其实现方案的可调性,可伸缩性和灵活性问题,因此不使用相反的空间冗余方法。在本文中,我们提出了前向纠错的博弈论表述(一种引入空间冗余的均值方案),以解决上述问题。此外,我们引入了分布式战略学习,以优化游戏中的收益函数,并有效地适应了经验丰富的损失模式中的变化。通过使用在OMNET ++上运行的一系列模拟,我们证明了该解决方案的质量。 (C)2017 Elsevier B.V.保留所有权利。

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