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OptCaching: A Stackelberg Game and Belief Propagation Based Caching Scheme for Joint Utility Optimization in Fog Computing

机译:OptCaching:基于Stackelberg博弈和信念传播的雾计算联合方案,用于雾计算的联合优化

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Fog Computing which extends the cloud computing paradigm to the edge of the network provides great opportunities for applications with stringent latency requirement. How to allocate the limited caching resources of Fog Nodes (FNs)influences the performance of the fog computing system. In contrast to previous works on caching resource allocation with users' utility as the only consideration, we propose OptCaching which jointly optimize the utility of all network participants including Content Provider (CP), Internet Service Provider (ISP)and users. With caching incentive introduced, utility functions of these three roles are defined. Our joint utility optimization caching scheme is conducted in two stages combining global and local decision making. Firstly, interaction between CP and ISP is modeled as a non-cooperative hierarchy Stackelberg game to make decision on incentive caching prices and global caching amount aiming at optimizing the utility of all network participants. Secondly, for the purpose of further optimizing the utility of users, a belief propagation based cache placement algorithm which utilizes global caching amount constraint and local information is conducted by FNs to reduce users' average download delay. Mathematical analysis and simulation results show that the utility of CP, ISP and users are jointly optimized at Stackelberg equilibrium. The utility of users is further optimized by belief propagation based cache placement algorithm with users' average download delay reduced by 33.7% compared with global popularity based caching strategy.
机译:将云计算范式扩展到网络边缘的雾计算为要求严格的延迟的应用程序提供了巨大的机会。如何分配有限的雾节点(FNs)缓存资源会影响雾计算系统的性能。与以前的以用户的实用程序作为唯一考虑来缓存资源分配的工作相反,我们提出了OptCaching,它可以联合优化所有网络参与者(包括内容提供商(CP),Internet服务提供商(ISP)和用户)的实用程序。通过引入缓存激励,定义了这三个角色的效用函数。我们的联合效用优化缓存方案分两个阶段进行,结合了全局和局部决策。首先,CP和ISP之间的交互被建模为非合作层次的Stackelberg游戏,以决定激励缓存价格和全局缓存量,从而优化所有网络参与者的效用。其次,为了进一步优化用户的效用,FN进行了利用全局缓存量约束和局部信息的基于置信传播的缓存放置算法,以减少用户的平均下载延迟。数学分析和仿真结果表明,CP,ISP和用户的效用是在Stackelberg平衡条件下共同优化的。通过基于置信传播的缓存放置算法进一步优化了用户的效用,与基于全局流行性的缓存策略相比,用户的平均下载延迟减少了33.7%。

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